Patent Publication Number: US-2021182352-A1

Title: Systems and methods for semantic keyword analysis

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 16/523,267 titled “SYSTEMS AND METHODS FOR SEMANTIC KEYWORD ANALYSIS” and filed on Jul. 26, 2019 which is a continuation of U.S. application Ser. No. 14/928,210 titled “SYSTEMS AND METHODS FOR SEMANTIC KEYWORD ANALYSIS” and filed on Oct. 30, 2015, (now U.S. Pat. No. 10,409,875) which claims priority to and the benefit of U.S. Provisional Application No. 62/073560, titled “SYSTEMS AND METHODS FOR SEMANTIC KEYWORD ANALYSIS FOR ORGANIC SEARCH” and filed on Oct. 31, 2014, which is incorporated by reference herein in its entirety for all purposes. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure generally relates to systems and methods for keyword research and analysis, and in particular, to keyword research and analysis with respect to organic search engine optimization. 
     BACKGROUND OF THE DISCLOSURE 
     In efforts to increase organic visibility and traffic of web pages (e.g., blogs, news sites, etc.), owners of web pages may engage in search engine optimization (SEO). Search engine optimization entails considerations of how search engines work, what people search for, how people search (e.g., what terms people use to search for various topics), and the like. As an example of one method of SEO, owners of websites may attempt to manually research search terms typically related to the topics of their websites, and attempt to incorporate those terms into their content. However, such a method may be cumbersome, time-consuming, and provide minimal beneficial effect on SEO. 
     BRIEF SUMMARY OF THE DISCLOSURE 
     The present solution provides a new tool for keyword research and analysis for search engine optimization. Various embodiments of the tool provide an efficient and user-friendly mechanism for identifying related topics that may be incorporated into a user&#39;s website in an effort to increase organic traffic. 
     In various embodiments, a method for generating from one or more keywords a list of related topics for organic search includes receiving, by a topic tool, an input of one or more keywords for which to generate a list of related topics. The method may further include acquiring, by a crawler, content from a plurality of different web content sources via one or more networks. The method may also include applying, by the topic tool, to the acquired content an ensemble of one or more key phrase extraction algorithms, one or more graph analyses algorithms and one or more natural language processing algorithms to identify a set of semantically relevant topics scored by relevance. The method may also include generating, by the topic tool, from the set of semantically relevant topics, a knowledge graph of related topics for the input of the one or more keywords. The method may further include outputting, by the topic tool based at least partially on the knowledge graph, an enumerated list of topics ranked by at least a relevance score. 
     In some embodiments, the method further includes receiving, by the topic tool, the input of one or more keywords from a topic inventory tool, the topic inventory tool generating the input keyword from analyses of content from an identified web site. 
     In some embodiments, the method further includes acquiring content, by a crawler, from the plurality of different web content sources including web sites, news articles, blog posts and keyword data. 
     According to some embodiments, the method further includes cleansing and normalizing the acquired content. 
     In some embodiments, the one or more key phrase extraction algorithms include a Bayesian statistical ensemble. 
     In some embodiments, the method further includes performing a plurality of term ranking functions including one or more of a core phrase term ranking function, a tail phrase term ranking function, a hyperdictionary graph traversal algorithm and/or a semantic knowledgebase path traversal score. 
     In some embodiments, the method further includes applying a weight to each of the more algorithms of the ensemble to generate the relevance score for the set of semantic relevance scored phrases. 
     In some embodiments, the method further includes outputting the enumerated list of topics ranked by a measure of frequency including one of more of frequency in page body, frequency in title and/or number of pages where the topics occur. 
     In some embodiments, the method further includes outputting the enumerated list of topics ranked by at least one of an attractiveness score, a volume score and a competition score. 
     In some embodiments, the method further includes outputting the enumerated list of topics ranked by an estimated equivalent value associated with paid advertising. 
     According to various embodiments, a system for generating from one or more keywords a list of related topics for organic search includes a crawler configured to acquire content from a plurality of different web content sources via one or more networks. The system further includes a topic tool configured to receive an input of one or more keywords for which to generate a list of related topics. The topic tool may be further configured to receive an input of one or more keywords for which to generate a list of related topics and to apply to the acquired content an ensemble of one or more key phrase extraction algorithms, one or more graph analyses algorithms and one or more natural language processing algorithms to identify a set of semantically relevant topics scored by relevance. The topic tool may be configured to generate from the set of semantically relevant topics, a knowledge graph of related topics for the input of the one or more keywords. The topic tool may be configured to output based at least partially on the knowledge graph and an enumerated list of topics ranked by at least a relevance score. 
     In some embodiments, the system further includes a topic inventory tool configured to generate the input of one or more keywords from analyses of content from an identified web site. 
     In some embodiments, the key phrase extraction algorithms include a Bayesian statistical ensemble. 
     In some embodiments, the ensemble is further configured to perform a plurality of term ranking functions including one or more of a core phrase term ranking function, a tail phrase term ranking function, a hyperdictionary graph traversal algorithm and/or a semantic knowledgebase path traversal score. 
     In some embodiments, the topic tool is configured to output the enumerated list of topics ranked by one or more of measuring frequency, an attractiveness score, volume score and/or a competition score. 
     According to various embodiments, a system including a content audit tool configured to execute on a processer to receive a focus one or more keywords for a website, the website crawled by a crawler for content. The content audit tool may be configured to apply to the content an ensemble of one or more key phrase extraction algorithms, one or more graph analyses algorithms and one or more natural language processing algorithms to identify a set of semantic relevant topics scored by relevance. The content audit tool may be configured to identify a plurality of pages of the website with one or more related topics from the set of semantically relevant topics and generating a content performance metric for each page of the plurality of pages. The content audit tool may be configured to output a topical content score for the content, the topical content score identifying a level of coverage of the topic by the content of the website. 
     In some embodiments, the content audit tool is further configured to filter content by at least one of company name, product name or people&#39;s names. 
     In some embodiments, the content audit tool is further configured to output a relevance score for each related topic of the set of one or more related topics. 
     In some embodiments, the content audit tool is further configured to output a count of a number of instances of each related topic. 
     In some embodiments, the content audit tool is further configured to output a total number of mentions of related topics in the content. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the following description taken in conjunction with the accompanying drawings, in which: 
         FIG. 1A  is a block diagram depicting an embodiment of a network environment comprising client devices in communication with server devices; 
         FIG. 1B  is a block diagram depicting a cloud computing environment comprising client devices in communication with cloud service providers; 
         FIGS. 1C and 1D  are block diagrams depicting embodiments of computing devices useful in connection with the methods and systems described herein; 
         FIG. 2A  is an embodiment of a system comprising a keyword research and analysis tool; 
         FIG. 2B  is an embodiment of a screen shot of a topic tool page; 
         FIG. 2C  depicts a block diagram of a method for generating related keywords. 
         FIG. 2D  is an embodiment of a screen shot of a topic tool page; 
         FIG. 2E  is an embodiment of a screen shot of a relevant topics table page; 
         FIG. 2F  is an embodiment of a screen shot of content audit page; 
         FIG. 2G  is another embodiment of a screen shot of a content audit page; 
         FIG. 2H  is an embodiment of a screen shot of a topic inventory tool page; 
         FIG. 2I  is another embodiment of a screen shot of a topic inventory tool page; 
         FIG. 3A  is a flow diagram depicting an embodiment of a method of using a topic tool; and 
         FIG. 3B  is a flow diagram depicting an embodiment of a method of using a content audit tool. 
     
    
    
     DETAILED DESCRIPTION 
     For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specification and their respective contents may be helpful: 
     Section A describes a network environment and computing environment that may be useful for practicing embodiments described herein. 
     Section B describes embodiments of systems and methods for a keyword research and analysis tool. 
     Section C describes embodiments of systems and methods for a crawler. 
     Section D describes embodiments of a storage medium including an ensemble of algorithms. 
     Section E describes embodiments of systems and methods for a topic tool. 
     Section F describes embodiments of systems and methods for a content audit tool. 
     Section G describes embodiments of systems and methods for a topic inventory tool. 
     A. Computing and Network Environment 
     Prior to discussing specific embodiments of the present solution, it may be helpful to describe aspects of the operating environment as well as associated system components (e.g., hardware elements) in connection with the methods and systems described herein. Referring to  FIG. 1A , an embodiment of a network environment is depicted. In brief overview, the network environment includes one or more clients  102   a - 102   n  (also generally referred to as local machine(s)  102 , client(s)  102 , client node(s)  102 , client machine(s)  102 , client computer(s)  102 , client device(s)  102 , endpoint(s)  102 , or endpoint node(s)  102 ) in communication with one or more servers  106   a - 106   n  (also generally referred to as server(s)  106 , node  106 , or remote machine(s)  106 ) via one or more networks  104 . In some embodiments, a client  102  has the capacity to function as both a client node seeking access to resources provided by a server and as a server providing access to hosted resources for other clients  102   a - 102   n.    
     Although  FIG. 1A  shows a network  104  between the clients  102  and the servers  106 , the clients  102  and the servers  106  may be on the same network  104 . In some embodiments, there are multiple networks  104  between the clients  102  and the servers  106 . In one of these embodiments, a network  104 ′ (not shown) may be a private network and a network  104  may be a public network. In another of these embodiments, a network  104  may be a private network and a network  104 ′ a public network. In still another of these embodiments, networks  104  and  104 ′ may both be private networks. 
     The network  104  may be connected via wired or wireless links. Wired links may include Digital Subscriber Line (DSL), coaxial cable lines, or optical fiber lines. The wireless links may include BLUETOOTH, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), an infrared channel or satellite band. The wireless links may also include any cellular network standards used to communicate among mobile devices, including standards that qualify as 1G, 2G, 3G, or 4G. The network standards may qualify as one or more generation of mobile telecommunication standards by fulfilling a specification or standards such as the specifications maintained by International Telecommunication Union. The 3G standards, for example, may correspond to the International Mobile Telecommunications-2000 (IMT-2000) specification, and the 4G standards may correspond to the International Mobile Telecommunications Advanced (IMT-Advanced) specification. Examples of cellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTE Advanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standards may use various channel access methods e.g. FDMA, TDMA, CDMA, or SDMA. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same types of data may be transmitted via different links and standards. 
     The network  104  may be any type and/or form of network. The geographical scope of the network  104  may vary widely and the network  104  can be a body area network (BAN), a personal area network (PAN), a local-area network (LAN), e.g. Intranet, a metropolitan area network (MAN), a wide area network (WAN), or the Internet. The topology of the network  104  may be of any form and may include, e.g., any of the following: point-to-point, bus, star, ring, mesh, or tree. The network  104  may be an overlay network which is virtual and sits on top of one or more layers of other networks  104 ′. The network  104  may be of any such network topology as known to those ordinarily skilled in the art capable of supporting the operations described herein. The network  104  may utilize different techniques and layers or stacks of protocols, including, e.g., the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET (Synchronous Optical Networking) protocol, or the SDH (Synchronous Digital Hierarchy) protocol. The TCP/IP internet protocol suite may include application layer, transport layer, internet layer (including, e.g., IPv6), or the link layer. The network  104  may be a type of a broadcast network, a telecommunications network, a data communication network, or a computer network. 
     In some embodiments, the system may include multiple, logically-grouped servers  106 . In one of these embodiments, the logical group of servers may be referred to as a server farm  38  or a machine farm  38 . In another of these embodiments, the servers  106  may be geographically dispersed. In other embodiments, a machine farm  38  may be administered as a single entity. In still other embodiments, the machine farm  38  includes a plurality of machine farms  38 . The servers  106  within each machine farm  38  can be heterogeneous—one or more of the servers  106  or machines  106  can operate according to one type of operating system platform (e.g., WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), while one or more of the other servers  106  can operate on according to another type of operating system platform (e.g., Unix, Linux, or Mac OS X). 
     In one embodiment, servers  106  in the machine farm  38  may be stored in high-density rack systems, along with associated storage systems, and located in an enterprise data center. In this embodiment, consolidating the servers  106  in this way may improve system manageability, data security, the physical security of the system, and system performance by locating servers  106  and high performance storage systems on localized high performance networks. Centralizing the servers  106  and storage systems and coupling them with advanced system management tools allows more efficient use of server resources. 
     The servers  106  of each machine farm  38  do not need to be physically proximate to another server  106  in the same machine farm  38 . Thus, the group of servers  106  logically grouped as a machine farm  38  may be interconnected using a wide-area network (WAN) connection or a metropolitan-area network (MAN) connection. For example, a machine farm  38  may include servers  106  physically located in different continents or different regions of a continent, country, state, city, campus, or room. Data transmission speeds between servers  106  in the machine farm  38  can be increased if the servers  106  are connected using a local-area network (LAN) connection or some form of direct connection. Additionally, a heterogeneous machine farm  38  may include one or more servers  106  operating according to a type of operating system, while one or more other servers  106  execute one or more types of hypervisors rather than operating systems. In these embodiments, hypervisors may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments, allowing multiple operating systems to run concurrently on a host computer. Native hypervisors may run directly on the host computer. Hypervisors may include VMware ESX/ESXi, manufactured by VMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an open source product whose development is overseen by Citrix Systems, Inc.; the HYPER-V hypervisors provided by Microsoft or others. Hosted hypervisors may run within an operating system on a second software level. Examples of hosted hypervisors may include VMware Workstation and VIRTUALBOX. 
     Management of the machine farm  38  may be de-centralized. For example, one or more servers  106  may comprise components, subsystems and modules to support one or more management services for the machine farm  38 . In one of these embodiments, one or more servers  106  provide functionality for management of dynamic data, including techniques for handling failover, data replication, and increasing the robustness of the machine farm  38 . Each server  106  may communicate with a persistent store and, in some embodiments, with a dynamic store. 
     Server  106  may be a file server, application server, web server, proxy server, appliance, network appliance, gateway, gateway server, virtualization server, deployment server, SSL VPN server, or firewall. In one embodiment, the server  106  may be referred to as a remote machine or a node. In another embodiment, a plurality of nodes  290  may be in the path between any two communicating servers. 
     Referring to  FIG. 1B , a cloud computing environment is depicted. A cloud computing environment may provide client  102  with one or more resources provided by a network environment. The cloud computing environment may include one or more clients  102   a - 102   n , in communication with the cloud  108  over one or more networks  104 . Clients  102  may include, e.g., thick clients, thin clients, and zero clients. A thick client may provide at least some functionality even when disconnected from the cloud  108  or servers  106 . A thin client or a zero client may depend on the connection to the cloud  108  or server  106  to provide functionality. A zero client may depend on the cloud  108  or other networks  104  or servers  106  to retrieve operating system data for the client device. The cloud  108  may include back end platforms, e.g., servers  106 , storage, server farms or data centers. 
     The cloud  108  may be public, private, or hybrid. Public clouds may include public servers  106  that are maintained by third parties to the clients  102  or the owners of the clients. The servers  106  may be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds may be connected to the servers  106  over a public network. Private clouds may include private servers  106  that are physically maintained by clients  102  or owners of clients. Private clouds may be connected to the servers  106  over a private network  104 . Hybrid clouds  108  may include both the private and public networks  104  and servers  106 . 
     The cloud  108  may also include a cloud based delivery, e.g. Software as a Service (SaaS)  110 , Platform as a Service (PaaS)  112 , and Infrastructure as a Service (IaaS)  114 . IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS include AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif. PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Wash., Google App Engine provided by Google Inc., and HEROKU provided by Heroku, Inc. of San Francisco, Calif. SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc., SALESFORCE provided by Salesforce.com Inc. of San Francisco, Calif., or OFFICE  365  provided by Microsoft Corporation. Examples of SaaS may also include data storage providers, e.g. DROPBOX provided by Dropbox, Inc. of San Francisco, Calif., Microsoft SKYDRIVE provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif. 
     Clients  102  may access IaaS resources with one or more IaaS standards, including, e.g., Amazon Elastic Compute Cloud (EC2), Open Cloud Computing Interface (OCCI), Cloud Infrastructure Management Interface (CIMI), or OpenStack standards. Some IaaS standards may allow clients access to resources over HTTP, and may use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients  102  may access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that may be built on REST, HTTP, XML, or other protocols. Clients  102  may access SaaS resources through the use of web-based user interfaces, provided by a web browser (e.g. GOOGLE CHROME, Microsoft INTERNET EXPLORER, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, Calif.). Clients  102  may also access SaaS resources through smartphone or tablet applications, including ,e.g., Salesforce Sales Cloud, or Google Drive app. Clients  102  may also access SaaS resources through the client operating system, including, e.g., Windows file system for DROPBOX. 
     In some embodiments, access to IaaS, PaaS, or SaaS resources may be authenticated. For example, a server or authentication server may authenticate a user via security certificates, HTTPS, or API keys. API keys may include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources may be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL). 
     The client  102  and server  106  may be deployed as and/or executed on any type and form of computing device, e.g. a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein.  FIGS. 1C and 1D  depict block diagrams of a computing device  100  useful for practicing an embodiment of the client  102  or a server  106 . As shown in  FIGS. 1C and 1D , each computing device  100  includes a central processing unit  121 , and a main memory unit  122 . As shown in  FIG. 1C , a computing device  100  may include a storage device  128 , an installation device  116 , a network interface  118 , an I/O controller  123 , display devices  124   a - 124   n , a keyboard  126  and a pointing device  127 , e.g. a mouse. The storage device  128  may include, without limitation, an operating system, software, and a software of a tool for keyword research and analysis  120 . As shown in  FIG. 1D , each computing device  100  may also include additional optional elements, e.g. a memory port  103 , a bridge  170 , one or more input/output devices  130   a - 130   n  (generally referred to using reference numeral  130 ), and a cache memory  140  in communication with the central processing unit  121 . 
     The central processing unit  121  is any logic circuitry that responds to and processes instructions fetched from the main memory unit  122 . In many embodiments, the central processing unit  121  is provided by a microprocessor unit, e.g.: those manufactured by Intel Corporation of Mountain View, Calif.; those manufactured by Motorola Corporation of Schaumburg, Ill.; the ARM processor and TEGRA system on a chip (SoC) manufactured by Nvidia of Santa Clara, Calif.; the POWER7 processor, those manufactured by International Business Machines of White Plains, N.Y.; or those manufactured by Advanced Micro Devices of Sunnyvale, Calif. The computing device  100  may be based on any of these processors, or any other processor capable of operating as described herein. The central processing unit  121  may utilize instruction level parallelism, thread level parallelism, different levels of cache, and multi-core processors. A multi-core processor may include two or more processing units on a single computing component. Examples of a multi-core processors include the AMD PHENOM IIX2, INTEL CORE i5 and INTEL CORE i7. 
     Main memory unit  122  may include one or more memory chips capable of storing data and allowing any storage location to be directly accessed by the microprocessor  121 . Main memory unit  122  may be volatile and faster than storage  128  memory. Main memory units  122  may be Dynamic random access memory (DRAM) or any variants, including static random access memory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM), Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), or Extreme Data Rate DRAM (XDR DRAM). In some embodiments, the main memory  122  or the storage  128  may be non-volatile; e.g., non-volatile read access memory (NVRAM), flash memory non-volatile static RAM (nvSRAM), Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-change memory (PRAM), conductive-bridging RAM (CBRAM), Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM), Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory  122  may be based on any of the above described memory chips, or any other available memory chips capable of operating as described herein. In the embodiment shown in  FIG. 1C , the processor  121  communicates with main memory  122  via a system bus  150  (described in more detail below).  FIG. 1D  depicts an embodiment of a computing device  100  in which the processor communicates directly with main memory  122  via a memory port  103 . For example, in  FIG. 1D  the main memory  122  may be DRDRAM. 
       FIG. 1D  depicts an embodiment in which the main processor  121  communicates directly with cache memory  140  via a secondary bus, sometimes referred to as a backside bus. In other embodiments, the main processor  121  communicates with cache memory  140  using the system bus  150 . Cache memory  140  typically has a faster response time than main memory  122  and is typically provided by SRAM, BSRAM, or EDRAM. In the embodiment shown in  FIG. 1D , the processor  121  communicates with various I/O devices  130  via a local system bus  150 . Various buses may be used to connect the central processing unit  121  to any of the I/O devices  130 , including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. For embodiments in which the I/O device is a video display  124 , the processor  121  may use an Advanced Graphics Port (AGP) to communicate with the display  124  or the I/O controller  123  for the display  124 .  FIG. 1D  depicts an embodiment of a computer  100  in which the main processor  121  communicates directly with I/O device  130   b  or other processors  121 ′ via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.  FIG. 1D  also depicts an embodiment in which local busses and direct communication are mixed: the processor  121  communicates with I/O device  130   a  using a local interconnect bus while communicating with I/O device  130   b  directly. 
     A wide variety of I/O devices  130   a - 130   n  may be present in the computing device  100 . Input devices may include keyboards, mice, trackpads, trackballs, touchpads, touch mice, multi-touch touchpads and touch mice, microphones, multi-array microphones, drawing tablets, cameras, single-lens reflex camera (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices may include video displays, graphical displays, speakers, headphones, inkjet printers, laser printers, and 3D printers. 
     Devices  130   a - 130   n  may include a combination of multiple input or output devices, including, e.g., Microsoft KINECT, Nintendo Wiimote for the WII, Nintendo WII U GAMEPAD, or Apple IPHONE. Some devices  130   a - 130   n  allow gesture recognition inputs through combining some of the inputs and outputs. Some devices  130   a - 130   n  provides for facial recognition which may be utilized as an input for different purposes including authentication and other commands. Some devices  130   a - 130   n  provides for voice recognition and inputs, including, e.g., Microsoft KINECT, SIRI for IPHONE by Apple, Google Now or Google Voice Search. 
     Additional devices  130   a - 130   n  have both input and output capabilities, including, e.g., haptic feedback devices, touchscreen displays, or multi-touch displays. Touchscreen, multi-touch displays, touchpads, touch mice, or other touch sensing devices may use different technologies to sense touch, including, e.g., capacitive, surface capacitive, projected capacitive touch (PCT), in-cell capacitive, resistive, infrared, waveguide, dispersive signal touch (DST), in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT), or force-based sensing technologies. Some multi-touch devices may allow two or more contact points with the surface, allowing advanced functionality including, e.g., pinch, spread, rotate, scroll, or other gestures. Some touchscreen devices, including, e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, may have larger surfaces, such as on a table-top or on a wall, and may also interact with other electronic devices. Some I/O devices  130   a - 130   n , display devices  124   a - 124   n  or group of devices may be augment reality devices. The I/O devices may be controlled by an I/O controller  123  as shown in  FIG. 1C . The I/O controller may control one or more I/O devices, such as, e.g., a keyboard  126  and a pointing device  127 , e.g., a mouse or optical pen. Furthermore, an I/O device may also provide storage and/or an installation medium  116  for the computing device  100 . In still other embodiments, the computing device  100  may provide USB connections (not shown) to receive handheld USB storage devices. In further embodiments, an I/O device  130  may be a bridge between the system bus  150  and an external communication bus, e.g. a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or a Thunderbolt bus. 
     In some embodiments, display devices  124   a - 124   n  may be connected to I/O controller  123 . Display devices may include, e.g., liquid crystal displays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD, electronic papers (e-ink) displays, flexile displays, light emitting diode displays (LED), digital light processing (DLP) displays, liquid crystal on silicon (LCOS) displays, organic light-emitting diode (OLED) displays, active-matrix organic light-emitting diode (AMOLED) displays, liquid crystal laser displays, time-multiplexed optical shutter (TMOS) displays, or 3D displays. Examples of 3D displays may use, e.g. stereoscopy, polarization filters, active shutters, or autostereoscopy. Display devices  124   a - 124   n  may also be a head-mounted display (HMD). In some embodiments, display devices  124   a - 124   n  or the corresponding I/O controllers  123  may be controlled through or have hardware support for OPENGL or DIRECTX API or other graphics libraries. 
     In some embodiments, the computing device  100  may include or connect to multiple display devices  124   a - 124   n , which each may be of the same or different type and/or form. As such, any of the I/O devices  130   a - 130   n  and/or the I/O controller  123  may include any type and/or form of suitable hardware, software, or combination of hardware and software to support, enable or provide for the connection and use of multiple display devices  124   a - 124   n  by the computing device  100 . For example, the computing device  100  may include any type and/or form of video adapter, video card, driver, and/or library to interface, communicate, connect or otherwise use the display devices  124   a - 124   n . In one embodiment, a video adapter may include multiple connectors to interface to multiple display devices  124   a - 124   n . In other embodiments, the computing device  100  may include multiple video adapters, with each video adapter connected to one or more of the display devices  124   a - 124   n . In some embodiments, any portion of the operating system of the computing device  100  may be configured for using multiple displays  124   a - 124   n . In other embodiments, one or more of the display devices  124   a - 124   n  may be provided by one or more other computing devices  100   a  or  100   b  connected to the computing device  100 , via the network  104 . In some embodiments software may be designed and constructed to use another computer&#39;s display device as a second display device  124   a  for the computing device  100 . For example, in one embodiment, an Apple iPad may connect to a computing device  100  and use the display of the device  100  as an additional display screen that may be used as an extended desktop. One ordinarily skilled in the art will recognize and appreciate the various ways and embodiments that a computing device  100  may be configured to have multiple display devices  124   a - 124   n.    
     Referring again to  FIG. 1C , the computing device  100  may comprise a storage device  128  (e.g. one or more hard disk drives or redundant arrays of independent disks) for storing an operating system or other related software, and for storing application software programs such as any program related to the software  120  for the experiment tracker system. Examples of storage device  128  include, e.g., hard disk drive (HDD); optical drive including CD drive, DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive; or any other device suitable for storing data. Some storage devices may include multiple volatile and non-volatile memories, including, e.g., solid state hybrid drives that combine hard disks with solid state cache. Some storage device  128  may be non-volatile, mutable, or read-only. Some storage device  128  may be internal and connect to the computing device  100  via a bus  150 . Some storage device  128  may be external and connect to the computing device  100  via a I/O device  130  that provides an external bus. Some storage device  128  may connect to the computing device  100  via the network interface  118  over a network  104 , including, e.g., the Remote Disk for MACBOOK AIR by Apple. Some client devices  100  may not require a non-volatile storage device  128  and may be thin clients or zero clients  102 . Some storage device  128  may also be used as a installation device  116 , and may be suitable for installing software and programs. Additionally, the operating system and the software can be run from a bootable medium, for example, a bootable CD, e.g. KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux distribution from knoppix.net. 
     Client device  100  may also install software or application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., the Mac App Store provided by Apple, Inc., GOOGLE PLAY for Android OS provided by Google Inc., Chrome Webstore for CHROME OS provided by Google Inc., and Amazon Appstore for Android OS and KINDLE FIRE provided by Amazon.com, Inc. An application distribution platform may facilitate installation of software on a client device  102 . An application distribution platform may include a repository of applications on a server  106  or a cloud  108 , which the clients  102   a - 102   n  may access over a network  104 . An application distribution platform may include application developed and provided by various developers. A user of a client device  102  may select, purchase and/or download an application via the application distribution platform. 
     Furthermore, the computing device  100  may include a network interface  118  to interface to the network  104  through a variety of connections including, but not limited to, standard telephone lines LAN or WAN links (e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet, Ethernet-over-SONET, ADSL, VDSL, BPON, GPON, fiber optical including FiOS), wireless connections, or some combination of any or all of the above. Connections can be established using a variety of communication protocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE 802.11a/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections). In one embodiment, the computing device  100  communicates with other computing devices  100 ′ via any type and/or form of gateway or tunneling protocol e.g. Secure Socket Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems, Inc. of Ft. Lauderdale, Fla. The network interface  118  may comprise a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device  100  to any type of network capable of communication and performing the operations described herein. 
     A computing device  100  of the sort depicted in  FIGS. 1B and 1C  may operate under the control of an operating system, which controls scheduling of tasks and access to system resources. The computing device  100  can be running any operating system such as any of the versions of the MICROSOFT WINDOWS operating systems, the different releases of the Unix and Linux operating systems, any version of the MAC OS for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. Typical operating systems include, but are not limited to: WINDOWS 2000, WINDOWS Server 2012, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, WINDOWS RT, and WINDOWS 8 all of which are manufactured by Microsoft Corporation of Redmond, Wash.; MAC OS and iOS, manufactured by Apple, Inc. of Cupertino, Calif.; and Linux, a freely-available operating system, e.g. Linux Mint distribution (“distro”) or Ubuntu, distributed by Canonical Ltd. of London, United Kingom; or Unix or other Unix-like derivative operating systems; and Android, designed by Google, of Mountain View, Calif., among others. Some operating systems, including, e.g., the CHROME OS by Google, may be used on zero clients or thin clients, including, e.g., CHROMEBOOKS. 
     The computer system  100  can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computer system  100  has sufficient processor power and memory capacity to perform the operations described herein. In some embodiments, the computing device  100  may have different processors, operating systems, and input devices consistent with the device. The Samsung GALAXY smartphones, e.g., operate under the control of Android operating system developed by Google, Inc. GALAXY smartphones receive input via a touch interface. 
     In some embodiments, the computing device  100  is a gaming system. For example, the computer system  100  may comprise a PLAYSTATION 3, or PERSONAL PLAYSTATION PORTABLE (PSP), or a PLAYSTATION VITA device manufactured by the Sony Corporation of Tokyo, Japan, a NINTENDO DS, NINTENDO 3DS, NINTENDO WII, or a NINTENDO WII U device manufactured by Nintendo Co., Ltd., of Kyoto, Japan, an XBOX 360 device manufactured by the Microsoft Corporation of Redmond, Wash. 
     In some embodiments, the computing device  100  is a digital audio player such as the Apple IPOD, IPOD Touch, and IPOD NANO lines of devices, manufactured by Apple Computer of Cupertino, Calif. Some digital audio players may have other functionality, including, e.g., a gaming system or any functionality made available by an application from a digital application distribution platform. For example, the IPOD Touch may access the Apple App Store. In some embodiments, the computing device  100  is a portable media player or digital audio player supporting file formats including, but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audible audiobook, Apple Lossless audio file formats and .mov, .m4v, and .mp4 MPEG-4 (H.264/MPEG-4 AVC) video file formats. 
     In some embodiments, the computing device  100  is a tablet e.g. the IPAD line of devices by Apple; GALAXY TAB family of devices by Samsung; or KINDLE FIRE, by Amazon.com, Inc. of Seattle, Wash. In other embodiments, the computing device  100  is a eBook reader, e.g. the KINDLE family of devices by Amazon.com, or NOOK family of devices by Barnes &amp; Noble, Inc. of New York City, N.Y. 
     In some embodiments, the communications device  102  includes a combination of devices, e.g. a smartphone combined with a digital audio player or portable media player. For example, one of these embodiments is a smartphone, e.g. the IPHONE family of smartphones manufactured by Apple, Inc.; a Samsung GALAXY family of smartphones manufactured by Samsung, Inc; or a Motorola DROID family of smartphones. In yet another embodiment, the communications device  102  is a laptop or desktop computer equipped with a web browser and a microphone and speaker system, e.g. a telephony headset. In these embodiments, the communications devices  102  are web-enabled and can receive and initiate phone calls. In some embodiments, a laptop or desktop computer is also equipped with a webcam or other video capture device that enables video chat and video call. 
     In some embodiments, the status of one or more machines  102 ,  106  in the network  104  is monitored, generally as part of network management. In one of these embodiments, the status of a machine may include an identification of load information (e.g., the number of processes on the machine, CPU and memory utilization), of port information (e.g., the number of available communication ports and the port addresses), or of session status (e.g., the duration and type of processes, and whether a process is active or idle). In another of these embodiments, this information may be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards decisions in load distribution, network traffic management, and network failure recovery as well as any aspects of operations of the present solution described herein. Aspects of the operating environments and components described above will become apparent in the context of the systems and methods disclosed herein. 
     B. Keyword Research and Analysis Tool 
     Systems and methods of the present solution are directed to performing keyword research and analysis to generate a list of topically-related and conceptually related keywords to a specific topic, for example, for purposes of search engine optimization (SEO). 
     One aspect of the present disclosure is directed to systems and methods for performing a keyword search to generate a list of topically-related and conceptually related keywords to a specific topic to be marketed. In some embodiments, a keyword search tool can perform semantic keyword research and semantic content analysis. In one aspect of the present disclosure, there is provided a system and method for performing semantic keyword research (e.g., semantic SEO keyword research), which may quickly and efficiently generate a list of topically-related keywords. Unlike the traditional keyword planner and research tools, a tool may generate keywords that are conceptually-related to the topic that a user is marketing. For example, for the term “dog food,” the tool can generate topics such as “pet food” and “doggy treats.” The tool suggestions may then be ranked by relevance score, a measure of topical relevance, saving a user from hours of manual keyword research effort. In some embodiments, traditional applications include keyword research for prospecting and new clients, identifying new keywords for existing clients, and identifying relevant head terms for semantic SEO applications. In some embodiments, the systems and methods described herein can be used alongside a long-tail keyword tool (e.g., Google Keyword Planner) to identify new long-tail opportunities. Search engine marketing campaigns can be optimized with semantic machine learning. 
     In another aspect, the present disclosure is directed to system and methods for performing semantic content analysis. In some embodiments, the keyword search tool can be used with a content analyzer to help improve blog posts, landing pages, whitepapers and other content by identifying topical gaps. The content analyzer may use the keyword search tool data to notify a user of related keywords and topics that are missing from the user&#39;s current content. For example, a blog post on “dog food” may be silent in regards to “pet food,” meaning “pet food” is not found in the body of the post. The content analyzer can highlight the gap. In some embodiments, the content analyzer highlights absolute gaps (e.g., whether a related keyword is totally missing from the website). In some embodiments, the content analyzer may highlight relative gaps (e.g., a related keyword that is mentioned on the website, but is not mentioned very frequently compared with the strength of relevance of the related keyword and the size of the website). In additional embodiments, the content analyzer may highlight topics that are mentioned overly often at a user&#39;s website. Adding relevant keywords to content improves the quality of content and, due to greater topical authority, increases organic traffic. 
     Accordingly, aspects of the present disclosure are directed to a keyword search tool that can generate a list of topically-related and conceptually related keywords to a specific topic to be marketed. In some embodiments, the keyword search tool may be used to build keyword strategy. For example, the keyword search tool can generate a target keyword list for organic searches or identify keyword candidates. In some embodiments, the keyword search tool can be used to identify content needs. For example, the keyword search tool may crawl a domain site and identify gaps in content on the domain site and suggest high-value topics that are not being sufficiently covered. In some embodiments, the keyword search tool can be used to create content. For example, primary keywords and topic suggestions can be used to create content. For long-form content, the keyword search tool can suggest additional topics to cover. 
     Referring to  FIG. 2A , an embodiment of a computer environment similar to that illustrated in  FIG. 1A  is depicted. In addition to the elements previously described in connection with  FIG. 1A , a server  106  including a keyword research and analysis tool (“keyword tool”)  202  may also be connected to the network  104 . Accordingly, the keyword tool  202  may be connected to the client(s)  102   a - 102   n  and the server(s)  106   a - 106   n  via the network  104 . In addition, the server(s)  106   a - 106   n  may each be a content source  206 A for providing content for research and analysis by the keyword tool  202 . In various embodiments, the content may include data corresponding to websites, news articles, blog posts, keyword data, and/or any other suitable information for use with the keyword tool  202 . 
     In the embodiment shown in  FIG. 2A , the keyword tool  202  may include one or more components or modules for performing various functions corresponding to keyword research and analysis. In the embodiment of  FIG. 2A , the keyword tool  202  includes a topic tool  202   a , a content audit tool  202   b , a topic inventory tool  202   c , an ensemble algorithm tool  202   d , a crawler  202   e , and a database  202   f . Each of these components are described in further detail below. In other embodiments, the keyword tool  202  may include less or more components, depending on the desired functions to be implemented by the keyword tool  202 . For example, one embodiment of the keyword tool  202  may include the topic tool  202   a  and the content audit tool  202   b  only. 
     C. Crawler 
     According to various embodiments, the crawler  202   e  may be a tool for parsing and collecting relevant content over the web. In some embodiments, each of the other tools at the keyword tool  202 , such as, but not limited to, the topic tool  202   a , the content audit tool  202   b , and the topic inventory tool  202   c , may be connected to the crawler  202   e  and may operate in conjunction with the crawler  202   e . In other words, the other tools may perform various operations on the content acquired by the crawler  202   e.    
     In some embodiments, the crawler  202   e  at the keyword tool  202  may use one or more keywords to search the web for a plurality of different relevant web content sources. For example, the crawler  202   e  may acquire content by crawling and searching the web for the one or more keywords and any content related to the keywords. In various embodiments, the crawler  202   e  may be configured to acquire content from a variety of media, such as, but not limited to, websites, news articles, blog posts, keyword data (e.g., stored in the database  202   f  of the keyword tool  202 ), internet forums, social networking sites, advertising sites, and/or the like. The content to be acquired by the crawler  202   e  may be located at servers  106   a - 106   n . The keyword tool  202  (e.g., the crawler  202   e ) may analyze the acquired content to build a collection of sources (e.g., articles, blogs, etc.) related to the input keywords. In some embodiments, the crawler  202   e  may be configured to perform advanced semantic processing, such as, but not limited to, stemming and lemmatization. 
     In some embodiments, the database  202   f  may include data acquired from one or more other sources. In some embodiments, the database  202   f  may include keyword data acquired or obtained from a search engine. In some embodiments, the database  202   f  may include keyword data acquired or obtained from a web site. In some embodiments, the database  202   f  may include keyword data acquired or obtained from a social networking site. In some embodiments, the database  202   f  may include keyword data acquired or obtained from a third party that aggregates data and provides data for use or purchase. Any of the systems and methods described herein, such any of any of the algorithms may use the data in the database  202   f  for any of the computations described herein, such as relevance scoring. 
     According to some embodiments, the crawler  202   e  may execute any suitable search software for crawling a given website. In particular embodiments, the crawler  202   e  may be configured to crawl the web by jumping from website to website. In other embodiments, the crawler  202   e  may be configured to capture all pages on a single website, as opposed to jumping from website to website. In such embodiments where the crawler  202   e  is capable of capturing all pages on a single website, the crawler  202   e  may have particularly configured parameters, such as, but not limited to, which directories to exclude, which special directories to include, directories and/or pages that the crawler  202   e  should merely visit but not index, and special customizations in terms of what pattern of pages to include or exclude. In other words, the crawler  202   e  may be configured to exclude certain elements of a website, while including certain other elements of the website. 
     In further embodiments, the crawler  202   e  may be configured to execute crawls so as not to weigh down or hinder servers. In particular embodiments, the crawler  202   e  may be configured to be limited in the number of webpages it fetches during a predetermined amount of time. As a non-limiting example, the crawler  202   e  may be configured to fetch no more than five pages per second. In additional embodiments, the crawler  202   e  may be configured to call the header of a page (e.g., HTTP HEAD request) before fetching the body of the page to retrieve information about the page to determine if the page is something that should be downloaded or not. In yet further embodiments, the crawler  202   e  may be configured to monitor a response time of a server. In other words, the crawler  202   e  may monitor how long it takes a server to send a response to a request, and if the response time drops below a predetermined threshold (e.g., five seconds), the crawler  202   e  may be configured to stand by for a predetermined amount of time (e.g., 20 seconds) before resuming crawling. In still further embodiments, after waiting for the predetermined amount of time, the crawler  202   e  may resume crawling at a slightly more cautious pace than previously exhibited. As a non-limiting example, the crawler  202   e  may crawl at a rate of four pages per second, as opposed to a previous rate of five pages per second. According to various embodiments, once the crawler  202   e  has retrieved sources relevant to the focus keywords, the crawler  202   e  may be configured to interact with any suitable indexing technology, such as, but not limited to, an open source software (e.g., Solr) for indexing of the content acquired by the crawler  202   e . In such embodiments, the indexing technology for use with the crawler  202   e  may be configured to capture necessary fields, clean data, and/or perform statistical analyses on the acquired content. 
     D. Ensemble of Algorithms 
     According to various embodiments, the ensemble of algorithms  202   d  may be a storage medium for storing a plurality of algorithms to be accessed by each of the other tools at the keyword tool  202 , such as, but not limited to, the topic tool  202   a , the content audit tool  202   b , and the topic inventory tool  202   c , which all may be connected to the ensemble of algorithms  202   d  and may operate in conjunction with the ensemble of algorithms  202   d . In other words, the other tools may access the ensemble of algorithms  202   d  and perform operations based on the instructions stored at the ensemble of algorithms  202   d.    
     In some embodiments, the ensemble of algorithms takes as input a set or corpus of pseudo-relevant documents, such as content acquired by the crawler and provides a output a set of semantic-relevance-scored keyphrases or keywords. Accordingly, in various embodiments, in generating a set of semantically relevant topics scored or ranked by relevance, a tool may be configured to receive data corresponding to a corpus of pseudo-relevant documents (e.g., acquired by the crawler  202   e ), and may be instructed by the ensemble of algorithms  202   d  to cleanse and normalize the received documents and information. In some embodiments, the crawler  202   e  may be configured to lemmatize the documents as well. In some embodiments, the ensemble of algorithms  202   d  may instruct all descriptive phrase n-grams in the corpus up to some length to be identified. In some embodiments, the length of the n-grams may be predetermined by a user or administrator. Thresh-holding may then be performed using some function of frequency and available memory. Next, in some embodiments, phrases starting or ending with conjunctives or other stop words may be discarded. In some embodiments, the method includes building term frequency and inverse document frequency matrices to act as a shared resource for subsequent phrases. The most frequent morphological phrase forms may be re-allocated based upon a weight value assigned to the phrase. In some embodiments, a lemmatization-equivalent of unigrams or phrases may be used to group. In further embodiments, the method includes estimating the bayesian prior of phrases. This may be done optionally as an adjunct to the term frequency matrix. In an embodiment, a linear combination of rarity-ranked bayesian priors of the constituent unigrams of the phrase is used. 
     In some embodiments, the ensemble of algorithms  202   d  includes instructions for applying an ensemble of a plurality of different classes of algorithms such as four constituent classes of algorithm. For example, term-ranking functions (TRFs) may be performed based on analysis of phrase distribution in corpus and the estimated phrase priors. First, a core phrase TRF may be performed (e.g., Robertson selection value and Kullback-Leibler distance). Second, a tail phrase TRF may be performed (e.g., Rocchio&#39;s weights, chi-square, and binary independence model). Third, a hyperdictionary graph traversal algorithm may be performed (e.g. TextRank). Fourth, a semantic knowledgebases path traversal score based on terseness of semantic path and some curated predicate weights may be generated (e.g., using ConceptNet or Yago knowledgebases). Each constituent class of algorithm may itself be an ensemble of algorithms under that class, such that the ensemble of algorithms comprises a plurality of ensembles. 
     In some embodiments, the ensemble method may be a weighted combination of the sets of scored phrases from each of the above constituents (e.g. a linear combination, or a linear combination of some normalization of the constituents). The weights may be tuned by how tail-oriented the desired output is (e.g. where tail TRFs&#39; output are weighed more heavily if more tail-like concepts are desired). In other embodiments, the weights may be tuned by how much n-grams of various n are desired (e.g. by multiplication by a function on desired lengths and phrase length under consideration). In some embodiments, unigrams with high bayesian priors are then reduced in the distribution&#39;s weights by some function of the unigram&#39;s prior. As such, the output of this method may be a set of semantic-relevance-scored phrases. 
     Although generally at times the word keyword may be used to described one or more keywords to be used by the systems and methods, keyphrases may be used interchangeably with keywords. In some embodiments, a keyphrase of one phrase is a keyword. In some embodiments, a keyword in a single phrase keyphrase. As such, in some embodiments, a plurality of keywords is a keyphrase. 
     E. Topic Tool 
     Referring to  FIG. 2B , illustrated is an embodiment of a screen shot of a topic tool page. In various embodiments, the screen shot may represent a keyword user interface (UI)  210  to be presented to a user at the client device  102 . After a user populates entries into the keyword UI  210  and submits the entries, the topic tool  202   a  may receive the entered information via the network  104  to perform one or more operations with respect to the user entries. The functions of the topic tool  202   a  are discussed further below. 
     According to the embodiment shown in  FIG. 2B , the keyword UI  210  may include an input portion including a keyword field  211  for receiving and entering one or more user-entered keywords  212 . In addition, the keyword UI  210  may also include an output portion including a topic table  213  for displaying topic results of the one or more operations performed with respect to the one or more keywords  212 . The topic table  213  may include one or more column headers or subjects  213   a - 213   n , each column header indicating a subject characteristic, attribute, or statistic associated with the topic results, for example, but not limited to, the name of the topic(s), a relevance score of the topic to the one or more entered keywords, a volume value of the topic, and/or the like. Each of the columns  213   a - 213   n of the topic table  213  may include one or more corresponding resulting values  214   a - 214   n ,  215   a - 215   n , and  216   a - 216   n  indicating results of the one or more operations performed by the topic tool  202   a  and corresponding to the one or more column headers  213   a - 213   n.    
     In other embodiments, the keyword UI  210  may include any suitable variation of the layout illustrated in  FIG. 2B , or even different layouts, for example, but not limited to, the keyword field  211  and the topic table  213  being in opposite locations at the keyword UI  210 . In alternative embodiments, the user input interface may take on other suitable formats, for example, but not limited to, a list for user selection and/or the like. In some embodiments, the keyword field  211  may auto-populate the one or more keywords  212  as a user types into the keyword field  211 . In further embodiments, a list of keywords may be stored in the database  202   f , for example, to auto-populate the one or more keywords. In additional embodiments, the results of the one or more operations may be displayed in other suitable formats, for example, but not limited to, a chart, a graph, etc., or combinations thereof. 
     After entering the one or more keywords  212 , and submitting the entries, the topic tool  202   a  may identify topics that are relevant to the entered one or more keywords  212 . In one embodiment, the topic tool  202   a  identifies and generates a set of semantically relevant topics to the one or more keywords  212 . In one embodiment, the results of the one or more operations performed by the topic tool  202   a  are displayed at a topic table  213  at the first column  213   a . The column  213   a  may list the identified one or more topics that are related to the one or more user-entered keywords  212  as topic results  214   a - 214   n . In various embodiments, the crawler  202   e  searches the web to acquire the topics  214   a - 214   n  related to the keywords  212 . The method and operation of identifying and generating the topic list  214   a - 214   n , performed by the keyword tool  202 , are described in further detail below in connection with  FIG. 2C . 
     In further embodiments, the topic tool  202   a  may perform additional analyses with respect to the identified topic results  214   a - 214   n , and generate results of the additional analyses for viewing at the topic table  213 . In the embodiment shown in  FIG. 2B , an additional analysis may include a relevance analysis for each topic result  214   a - 214   n . According to the present embodiment, the results of the relevance analysis may be displayed under column  213   b  as relevance scores  215   a - 215   n  associated with the corresponding topic results  214   a - 214   n . According to various embodiments, the relevance scores  215   a - 215   n  are a measure of topical relevance and indicate a strength of relevance between the one or more keywords  212  and the corresponding topic result  214   a - 214   n . According to the present embodiment, the relevance scores  215   a - 215   n  take the form of percentages (e.g., out of 100%) to indicate the strength of the corresponding topic  214   a - 214   n  (e.g., the higher the percentage, the more relevant the corresponding topic  214   a - 214   n  is). In other embodiments, the relevance scores  214   a - 214   n  may be embodied in any suitable form to indicate the strength of relevance of a topic  214   a - 214   n  to a user-entered keyword  212 , such as, but not limited to, color representation (e.g., where a shade or type of color indicates topic relevance strength), tally representation (e.g., where a number of displayed stars indicates relevance strength), meter representation, and/or the like. In the present embodiment, the topics  214   a - 214   n  are sequenced or ranked by their corresponding relevance scores  215   a - 215   n  in descending order. In other embodiments, the topics  214   a - 214   n  may be ranked in any suitable sequence, such as, but not limited to, according to volume values of the topics  214   a - 214   n , according to alphabetical order, or according to any other suitable characteristic of the topics. In some embodiments, the topics  214   a - 214   n  may be actively ranked according to a user preference. 
     Similarly, a further additional analysis may include a volume analysis for each topic result  214   a - 214   n . In some embodiments, the results of the volume analysis may be displayed under column  213   c  as volume values or scores  216   a - 216   n  associated with the corresponding topic results  214   a - 214   n . According to some embodiments, the volume values  215   a - 215   n  are an indication of how often the corresponding topic result  214   a - 214   n  have been used in various forms of media, such as, but not limited to, social networking websites, marketing tools and databases, websites, news articles, blog posts, other forms of internet related advertising, and/or the like. According to the present embodiment, the volume values  216   a - 216   n  take the form of a number of hits or a measure of frequency to indicate the volume of the corresponding topic  214   a - 214   n . In further embodiments, the volume score  216   a - 216   n  indicates a frequency in terms of individual instances, number of webpage hits, frequency in title hits, a number of pages where the corresponding topic occurs, and/or the like. In other embodiments, the volume values  216   a - 216   n  may be embodied in any suitable form to indicate the volume or relative volume of a topic  214   a - 214   n , such as, but not limited to, color representation, tally representation, meter representation, and/or the like. 
     By way of example, according to the embodiment shown in  FIG. 2B , a user of keyword UI  210  may enter into the field  211  “dog food” as a keyword, and submit the keyword  212  for analysis by the topic tool  202   a . Accordingly, the topic tool  202   a  may output a list in the form of topic table  213  of topics  214   a - 214   n  related to the keyword “dog food,” such as “pet food,” “pet foods,” “homemade dog food,” etc. Furthermore, the topic tool  202   a  may rank these topics according to their relevance to the keyword “dog food,” as indicated by the relevance scores  215   a - 215   n . In this embodiment, the topic “pet food” corresponds to a relevance score of 65% to the user-entered keyword “dog food.” In addition, the topic “pet food” corresponds to a volume value of 5,400. As such, a user may read and interpret the information embodied in topic table  213  and can identify further keywords or topics to include in their content to improve the quality of the content and to increase organic traffic to the website by using the topic table  213  for SEO. The listed topics  214   a - 214   n  may be included to enhance traffic to any webpage, as desired, such as, but not limited to, blog posts, websites, forum postings, articles, and/or the like. 
     Referring to  FIG. 2C , a block diagram of a method  220  for performing keyword research and analysis of a website is depicted. The method may result in a list of suggested keywords to be added to the user&#39;s website for SEO. The method and algorithms described in connection with  FIG. 2C  may also be utilized in connection with the embodiments described in connection with  FIG. 2B , where applicable. In the present embodiment, the method  220  may include a user input block  222 , an acquire content block  224 , a build knowledge graph block  226 , and a serve suggestions block  228 . 
     According to various embodiments, the block  222  may be performed at a user interface  221  at a client  102 . The UI  221  may include a focus keyword field  225  (similar to the keyword field  211 ) for a user to populate with one or more focus keywords  225   a  (similar to the one or more keywords  212 ). The UI  221  may further include a website field  223  for a user to populate with a website address  223   a  that the user wishes to perform the keyword analysis on. In some embodiments, the focus keywords  225   a  may be words or topics a user or administrator uses or plans to use at the website address  223   a . In various embodiments, the website address  223   a  is a Uniform Resource Identifier (URI), a Uniform Resource Locator (URL), a Uniform Resource Name (URN), or any other suitable protocol for identifying a website address. The UI  221  may further include a crawler status field  227  for identifying a status of the crawler  202   e . In some embodiments, the crawler status field  227  may indicate whether the crawler  202   e  is running or not, whether the crawler  202   e  has completed its crawls, whether the crawler  202   e  is inactive or disabled, and/or the like. During block  222 , a user may provide the website address  223   a  for performing content auditing on and the one or more focus keywords  225   a  to be researched and analyzed. After the fields of the UI  221  are populated and submitted by a user, the entered information may be transmitted to the keyword research and analysis tool  202  via the network  104 , at which one or more operations may be performed corresponding to the focus keywords  225   a  and the website address  223   a , as described below. 
     According to various embodiments, at block  224 , the submitted focus keywords  225   a  are received by the crawler  202   e . The crawler  202   e  may then acquire content from various web sources to build a corpus of collected content that is relevant to the focus keywords  225   a . Further details regarding the operation of the crawler  202   e  are described above. 
     According to various embodiments, at block  226 , after collecting and indexing the relevant sources from the web (e.g., via the crawler  202   e ), a knowledge graph  229  may be generated by applying, by the keyword tool  202  (e.g., by the topic tool  202   a ), the ensemble of algorithms  202   d  to the content acquired by the crawler  202   e  to identify and organize a set of semantically relevant topics from the acquired content, as illustrated in the knowledge graph  229 . In some embodiments, the step of generating a knowledge graph  229  may be optional, and the topic tool  202   a  may directly output the related topic results to the user based on the results of application of the ensemble of algorithms to the acquired content, and skip the building of any knowledge graph. 
     In various embodiments, the knowledge graph  229  is a knowledge base that organizes information gathered from the various web sources to provide structured and detailed information regarding each of the user-entered one or more focus keywords  225   a . In some embodiments, after the topic tool  202   e  applies the ensemble of algorithms  202   d , as described above, to the content acquired by the crawler  202   e  to generate the semantically relevant topics scored by relevance, the knowledge graph  229  may be built based on the semantically relevant topics scored by relevance. In other words, the knowledge graph  229  may be a representation of the semantically relevant topics that are ranked by relevance. In some embodiments, the knowledge graph  229  includes degrees of conceptual relevance to the initially entered focus keywords  223   a , which serve as the seed terms from which the knowledge graph is built. For example, as illustrated in the embodiment of  FIG. 2C , one of the focus keywords  225   a  is “java monitoring,” which is located at a first level (or seed level)  229   a  of the knowledge graph  229 . After applying the above described algorithm to the content previously acquired by the crawler  202   e , the topic tool  202   a  may be configured to populate lower levels of the knowledge graph branching out from the seed level  229   a . In the present embodiment, the topic “java profilers” and “SaaS solutions” were identified as having relatively high relevance, based on the acquired content and as determined using the above-described algorithms, to the focus keyword “java monitoring.” Accordingly, the topic “java profilers” and “SaaS solutions” are placed closest to the seed term “java monitoring” on the knowledge graph at the second level  229   b  of the knowledge graph. Similarly, the topic “best java profiler” was determined to be less relevant than the terms at level two  229   b , but still relevant to the focus keyword “java monitoring,” and so is placed at a third level  229   c  of the knowledge graph  229 , and so on. 
     Referring to block  228 , based on the knowledge graph  229  built in block  226 , topic suggestions may be served to the user (e.g., by the topic tool  202   a ) and displayed at the UI via suggestion table  230 . In other embodiments, the topic suggestions are served based directly on the results of the topic tool&#39;s application of the ensemble of algorithms to the acquired content, rather than based on a knowledge graph. In some embodiments, the knowledge graph  229  may be compared with the content identified from the initially entered web address  223   a . Accordingly, keyword performance data and high-priority gaps in keywords at the web address  223   a  may be identified and highlighted. 
     The suggestion table  230  may be for displaying topic results identified by the knowledge graph  229 . The suggestion table  230  may include one or more column headers or subjects  230   a - 230   d , each column header indicating a subject characteristic, attribute, or statistic associated with the topic results, for example, but not limited to, the name of the topic ( 230   a ), a relevance score of the topic ( 230   b ), a frequency of the topic ( 230   c ), an attractiveness of the topic ( 230   d ), and/or the like. Each of the columns  230   a - 230   d  of the suggestion table  230  may include one or more corresponding resulting values indicating results of that particular column  230   a - 230   d . The name of the topic column  230   a  may include a plurality of topics identified by the knowledge graph  229  as being relevant to the focus keywords  225   a . The values at the frequency column  230   b  may indicate the number of instances corresponding topics occur at the web address  223   a . The frequency column  230   b  may be similar to the volume column  213   c . The values of the relevance column  230   c  may indicate a degree of relevance a corresponding topic is to the focus keywords  225   a . The relevance column  230   c  may be similar to the relevance column  213   b . The values of the attractiveness column  230   d  may indicate a strength of the suggestion according to a topic, and may be based on the frequency and relevance values of a given topic. The attractiveness value may be a combination of the relevance score in combination with one or more keyword metrics. In some embodiments, the attractiveness score is determined by applying certain weights on each of the frequency value and the relevance value. As an example, the higher the relevance score and the lower the frequency score corresponding to a topic are, the higher the attractiveness score for that topic may be. In further embodiments, the suggested topics may be provided to the user in a list format and ranked by at least one of the frequency score, the relevance score, and the attractiveness score. In other embodiments, the suggested topics may be ranked in other orders depending on a preference of the user or administrator. 
     In some embodiments, a competitor&#39;s website may be entered as the web address  223   a  so that a user may analyze competitor content. In such embodiments, the topic tool  202   a  may crawl one or more competitor sites, analyze their mentions, and compare the competitor content to the content of a user&#39;s website. This competitor analysis feature may be utilized by the other tools of the keyword tool  202  as well (e.g., content audit tool  202   b  and topic inventory tool  202   c ) 
     Accordingly, in some embodiments, the suggestion table  230  may highlight the absolute gaps between the listed related topics and the website at web address  223   a  (e.g., whether a related topic is totally missing from the website of web address  223   a , which may be indicated by a corresponding frequency or volume score of 0). In some embodiments, the content analyzer may highlight relative gaps between the listed related topics and the website at web address  223   a  (e.g., a related keyword that is mentioned on the website, but is not mentioned frequently). In some embodiments, relative gaps may be determined based, for example, the strength of relevance of the related keyword and the size of the website (e.g., a highly relevant topic that is not mentioned too often at a large website may qualify as a relative gap). 
     In some embodiments, the suggestion table  230  may also highlight related topics that may be mentioned very often. For example, if a topic has a very high number of mentions (e.g. as indicated by the topics frequency and/or volume score), a user may be prompted to focus efforts on other topics rather than add content concerning the topic that is frequently mentioned. For example, if a user sees a high number of mentions, titles, and pages containing a topic and related topics, the topic may already have coverage and the collection of content that is a) targeted for the topic, and b) contains the topic, may be examined further so that the performance of those pages may be evaluated. If it is determined that the user is a top authority on the topic, the user may decide that there are other priority topics in which to invest content. However, for other customers, this scenario may lead to using the various tools in the keyword tool  202  to improve content that has mentions of the highly occurring target topic, such as, but not limited to, content audit to evaluate the pages containing the topic, competitive analysis to determine other sites&#39; mentions and architecture, and other strategic planning initiatives that take advantage of the existing coverage or more beneficially position the existing coverage. For example, if a user sees that they have a very high number of mentions of a topic, but no mentions in any titles, the user may consider planning initiatives that look at whether there should be content created or updated to refine targeting for the pages and include the topic in titles. As another example, if the customer sees that they have a very high number of mentions of a topic, mentions in titles and a lot of pages containing the topic, but they receive no traffic and have no rankings on that topic, that may be an indication that there is more investigation into that topic required and that there may be other needs and problems. For example, a problem may be that the content where the mentions occur is not comprehensive enough, is low quality, or competition is very high and more work is needed to establish rankings for this topic and related keywords with respect to the competition. As yet another example, if the user sees that they have a very high number of mentions of a topic, mentions in title and many pages containing the topic, but they receive no traffic and have many rankings on that topic and related keywords/topics, that may signal that there may be much opportunity for them to add pages for additional keyword variants and related topics with the confidence that they will perform well quickly after publication of the added topic. Accordingly, a user may take advantage of the existing coverage to direct or guide additional content development or planning. 
     Referring to  FIG. 2D , another embodiment of a keyword user interface  231  is illustrated. The keyword UI  231  may be similar to keyword UI  210 , but may include expanded features related to ranking of relevance for suggested topics. According to the embodiment shown in  FIG. 2D , the keyword UI  231  may include an input portion including a keyword field  232  for receiving and entering one or more user-entered keywords  232   a  and  232   b . In addition, the keyword UI  232  may also include an output portion including a topic table  235  for displaying topic results with respect to the one or more keywords  232   a  and  232   b . The topic table  235  may include one or more column headers or subjects  235   a - 235   g , each column header indicating a subject characteristic, attribute, or statistic associated with the topic results, for example, but not limited to, the name of the topic ( 235   a ), a relevance score of the topic ( 235   b ) to the one or more entered keywords  232   a  and  232   b , a volume value of the topic ( 235   c ), a competition score ( 235   d ), a cost per click (CPC) value ( 235   e ), an attractiveness score ( 235   f ), a refining option ( 235   g ), and/or the like. Each of the columns  235   a - 235   g  of the topic table  235  may include one or more corresponding resulting values indicating results of the one or more operations performed by the topic tool  202   a . In some embodiments, the data of the topic table  235  is obtained by implementing the ensemble of algorithms  202   d  described above as implemented by the topic tool  202   a.    
     In some embodiments, the topic column  235   a , the relevance column  235   b , the volume column  235   c , and the attractiveness column  235   f  may be similar to the topic column  213   a , the relevance column  213   b , the volume column  213   c , and the attractiveness column  235   f , respectively. In some embodiments, the respective attractiveness values of the column  235   f  may be determined based on one or more of the corresponding values of one or more of the columns  235   a - 235   g . In some embodiments, the values under the competition column  235   d  may indicate a competition or competitive value for the keyword. In some embodiments, the competition value or score indicates a number of advertisers bidding on each keyword relative to other or all keywords across one or more search engines or paid advertising sources. In some embodiments, the competition value or score indicates a relative demand for the keyword among other keywords. In some embodiments, the competition score may be a numeric value in a range. In some embodiments, the competition score may be a string indicating a level of completion to use the keyword. In the “Competition” column, you can see whether the competition for a keyword idea is low, medium, or high,” In some embodiments, the refine column  235   g  may allow a user to adjust a corresponding topic, such as, but not limited to, deleting the row of the corresponding topic, adding or deleting statistics or information of the corresponding topic (e.g., deleting the competition score for the corresponding topic), and the like. In particular embodiments, the topics under the topic column  235   a  may be ranked by the relevance score. In other embodiments, the topics may be ranked by any of the various scores assigned to them. 
     The CPC or cost-per-click value or score may indicate the amount one may earn each time a user clicks on an ad related to the keyword. The CPC value may be a historical or average amount to indicate how much that keyword has earned via paid advertising. The CPC may be determined over any time period from any one or more sources. The CPC may be an average, mean or other statistical measure. In some embodiments, the CPC value may be an indicator of predicted or future value of an amount the keyword should earn via advertising. 
     In further embodiments, the UI  231  may include an add group keywords interface  233 . The add group keywords interface  233  may aid a user by adding multiple related keywords to the keyword field  232 , for example, by providing keyword suggestions, by parsing the keyword field  232  to automatically add related keywords, by providing general keyword subjects that will populate the keyword field  232  with more narrow keywords under the keyword subject, and the like. In yet further embodiments, the UI  231  may include a find keywords interface  234  for aiding a user in identifying and selecting useful keywords. In such embodiments, the find keywords interface  234  may provide the user with a list of subject areas containing useful keywords, may suggest keywords based on the entered keywords  232   a  and  232   b , and the like. 
     Referring to  FIG. 2E , another embodiment of a topic table  240  ranked by relevance is depicted. According to the present embodiment, topic table  240  includes a topic column  240   a  and a relevance column  240   b  having relevance values associated with the topics under the topic column  240   a . In some embodiments, the topic column  240   a  and the relevance column  240   b  may be similar to the topic column  213   a  and the relevance column  213   b , respectively. Furthermore, the topic table  240  may be generated by utilizing the ensemble of algorithms  202   d  by the topic tool  202   a . The topic table  240  displays a generated list of recommended topics with only a relevance score displayed for each topic. The topics may be organized according to the relevance score, from highest score to lowest score. 
     F. Content Audit Tool 
     Referring to  FIG. 2F , an embodiment of a content audit user interface  250  corresponding to the content audit tool  202   b  is depicted. According to the present embodiment, the content audit UI  250  includes a domain or subdomain input field  251  for entering a web address  251   a . The web address  251   a  may correspond to a website or webpage for which a user wishes to perform the content audit operation. In addition, the UI  250  includes a crawler status field  252 , which may be similar to the crawler status field  227  and may further identify a number of pages crawled by the crawler  202   e  after completion of the crawling function. The UI  250  may further include a focus keyword field  253  for entering a focus keyword  253   a . In some embodiments, the focus keyword field  253  may be similar to the keyword field  211 , and may further include one or more filters  253   b . In particular embodiments, the filters  253   b  may serve to exclude or include particular categories of terms from the results of the content audit (e.g., “People&#39;s Names”). 
     After entering and submitting the relevant input data, the input data may be received by the content audit tool  202   b  for performing the content audit functions, that is, to analyze the pages corresponding to the website address  251   a  for identifying all the pages that are relevant to the entered focus keyword  253   a  and for calculating content performance metrics for each of the retrieved pages. In particular embodiments, the web address  251   a  may be crawled by the crawler  202   e  to acquire content of the web address  251   a . In further embodiments, the content audit tool  202   b  may be configured to identify all of the pages of the web address  251   a  that are relevant to the entered focus keyword  253   a , and configured to generate content performance metrics for each of the relevant pages. In some embodiments, the content performance metrics include a content score for each retrieved page, the content score for indicating how well a particular page covers the topic denoted by the focus keyword  253   a . In one embodiment, the content score may be calculated by generating a set of related topics (e.g., by the content audit tool  202   b  in conjunction with the ensemble of algorithms  202   d ), and count the number of mentions the topics have on a given page. In such an embodiment, if a relevant topic is mentioned once on a given page, a first predetermined amount of points are attributed to that page (e.g., one point), and if a relevant topic is mentioned two or more times at the page, a second predetermined number of points may be attributed to the page (e.g., two points). In particular embodiments, the content audit tool  202   b  may provide 50 topic suggestions per keyword input, in such embodiments, a maximum score that a page may receive is therefore 100 (e.g., if each of the topics was mentioned twice at the page). In other embodiments, any suitable method of calculating a content score may be implemented, such as, but not limited to, a simple count of the number of mentions of the topic keywords at a page, a weighted count of the mentions (e.g., more weight may be to a topic mentioned in the title of the page), and/or the like. 
     In some embodiments, the UI  250  further includes a content audit table  254  including a plurality of columns, such as, but not limited to, a page title column  254   a , a mentions column  254   b , a content score column  254   c , and an improve content option  254   d . After analyzing the website address  251   a  for mentions of related topics to the focus keyword  253   a , the resulting data may be organized and displayed in the audit table  254  so that a user may review the results. For example, each of the titles of each of the pages mentioning a relevant topic may be listed under column  254   a , and the corresponding number of mentions of the relevant topics may be listed under column  254   b . In addition, the content score of each of the listed pages may be listed under column  254   c . In some embodiments the content score may be a simple number of points attributed to the corresponding page. In other embodiments, the content score may be illustrated in any other suitable way, such as, but not limited to, a tally, a color, and the like. In the present embodiment, the number corresponding to the content score may also be highlighted with different types or different shades of color to represent the strength of the content score. In some embodiments, each of the listed pages may correspond to an improve content option listed under column  254   d . In particular embodiments, the improve content option, once actuated by a user, may provide suggestions for a user to modify the corresponding page to improve its content score. For example, the content audit tool  202   b  may suggest keyword topics, for incorporation into the page, that are relevant to the focus keyword  253   a . Accordingly, using the content audit tool  202   b , a user may, all at once, identify pages on a website that are highly relevant to a focus keyword, or pages that require modification to increase their relevance to the focus keyword. 
     Referring to  FIG. 2G , an embodiment of a content audit analyzer user interface  260  corresponding to the content audit tool  202   b  is depicted. According to the present embodiment, the content audit analyzer UI  260  is similar to the content audit UI  250 , with some differences which will be discussed below. In some embodiments, instead of requesting a web address, the UI  260  includes a content input field  261  for a user to directly enter content to be analyzed by the content audit tool  202   b . In some embodiments, the content to be entered are excerpts or full content to be posted on a webpage, or content that already exists on a webpage. The UI  260  may further include a focus keyword field  262  for entering a focus keyword  262   a . In some embodiments, the focus keyword field  262  may be similar to the keyword field  211 , and may further include one or more filters  262   b . Accordingly, whereas the embodiment illustrated in  FIG. 2F  is configured to return more robust results corresponding to multiple pages of a web address, the embodiment of  FIG. 2G  may be focused on analysis of particular content entered into the UI  260 . Accordingly, once a user enters the content to be analyzed in field  261  and the focus keyword  262   a . the content audit tool  202   b  may analyze the content in a similar manner as that described above in connection with  FIG. 2F  to determine a content score of the content in field  261 . 
     As such, in some embodiments, the results of the analysis performed by the content audit tool  202   b  may be organized and displayed at topic table  264  including a related topics column  264   a , a count column  264   b , and a relevance score column  264   c . In the present embodiment, a plurality of topics related to the focus keyword  262   a  are listed under column  264   a . Under column  264   b , a number of occurrences corresponding to a given related topic is associated with each listed topic, and, under column  264   c , a corresponding relevance score is also listed. In further embodiments, the UI  260  may display the content score of the content (e.g., based on the overall count score of the content and the corresponding relevance score of the topics mentioned in the content. According to the present embodiment, a user may quickly determine how related content is to a desired focus keyword, and may identify topics that may be added to the content for increasing the relevance of the content to the focus keyword, for example, to increase organic traffic to the content (e.g., SEO). 
     G. Topic Inventory Tool 
     Referring to  FIG. 2H , an embodiment of a topic inventory user interface  270  corresponding to the topic inventory tool  202   c  is depicted. According to the present embodiment, the topic inventory UI  270  includes a domain or subdomain input field  271  for entering a web address  271   a . The web address  271   a  may correspond to a website or webpage for which a user wishes to perform the topic inventory operation. In addition, the UI  270  includes a crawler status field  272 , which may be similar to the crawler status field  227  and may further identify a number of pages crawled by the crawler  202   e  after completion of the crawling function. The UI  270  may further include a focus keyword field  273  for entering a focus keyword  273   a . In some embodiments, the focus keyword field  273  may be similar to the keyword field  211 . 
     According to some embodiments, the topic inventory tool  202   c  may be configured to identify and analyze the topics in the content of the website at address  271   a . In the embodiment illustrated in  FIG. 2H , the topic inventory tool  202   c  may be configured to perform a targeted analysis with respect to the entered focus keyword  273   a  and the content of the entered web address  271   a . In such embodiments, the topic inventory tool  202   c  may limit its analysis to the pages corresponding to the web address  271   a  that mention the focus keyword  273   a  at least once. Accordingly, the topic inventory tool  202   c  may perform topic modeling in accordance with the ensemble of algorithms  202   d , and run the topic modeling to the filtered list of pages and return relevance scores and other corresponding statistics. 
     As such, in some embodiments, the results of the analysis performed by the topic inventory tool  202   c  may be organized and displayed at topic table  274  including a related topics column  274   a , a relevance score column  264   b , a mentions column  274   c , a title column  274   d , a body column  274   e , and a pages column  274   f . In the present embodiment, a plurality of topics related to the focus keyword  273   a  are listed under column  274   a . Under column  274   b , a relevance score corresponding to an associated topic is listed, and under column  274   c , a number of mentions corresponding to the corresponding related topic is listed. In some embodiments, the values under title column  274   d  may indicate the number of mentions of the corresponding topic in titles of the pages of the website at web address  271   a . Similarly, the values under body column  274   e  may indicate the number of mentions of the corresponding topic in the bodies of the pages of the website at web address  271   a . Furthermore, the values under pages column  274   f  may indicate the number of page of the website at web address  271   a  the corresponding topic is mentioned. In further embodiments, other statistics may be included as columns of the topic table  274 , such as, but not limited to, the popularity of the corresponding topic and the like. According to the present embodiment, a user may quickly be able to identify related topics to a desired focus keyword, and may identify how prevalent each of the related topics are at a website, for example, to identify topics that should be expanded on to increase organic traffic to the content (e.g., SEO). 
     Referring to  FIG. 2I , another embodiment of a topic inventory user interface  280  corresponding to the topic inventory tool  202   c  is depicted. According to the present embodiment, the topic inventory UI  280  is similar to the topic inventory UI  270 . However, in the present embodiment, a focus keyword field  283  is left blank and may be an optional entry. Accordingly, in the present embodiment, instead of a targeted analysis based on a user-entered focus keyword, the topic inventory tool  202   c  may perform an automatic or unsupervised analysis of all the pages of the website at the entered web address  281   a . According to such embodiments, the topic inventory tool  202   c  may identify which topics are most relevant across all of the website&#39;s content, and may returns a list of relevant topics, including relevance scores that measure the degree of relevance, and other statistics, such as, but not limited to, page count frequencies. In some embodiments, such an analysis may be performed by the topic inventory tool  202   c  which may utilize the crawler  202   e  to crawl the pages of the website and may receive the acquired content from the crawler, and may utilize the ensemble of algorithms  202   d  for topic modeling to identify and rank by relevance the various topics of the website. In some embodiments, the topic inventory tool  202   c  may perform the topic modeling of the website. In other embodiments, the topic tool  202   a  may perform the topic modeling. 
     As such, in some embodiments, the results of the analysis performed by the topic inventory tool  202   c  may be organized and displayed at topic table  284 , which may be similar to topic table  274 . In some embodiments, the topic table  284  includes a related topics column  284   a , a relevance score column  284   b , a volume score column  284   c , a mentions column  284   d , a title column  284   e , a body column  284   f , and a pages column  284   g . In the present embodiment, a plurality of topics extracted from the website at web address  281   a  are listed under column  284   a . Furthermore, in columns  284   b - 284   g , statistics and characteristics corresponding to the topics are listed. In some embodiments, the topic table  284  may include a highlight threshold field  285 . The highlight threshold field  285  may allow a user to highlight certain topics that occur below a user-entered threshold (e.g., any topic that is mentioned less than 34 times). Accordingly, the topic inventory UI  280  can immediately alert a user to those topics that may require attention. According to the present embodiment, a user may quickly be able to identify prominent topics of a given website, and may discern how prevalent each of the topics are at the website, for example, to identify topics that should be expanded on to increase organic traffic to the website (e.g., SEO). 
     Referring to  FIG. 3A , a method  310  for generating from one or more keywords a list of related topics for organic search is depicted. At step  311 , the method  310  includes receiving, by a topic tool, an input of one or more keywords for which to generate a list of related topics. At step  312 , the method  310  includes acquiring, by a crawler, content from a plurality of different web content sources via one or more networks. At step  313 , the method  310  includes applying, by the topic tool, to the acquired content an ensemble of algorithms to identify a set of semantically relevant topics scored by relevance. At step  314 , the method  310  includes generating, by the topic tool, from the set of semantically relevant topics, a knowledge graph of related topics for the input of the one or more keywords. At step  314 , the method  310  includes outputting, by the topic tool based at least partially on the knowledge graph, an enumerated list of topics ranked by at least a relevance score. 
     In some embodiments, step  311  may be performed by the topic tool  202   a . In such embodiments, the topic tool  202   a  may receive the one or more keywords for which to generate the list of related topics from a user interface at a client  102  and via the network  104 . For example, the user interface may correspond to UI  210  or any other UI identified above. The one or more keywords may be various words or phrases that a user wishes to acquire related topics for. For example, keywords such as keyword  212  may be entered into the UI. 
     In some embodiments, step  312  may be performed by the crawler  202   c . In such embodiments, the crawler  202   c  may be configured to acquire content from various sources, such as, but not limited to, websites, blogs, articles, and/or the like. For example, the step of acquiring content by the crawler  202   e  may correspond to block  224 . 
     In some embodiments, step  313  may be performed by the topic tool  202   a , which may receive the content acquired from the crawler  202   e . In further embodiments, the topic tool  202   a  may access and work in conjunction with the ensemble algorithm  202   d  in applying the algorithms to the acquired content. In particular embodiments, the ensemble of algorithms  202   d  may include one or more key phrase extraction algorithms, one or more graph analyses algorithms, and one or more natural language processing algorithms. 
     In some embodiments, step  314  may be performed by the topic tool  202   a  after the topic tool  202   a  identifies the set of semantically relevant topics scored by relevance from the content acquired by the crawler  202   e . The knowledge graph may correspond to the knowledge graph  229  depicted in block  226 . In other embodiments, the generation of the knowledge graph is an optional step and may be skipped. 
     In some embodiments, step  315  may be performed by the topic tool  202   a . In particular embodiments, the topic tool  202   a  may output the list of topics ranked by the relevance scores to the client  102  via the network  104 . As such, the list of topics may be displayed at the UI at the client  102 . In particular embodiments, the list of topics may take the form of the topic table  213  or any other table identified above. The list of topics may further include a relevance score associated with each topic, and the list of topics may be organized from a highest relevance score to a lowest relevance score. 
     Referring to  FIG. 3B , a method  320  for auditing content for topic relevance is depicted. At step  321 , the method  320  includes receiving a focus one or more keywords for a website, the website crawled by a crawler for content. At step  322 , the method  320  includes applying to the content an ensemble of algorithms to identify a set of semantic relevant topics scored by relevance. At step  323 , the method  320  includes identifying a plurality of pages of the website with one or more related topics from the set of semantically relevant topics. At step  324 , the method  320  includes generating a content performance metric for each page of the plurality of pages. At step  325 , the method  320  includes outputting a topical content score for the content, the topical content score identifying a level of coverage of the topic by the content of the website. 
     In some embodiments, step  321  may be performed by the content audit tool  202   b . In such embodiments, the content audit tool  202   b  may receive the focus one or more keywords from a user interface at a client  102  and via the network  104 . For example, the user interface may correspond to UI  250  or any other UI identified above. The focus one or more keywords may be various words or phrases that a user wishes to analyze the provided website for. For example, keywords such as keyword  253   a  may be entered into the UI. In further embodiments, the crawler  202   e  receives the website address from the UI via the network  104 . 
     In some embodiments, step  322  may be performed by the content audit tool  202   b , which may receive the content acquired from the crawler  202   e . In further embodiments, the content audit tool  202   b  may access and work in conjunction with the ensemble of algorithms  202   d  in applying the algorithms to the acquired content. In particular embodiments, the ensemble of algorithms  202   d  may include one or more key phrase extraction algorithms, one or more graph analyses algorithms, and one or more natural language processing algorithms. 
     In some embodiments, step  323  may be performed by the content audit tool  202   b . The content audit tool  202   b  may receive the content acquired by the crawler  202   e  and parse the content to identify relevant pages of the website. 
     In some embodiments, step  324  may be performed by the content audit tool  202   b . The content performance metric may be based on the frequency that relevant topics occur in the pages of the website. 
     In some embodiments, step  325  may be performed by the content audit tool  202   b . In particular embodiments, the content audit tool  202   b  may output the topical content score to the client  102  via the network  104 . As such, the topical content score may be displayed at the UI at the client  102 . In particular embodiments, the content score may take the form of the table  254  or  261 . In some embodiments, content scores are associated with corresponding pages of the website ( FIG. 2F ). In other embodiments, the content score is associated with user-entered content ( FIG. 2G ). 
     While the invention has been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention described in this disclosure.