Patent Publication Number: US-9846740-B2

Title: Associative search systems and methods

Description:
BACKGROUND 
     Technical Field 
     The technical field relates generally to search engine based information retrieval and, more particularly, to systems and methods for providing comprehensive and relevant results to search queries. 
     Background Discussion 
     Information based economies generate vast amounts of stored information. Efficient and effective use of this stored information continues to be a challenge for individuals and organizations alike. However, several tools and techniques have emerged to meet this challenge. For instance, most personal computing devices, such as personal computers, personal digitals assistants, and smart phones include easily accessible, user-friendly search functions. These search functions enable a user to quickly find, review, and utilize information (e.g., personal contact information, scheduling information, and software applications) stored on these devices. 
     Perhaps the most widely known and utilized search technology is the internet search engine. Internet search engines crawl the web collecting, storing and organizing information for rapid retrieval via search interface. More specifically, internet search engines allow users to search for documents by submitting queries including one or more keywords. Normally, search engines parse submitted queries and find result documents that prominently feature the keywords included in the query. Upon identifying one or more result documents, internet search engines present the result documents to a user for review and selection. 
     SUMMARY 
     According to various aspects and embodiments, a system is configured to discover, store, and utilize associated words to enhance search accuracy. More specially, in some embodiments, the system executes an associative search that uncovers relationships between terms through document comparison and analysis. Once these relationships are surfaced, the system records the related terms and subsequently executes search queries utilizing the related terms. These subsequently executed search queries may result in additional documents relevant to a primary search query. 
     As described in detail below, in some embodiments, the system is configured to perform this analysis in near real-time. In these embodiments the system executes an associative search process that innovatively utilizes artifacts created during conventional search engine processing to identify secondary terms within documents that may be related to terms specified within the primary search query. These artifacts include both scores that indicate the relevancy of identified documents and the terms in the document that contributed to these scores. The associative search may evaluate one or more rules and parameters to determine whether these terms should be recorded as a related term. Further, as described below, the associative search may evaluate additional rules to determine whether and how many documents to analyze for related terms and how many related terms to record. 
     According to at least one embodiment, a computer system is provided. The computer system includes a memory, at least one processor coupled to the memory, and a search component executable by the at least one processor. The search component is configured to receive information descriptive of at least one search term; execute a first query against a plurality of documents that identifies at least one first document of the plurality of documents responsive to the at least one search term, wherein the at least one document includes the at least one search term; identify one or more secondary terms associated with the at least one first document based on occurrence of the one or more secondary terms within the at least one first document; and provide a search result including at least one of the one or more secondary terms and one or more identifiers of one or more documents including the one or more secondary terms. 
     In the computer system, the at least one first document may include a plurality of documents, the memory may be configured to store a configurable parameter specifying a maximum number of documents, and the search component may be further configured to limit the plurality of documents to a number of documents having a predefined relationship with the configurable parameter. The at least one first document may be an email or an attachment to an email. The one or more secondary terms may include a plurality of secondary terms, the memory may be configured to store a configurable parameter specifying a maximum number of secondary terms, and the search component may be further configured to limit the plurality of secondary terms to a number of secondary terms having a predefined relationship with the configurable parameter. The search result may include at least one identifier of a bookmarked document. The search result may include an identifier of an email or an attachment of an email. The bookmarked document may include an email or an attachment of an email. 
     In the computer system, the one or more secondary terms may include a plurality of secondary terms and the search component may be further configured to score each secondary term of the plurality of secondary terms according to a frequency with which each secondary term occurs within the at least one first document. The search component may be configured to score each secondary term using at least one of a term frequency-inverse document frequency process and an Okapi BM25 process. The search component may be configured to score secondary terms using other processes. 
     The computer system may further comprise an interface component configured to display each secondary term of the plurality of secondary terms sorted by score within the search result. The interface component may be further configured to receive information identifying at least one secondary term of the plurality of secondary terms; and identify, responsive to receiving the information identifying the at least one secondary term, one or more additional documents including the at least one secondary term. 
     In the computer system, the one or more documents may include one or more seed documents and the search component may be further configured to identify at least one seed document of the one or more seed documents; identify at least one additional secondary term associated with the at least one seed document based on occurrence of the at least one additional secondary term within the at least one seed document; identify one or more additional documents including the at least one additional secondary term; and provide the one or more additional documents within the search result. The interface component may be configured to receive information selecting the at least one seed document. 
     According to another embodiment, a method of executing an associative search using a computer system including memory and at least one processor coupled to the memory is provided. The method includes acts of receiving, by the computer system, information describing at least one search term; executing, by the computer system, a first query against a plurality of documents that identifies at least one first document of the plurality of documents responsive to the at least one search term, wherein the at least one document includes the at least one search term; identifying, by the computer system, one or more secondary terms associated with the at least one first document based on occurrence of the one or more secondary terms within the at least one first document; and providing, by the computer system, a search result including at least one of the one or more secondary terms and one or more identifiers of one or more documents including the one or more secondary terms. 
     In the method, the at least one first document may include a plurality of documents, the memory may be configured to store a configurable parameter specifying a maximum number of documents, and the method may further include an act of limiting the plurality of documents to a number of documents having a predefined relationship with the configurable parameter. The predefined relationship may be an equality or an inequality. The one or more secondary terms may include a plurality of secondary terms, the memory may be configured to store a configurable parameter specifying a maximum number of secondary terms, and the method may further include an act of limiting the plurality of secondary terms to a number of secondary terms having a predefined relationship with the configurable parameter. The act of providing the search result may include an act of providing at least one identifier of a bookmarked document. The one or more secondary terms may include a plurality of secondary terms and the method may further include an act of scoring each secondary term of the plurality of secondary terms according to a frequency with which each secondary term occurs within the at least one first document. The act of scoring each secondary term may include an act of scoring each secondary term using at least one of a term frequency-inverse document frequency process and an Okapi BM25 process. Secondary terms may be scored using other processes. 
     The method may further include an act of displaying the plurality of secondary terms sorted by score within the search result. The method may also further include acts of receiving information identifying at least one secondary term of the plurality of secondary terms; and identifying, responsive to receiving the information identifying the at least one secondary term, one or more additional documents including the at least one secondary term. 
     In the method, the one or more documents may include one or more seed documents and the method may further include acts of identifying at least one seed document of the one or more seed documents; identifying at least one additional secondary term associated with the at least one seed document based on occurrence of the at least one additional secondary term within the at least one seed document; identifying one or more additional documents including the at least one additional secondary term; and providing the one or more additional documents within the search result. The act of identifying the at least one seed document may include an act of identifying the at least one seed document in response to receiving information selecting the at least one seed document. 
     According to another embodiment, a non-transitory computer readable medium is provided. The computer readable medium stores instructions executable by at least one processor to execute an associative search method. The instructions instruct the at least one processor to receive information describing at least one search term; execute a first query against a plurality of documents that identifies at least one first document of the plurality of documents responsive to the at least one search term, wherein the at least one document includes the at least one search term; identify one or more secondary terms associated with the at least one first document based on occurrence of the one or more secondary terms within the at least one first document; and provide a search result including at least one of the one or more secondary terms and one or more identifiers of one or more documents including the one or more secondary terms. 
     According to the computer readable medium, the at least one first document may include a plurality of documents and the instructions may further instruct the at least one processor to limit the plurality of documents to a number of documents having a predefined relationship with a configurable parameter specifying a maximum number of documents. The one or more secondary terms may include a plurality of secondary terms and the instructions may further instruct the at least one processor to limit the plurality of secondary terms to a number of secondary terms having a predefined relationship with a configurable parameter specifying a maximum number of secondary terms. The instructions that instruct the at least one processor to provide the search result may include instructions that instruct the at least one processor to provide at least one identifier of a bookmarked document. 
     According to the computer readable medium, the one or more secondary terms may include a plurality of secondary terms and the instructions may further instruct the at least one processor to score each secondary term of the plurality of secondary terms according to a frequency with which each secondary term occurs within the at least one first document. The instructions that instruct the at least one processor to score each secondary term may include instructions that instruct the at least one processor to score each secondary term using at least one of a term frequency-inverse document frequency process and an Okapi BM25 process. The instructions may instruct the at least one processor to score secondary terms using other processes. The instructions may further instruct the at least one processor to display the plurality of secondary terms sorted by score within the search result. 
     According to the computer readable medium, the instructions may further instruct the at least one processor to receive information identifying at least one secondary term of the plurality of secondary terms and identify, responsive to receiving the information identifying the at least one secondary term, one or more additional documents including the at least one secondary term. 
     According to the computer readable medium, the one or more documents may include one or more seed documents and the instructions may further instruct the at least one processor to identify at least one seed document of the one or more seed documents; identify at least one additional secondary term associated with the at least one seed document based on occurrence of the at least one additional secondary term within the at least one seed document; identify one or more additional documents including the at least one additional secondary term; and provide the one or more additional documents within the search result. The instructions may further instruct the at least one processor to receive information identifying the at least one seed document and the instructions that instruct the at least one processor to identify the at least one seed document may include instructions that instruct the at least one processor to identify the at least one seed document in response to receiving the information identifying the at least one seed document. 
     Still other aspects, embodiments and advantages of these example aspects and embodiments, are discussed in detail below. Moreover, it is to be understood that both the foregoing information and the following detailed description are merely illustrative examples of various aspects and embodiments, and are intended to provide an overview or framework for understanding the nature and character of the claimed aspects and embodiments. Any embodiment disclosed herein may be combined with any other embodiment. References to “an embodiment,” “an example,” “some embodiments,” “some examples,” “an alternate embodiment,” “various embodiments,” “one embodiment,” “at least one embodiment,” “this and other embodiments” or the like are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. The appearances of such terms herein are not necessarily all referring to the same embodiment. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Various aspects of at least one embodiment are discussed below with reference to the accompanying figures, which are not intended to be drawn to scale. The figures are included to provide an illustration and a further understanding of the various aspects and embodiments, and are incorporated in and constitute a part of this specification, but are not intended as a definition of the limits of any particular embodiment. The drawings, together with the remainder of the specification, serve to explain principles and operations of the described and claimed aspects and embodiments. In the figures, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every figure. In the figures: 
         FIG. 1  is a block diagram including a system for executing associative searches; 
         FIG. 2  is a schematic diagram of an associative search engine; 
         FIG. 3  is a schematic diagram of a search interface; 
         FIG. 4  is a schematic diagram of a search interface including search results; 
         FIG. 5  is a schematic diagram of another search interface including search results; 
         FIG. 6  is a schematic diagram of a computer system; 
         FIG. 7  is a flow diagram of a process for executing associative searches; 
         FIG. 8  is a flow diagram of a process for executing a search query; 
         FIG. 9  is a flow diagram of a process for identifying secondary terms within a set of documents; 
         FIG. 10  is a sequence diagram illustrating an associative search process according to one embodiment; and 
         FIG. 11  is a sequence diagram illustrating another associative search process according to another embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Some embodiments disclosed herein include apparatus and processes for discovering associations between terms based on their coincidence within one or more documents. For example, according to one embodiment, a computer system is configured to provide an interface through which the computer system receives a search query including one or more terms of interest to an external entity, such as a user or remote computer system. In response to receipt of the search query, the computer system processes the search query by executing a search engine that parses the search query and identifies one or more documents that are relevant to the search terms included in the search query. In addition, the search engine identifies one or more secondary terms derived from terms that occur frequently within the one or more identified documents and executes additional search queries using the secondary terms as search terms. In these embodiments, the search engine provides links or other indicia of the one or more identified documents, secondary terms, and results of the additional search queries to the external entity as search results. 
     Examples of the methods and systems discussed herein are not limited in application to the details of construction and the arrangement of components set forth in the following description or illustrated in the accompanying drawings. The methods and systems are capable of implementation in other embodiments and of being practiced or of being carried out in various ways. Examples of specific implementations are provided herein for illustrative purposes only and are not intended to be limiting. In particular, acts, components, elements and features discussed in connection with any one or more examples are not intended to be excluded from a similar role in any other examples. 
     Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. Any references to examples, embodiments, components, elements or acts of the systems and methods herein referred to in the singular may also embrace embodiments including a plurality, and any references in plural to any embodiment, component, element or act herein may also embrace embodiments including only a singularity. References in the singular or plural form are not intended to limit the presently disclosed systems or methods, their components, acts, or elements. The use herein of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. References to “or” may be construed as inclusive so that any terms described using “or” may indicate any of a single, more than one, and all of the described terms. In addition, in the event of inconsistent usages of terms between this document and documents incorporated herein by reference, the term usage in the incorporated references is supplementary to that of this document; for irreconcilable inconsistencies, the term usage in this document controls. 
     Associative Search System 
     Various embodiments utilize one or more specially configured computer systems to execute associative searches and provide associative search results.  FIG. 1  illustrates one of these embodiments, an associative search system  100 . As shown,  FIG. 1  includes a user  102 , a search interface  104 , a computer system  106 , a document source system  108 , an associative search engine  110 , and a communication network  112 . The computer system  106 , the document source system  108 , the associative search engine  110 , and the communication network  112  may include one or more computer systems, such as the computer system described below with reference to  FIG. 6 . As illustrated in  FIG. 1 , the computer system  106 , the document management system  108 , and the associative search engine  110  exchange (e.g., send or receive) information via the network  112 . The network  112  may include any communication network through which devices may exchange information. For example, the network  110  may be a public network, such as the Internet, and may include other public or private networks such as LANs, WANs, extranets, intranets, and cloud computing systems. The network  110  may also include cellular networks such as CMDA, EvDO, GSM, and iDEN networks. 
     According to various embodiments, the associative search system  100  is configured to receive and execute associative search queries directed to a corpus of documents. In these embodiments, the associative search system  100  is configured to provide results from executed associative search queries to external entities, such as the user  102 , via one or more interfaces. For example, in some embodiments in accord with  FIG. 1 , the associative search engine  110  is configured to provide the search interface  104  to the user  102  via the computer system  106  and the network  112 . In some embodiments illustrated by  FIG. 1 , the search interface  104  is a browser-based user interface served by associative search engine  110 . In other embodiments, the search interface  104  is a specialized client program that executes outside of a browser environment, such as an application program executing on a mobile device. 
     The search interface  104  may include various interface elements (e.g., screens, windows, buttons, boxes, and other elements) arranged according to a variety of designs and metaphors. For example, in some embodiments, the search interface  104  includes interface elements that exchange information with the user  102  to allow the user  102  to search for, select, and review one or more documents from the corpus of documents. For instance,  FIG. 3  illustrates one example in which the search interface  104  presents a screen  300  with interface elements that receive input from the user  102 . As shown in  FIG. 3 , the screen  300  includes a text box  302 , and an ok button  304 . In this embodiment, these elements are configured to operate as follows. The text box  302  receives input specifying a search query. The search query includes one or more search terms (e.g., words, phrases, and other syntactical constructs) descriptive of documents targeted by the search query. The ok button  304  is configured to initiate execution of the search query in response to being actuated by the user  102 . 
     In one embodiment, in response to receiving input specifying a search query (e.g., population of the text box  302  and actuation of the ok button  304 ), the search interface  104  transmits information descriptive of the search query to the associative search engine  110 . In this embodiment, the associative search engine  110  is configured to process this information to execute the search query with reference to the corpus of documents. The associative search engine  110  is also configured to generate a primary search result. Processes that the associative search engine  110  is configured to execute during execution of the search query and generation of the search result are described further below with reference to  FIGS. 7, 8, 10 , and  11 . 
     The corpus of documents searched via the search query may be stored in a variety of formats at various locations. For instance, in at least one embodiment, the corpus of documents includes email and email attachments stored within a database controlled by the associative search engine  110 . In this embodiment, the associative search engine  110  is configured to import the corpus from the document source system  108  via the network  112 . In other embodiments in accord with  FIG. 1 , the corpus of documents is stored in the document source system  108 . Thus embodiments are not limited to a particular corpus of documents stored at any particular location. 
     In one embodiment, the associative search engine  110  transmits information descriptive of the primary search result to the search interface  104 . This information may include navigable links to documents determined to be relevant to the search query. These links may include hyperlinks that lead to documents stored in the associative search engine  110 , the document source system  108 , or elsewhere.  FIG. 4  illustrates one example in which the search interface  104  presents a screen  400  with interface elements that provide navigable links to documents included in the primary search result. As shown in  FIG. 4 , the screen  400  includes, in addition to the elements described above with reference to  FIG. 3 , a link area  402  that includes links to documents included in the primary search result. In this embodiment, the user  102  may navigate to a particular document by actuating a link associated with the document. 
     In some embodiments, the associative search engine  110  is configured to discover, after generating the primary search result, one or more secondary terms that are correlated to the one or more search terms within documents identified in the primary search result or previously associated (e.g., bookmarked) with the search query. In at least some embodiments, the associative search engine  110  discovers correlated secondary terms using a process that scores secondary terms based on the frequency with which the secondary terms occur within bookmarked documents and documents identified in primary search result. Examples of this scoring process may be based on a term frequency-inverse document frequency process, an Okapi BM25 process, or a similar process. Examples of processes executed by the associative search engine  110  to discover secondary terms are described further below with reference to  FIG. 9 . 
     After discovering one or more secondary terms, various embodiments utilize these secondary terms in a variety of ways. For example, some embodiments are configured to store the secondary terms and to execute a secondary search query including the secondary terms in response to receiving input indicating interest in a particular document identified in the primary search results, such as by actuating a link included in the link area  402 . This secondary search query may include one or more secondary terms as search terms. 
     Other embodiments present the secondary terms within the primary search result to enable an external entity to conduct a secondary search query using the secondary terms as search terms.  FIG. 5  illustrates one example in which the search interface  104  presents a screen  500  with interface elements that provide secondary terms within the primary search result. As shown in  FIG. 5 , the screen  500  includes, in addition to the elements described above with reference to  FIG. 3 , a link area  502  and a secondary term area  504 . The link area  502  includes links to documents included in the primary search result. The secondary term area  504  includes a listing of secondary terms found within one or more of the documents included in the primary search result. In this embodiment, the user  102  may navigate to a particular document by actuating a link associated with the document. Further, the user  102  may initiate a secondary search for one or more secondary terms by entering the secondary terms in the text box  302  or by actuating one or more of the secondary terms listed in the secondary terms area  504 . 
     Still other embodiments execute secondary search queries automatically (e.g., after identifying the secondary terms and without requiring further user input). These embodiments may present, within the primary search results (e.g., as shown in the link area  402 ), navigable links to secondary documents identified within secondary search results generated by execution of a secondary search query specifying the secondary terms as search terms. 
     As further illustrated in  FIG. 1 , the associative search engine  110  is configured to exchange information with the document source system  108  via the network  112 . In some embodiments, the associative search engine  110  uses this information to create and maintain information descriptive of documents available for search. For instance, in at least one embodiment, the associative search engine  110  creates and maintains an index and a document store controlled by the associative search engine  110 . One such embodiment is described further below with reference to  FIG. 2 . 
     The information exchanged between the associative search engine  110  and the document source system  108  may include requests and responses for document content or document metadata (e.g., name, storage location, size, and other document characteristics). To exchange information with the document source system  108 , the associative search engine  110  generates and transmits messages to the document source system  108  that are formatted according to a protocol supported by the document source system  108 . Responsive to receiving a response to a request for document content or document metadata, the associative search engine  110  may store or otherwise process the response to create and maintain document information that is local to the associative search engine  110 . Examples of document source system include content management systems, news and reference article repositories, and email systems. 
     For instance, in at least one embodiment, the document source system  108  is an Outlook/Exchange® email system, available from Microsoft of Redmond, Wash. In this embodiment, the associative search system  100  searches a corpus of documents that includes email and email attachments. Also, in this embodiment, the associative search system  100  provides an interface that locates email and email attachments more effectively than conventional email management systems by executing the associative search processes described herein. 
     Information may flow between the components illustrated in  FIG. 1 , or any of the elements, components and subsystems disclosed herein, using a variety of techniques. Such techniques include, for example, passing the information over a network using standard protocols, such as TCP/IP or HTTP or HTTPS, passing the information between modules in memory and passing the information by writing to a file, database, data store, or some other nonvolatile data storage device, among others. In addition, pointers or other references to information may be transmitted and received in place of, in combination with, or in addition to, copies of the information. Conversely, the information may be exchanged in place of, in combination with, or in addition to, pointers or other references to the information. Other techniques and protocols for communicating information may be used without departing from the scope of the examples and embodiments disclosed herein. 
     Associative Search Engine 
     The associative search engine  110  may be arranged according to a variety of architectures.  FIG. 2  illustrates one such architecture in which the physical and logical components of the associative search engine  110  are configured as a distributed system. The architecture illustrated in  FIG. 2  is provided for example purposes only and embodiments disclosed herein are not limited to the architecture shown in  FIG. 2 . 
     As shown in  FIG. 2 , the associative search engine  110  includes five primary physical elements: a load balancer  202 , nodes  204 ,  206 , and  208 , and a network  210 . The node  204  includes a search interface  212  and a search history data store  220 . The node  206  includes a primary query engine  224 , secondary query engine  226 , and an index  214 . The node  208  includes an index engine  216  and a document data store  218 . The load balancer  202  and each of the nodes  204 ,  206 , and  208  may include one or more computer systems as described below with reference to  FIG. 6 . Further, in some embodiments, each of the nodes  204 ,  206 , and  208  is a cluster implemented using Hadoop®, which is available from the Apache Software Foundation of Forest Hills, Md., or a similar software package. 
     In one embodiment illustrated by  FIG. 2 , the load balancer  202  provides load balancing services to the other components of associative search engine  110 . The nodes  204 ,  206 , and  208  provide computing resources to various processes executed by the associative search engine  100 . For example, each of the nodes  204 ,  206 , and  208  provides data storage and retrieval functionality to locally and remotely executing processes. In addition, the node  204  implements web server functionality in support of the search interface  212 . This web server functionality may serve content using any suitable protocol including, among others, HTTP, SMTP, IMAP, and POP, and any suitable data formatting standard including, among others, HTML, DHTML, XML and MIME. Like the network  112  described above with reference to  FIG. 1 , the network  210  may include any communication network through which member computer systems may exchange data. 
     In some embodiments, the node  204  implements the search interface  212  and the search history data store  220 . The search interface  212  is configured to exchange information with a variety of external entities, such as users or external systems. For example, according to one embodiment, the search interface  212  serves a browser-based user interface to the user  102  that is rendered as the search interface  104  by a web browser executing on the computer system  106 . In other embodiments, the search interface  212  implements a system interface that exchanges information with external systems according to a predefined protocol. For example, in some embodiment, the search interface  212  exchanges data with specialized client programs executing on remote systems, such as the computer system  106 . 
     In one embodiment, the search interface  212  is configured to receive information descriptive of search queries from the search interface  104  and to provide information descriptive of search results to the search interface  104 . This information descriptive of search queries may include search terms included in the search query, ordinal values of search terms within the search query, and indications of which search terms or sequences of search terms are required to be present in (or absent from) a document for the document to be identified as relevant and included in the search results. The search results may include links, identifiers, and locations of documents identified as relevant to a search query (e.g. bookmarked documents previously associated with the search query, documents included in primary search results, and documents included in secondary search results). As described further below, the search results may also include secondary terms from the bookmarked documents and the documents included in primary search results. 
     According to one embodiment illustrated by  FIG. 2 , the search interface  212  is configured to exchange information with the primary query engine  224 . The information exchanged between the search interface  212  and the primary query engine  224  may include query execution requests and query execution responses. To exchange query execution requests and query execution responses with the primary query engine  224 , the search interface  212  generates and transmits messages to the primary query engine  224  that are formatted according to a protocol supported by the primary query engine  224 . In response to receiving a query execution response to a query execution request, the search interface  212  may store or otherwise process the query execution response by providing search results to an external entity, such as the search interface  104 , as described above. Examples of processes executed by the search interface  212  that interoperate with the primary query engine  224  are described further below with reference to  FIG. 7 . 
     In another embodiment illustrated by  FIG. 2 , the search interface  212  is configured to exchange information with the secondary query engine  226 . The information exchanged between the search interface  212  and the secondary query engine  226  may include document harvesting requests and document harvesting responses. In some embodiments, document harvesting requests include information descriptive of one or more seed documents that have been identified as being relevant to a search query (e.g., bookmarked documents or documents referenced in primary search results). In these embodiments, document harvesting responses include search results for search queries executed using terms harvested from the seed documents identified in the corresponding document harvesting request. 
     To exchange document harvesting requests and document harvesting responses with the secondary query engine  226 , the search interface  212  generates and transmits messages to the secondary query engine  226  that are formatted according to a protocol supported by the secondary query engine  226 . In response to receiving a document harvesting response to a document harvesting request, the search interface  212  may store or otherwise process the document harvesting response by providing search results included in the document harvesting response to an external entity, such as the search interface  104 , as described above. Examples of processes executed by the search interface  212  that interoperate with the secondary query engine  226  are described further below with reference to  FIG. 7 . 
     In some embodiments, the primary query engine  224  is configured to process query execution requests. These query execution requests may include information descriptive of one or more search queries, as described above. In at least one embodiment, in response to receiving a query execution request, the primary query engine  224  identifies one or more documents satisfying the query execution request using the index  214 . More particularly, in embodiments where the index  214  is an inverted index, the primary query engine  224  searches the inverted index  214  for documents having characteristics specified by the query execution request. Examples of these characteristics include keywords, phrases, and other portions of documents that fully or partially match search terms included within the query execution request. In response to identifying documents that satisfy the query execution request, the primary query engine  224  retrieves information descriptive of the identified documents from the index  214  and transmits a query execution response including this information to the search interface  212 . Examples of this information include identifiers and locations of the documents. Examples of processes executed by the primary query engine  224  relative to the index  214  are described further below with reference to  FIG. 8 . 
     In various embodiments, the secondary query engine  226  is configured to process document harvesting requests. These document harvesting requests may include information descriptive of one or more seed documents, as described above. In at least one embodiment, in response to receiving a document harvesting request, the secondary query engine  226  identifies secondary terms included within one or more seed documents identified in the document harvesting request. Examples of processes executed by the secondary query engine  226  to identify secondary terms included within seed documents are described further below with reference to  FIG. 9 . After identifying these secondary terms, the secondary query engine  226  executes a secondary search query using these secondary terms. Examples of processes executed by the secondary query engine  226  to execute a secondary search query are described further below with reference to  FIG. 8 . 
     Thus, in some embodiments, the secondary query engine  226  identifies one or more documents from the index  214  using the secondary terms identified from the seed documents. More particularly, in embodiments where the index  214  is an inverted index, the secondary query engine  226  searches the inverted index  214  for documents having characteristics specified by the harvested secondary terms. Examples of these characteristics include keywords, phrases, and other portions of documents that fully or partially match the secondary terms. In response to identifying documents that satisfy the document harvesting request, the secondary query engine  226  retrieves information descriptive of the identified documents from the index  214  and transmits a document harvesting response including this information to the search interface  212 . 
     According to some embodiments, the search interface  212  is configured to store and retrieve information descriptive of the search activities conducted by users in the search history data store  220 . For example, in one embodiment, the search interface  212  stores a detailed audit trail of the search activities conducted by individual, identified users. These audit trails may include an exhaustively complete chronology of each user&#39;s interaction with the associative search engine  110 , or portions thereof. Thus, audit trails may include information descriptive of one or more search queries conducted by the user, manipulation of search results corresponding to the search queries (e.g., results selected and bookmarked by the user, follow-up search queries input by the user, time spent reviewing search results or documents cited in search results, etc.). As described further below with reference to  FIG. 9 , in some embodiments, the search engine  212  analyzes audit trails to enhance the relevance of search results presented to individual users based on inclinations demonstrated within audit trails associated with the user or a group of users. 
     According to at least one embodiment, the search history data store  220  is configured to store information descriptive of search activities conducted by users. This information may include audit trails associated with individual users, as described above. 
     In some embodiments, the node  206  implements the primary query engine  224 , the secondary query engine  226 , and the index  214 . The index  214  stores a variety of information related to documents that are searchable via the associative search engine  110 . Examples of this information include document characteristics such as keywords, phrases and other portions of documents. In some embodiments, the index  214  may organize the full text of each document, or portions thereof, into an inverted index to enable keywords, phrases, and other syntactical constructs to be used to identify documents efficiently. 
     Referring again to  FIG. 2 , the node  208  implements the index engine  216  and the document data store  218 . The index engine  216  is configured to perform a variety of processes that create and maintain (e.g., insert, remove, or modify) information stored in the index  214 . In some embodiments, the index engine  216  maintains the index  214  by accessing and processing data stored in the document data store  218 . 
     In some embodiments, the document data store  218  is configured to retrieve and store information descriptive of documents that are searchable via the associative search engine  110 . For instance, in at least one embodiment, the document data store  218  includes executable components configured to retrieve information from a document source system, such as the document source system described above with reference to  FIG. 1 . Examples of the information retrieved and stored within the document data store  218  include complete copies of the documents and metadata descriptive of the documents (e.g., bibliographic information, versioning information, origin, date of creation, date of last modification, etc.). The complete copies of the documents may include the full text of the document, partitioned portions of the document (e.g., abstract, forwards or other commentary, etc.), and non-textual document components (e.g., images, audio information, attachments to the document, etc.). In at least one embodiment, the document data store  218  includes a repository of email and email attachments that is archived from one or more active email systems. 
     Data stores within the associative search engine  110 , such as the index  214 , the document data store  218 , and the search history data store  220  may take the form of any logical construction capable of storing information on a computer readable medium including flat files, indexed files, hierarchical databases, relational databases or object oriented databases. The data may be modeled using unique and foreign key relationships and indexes. The unique and foreign key relationships and indexes may be established between the various fields and tables to ensure both data integrity and data interchange performance. 
     Embodiments of the associative search system  100  are not limited to the particular configuration illustrated in  FIGS. 1 and 2 . These configurations are included for the purposes of illustration only. It is to be appreciated that various examples and embodiments utilize a variety of hardware components, software components, and combinations of hardware and software components that are configured to perform the processes and functions described herein. In addition, in some embodiments, the hardware components described above may be virtualized. Thus the scope of the embodiments disclosed herein is not limited to a particular set of hardware, software, or a combination thereof. 
     Computer System 
     As discussed above with regard to  FIGS. 1 and 2 , various aspects and functions described herein may be implemented as specialized hardware or software components executing in one or more computer systems. There are many examples of computer systems that are currently in use. These examples include, among others, network appliances, personal computers, workstations, mainframes, networked clients, servers, media servers, application servers, database servers, and web servers. Other examples of computer systems may include mobile computing devices, such as cellular phones and personal digital assistants, and network equipment, such as load balancers, routers, and switches. Further, aspects may be located on a single computer system or may be distributed among a plurality of computer systems connected to one or more communications networks. 
     For example, various aspects, functions, and processes may be distributed among one or more computer systems configured to provide a service to one or more client computers, or to perform an overall task as part of a distributed system. Additionally, aspects may be performed on a client-server or multi-tier system that includes components distributed among one or more server systems that perform various functions. Consequently, embodiments are not limited to executing on any particular system or group of systems. Further, aspects, functions, and processes may be implemented in software, hardware or firmware, or any combination thereof. Thus, aspects, functions, and processes may be implemented within methods, acts, systems, system elements and components using a variety of hardware and software configurations, and examples are not limited to any particular distributed architecture, network, or communication protocol. 
     Referring to  FIG. 6 , there is illustrated a block diagram of a distributed computer system  600 , in which various aspects and functions are practiced. As shown, the distributed computer system  600  includes one or more computer systems that exchange information. More specifically, the distributed computer system  600  includes computer systems  602 ,  604 , and  606 . As shown, the computer systems  602 ,  604 , and  606  are interconnected by, and may exchange data through, a communication network  608 . The network  608  may include any communication network through which computer systems may exchange data. To exchange data using the network  608 , the computer systems  602 ,  604 , and  606  and the network  608  may use various methods, protocols and standards, including, among others, Fibre Channel, Token Ring, Ethernet, Wireless Ethernet, Bluetooth, IP, IPV6, TCP/IP, UDP, DTN, HTTP, FTP, SNMP, SMS, MMS, SS7, JSON, SOAP, CORBA, REST, and Web Services. To ensure data transfer is secure, the computer systems  602 ,  604 , and  606  may transmit data via the network  608  using a variety of security measures including, for example, SSL or VPN technologies. While the distributed computer system  600  illustrates three networked computer systems, the distributed computer system  600  is not so limited and may include any number of computer systems and computing devices, networked using any medium and communication protocol. 
     As illustrated in  FIG. 6 , the computer system  602  includes a processor  610 , a memory  612 , an interconnection element  614 , an interface  616  and data storage element  618 . To implement at least some of the aspects, functions, and processes disclosed herein, the processor  610  performs a series of instructions that result in manipulated data. The processor  610  may be any type of processor, multiprocessor or controller. Example processors may include a commercially available processor such as an Intel Xeon, Itanium, Core, Celeron, or Pentium processor; an AMD Opteron processor; an Apple A4 or A5 processor; a Sun UltraSPARC processor; an IBM Power5+ processor; an IBM mainframe chip; or a quantum computer. The processor  610  is connected to other system components, including one or more memory devices  612 , by the interconnection element  614 . 
     The memory  612  stores programs (e.g., sequences of instructions coded to be executable by the processor  610 ) and data during operation of the computer system  602 . Thus, the memory  612  may be a relatively high performance, volatile, random access memory such as a dynamic random access memory (“DRAM”) or static memory (“SRAM”). However, the memory  612  may include any device for storing data, such as a disk drive or other nonvolatile storage device. Various examples may organize the memory  612  into particularized and, in some cases, unique structures to perform the functions disclosed herein. These data structures may be sized and organized to store values for particular data and types of data. 
     Components of the computer system  602  are coupled by an interconnection element such as the interconnection element  614 . The interconnection element  614  may include any communication coupling between system components such as one or more physical busses in conformance with specialized or standard computing bus technologies such as IDE, SCSI, PCI and InfiniBand. The interconnection element  614  enables communications, including instructions and data, to be exchanged between system components of the computer system  602 . 
     The computer system  602  also includes one or more interface devices  616  such as input devices, output devices and combination input/output devices. Interface devices may receive input or provide output. More particularly, output devices may render information for external presentation. Input devices may accept information from external sources. Examples of interface devices include keyboards, mouse devices, trackballs, microphones, touch screens, printing devices, display screens, speakers, network interface cards, etc. Interface devices allow the computer system  602  to exchange information and to communicate with external entities, such as users and other systems. 
     The data storage element  618  includes a computer readable and writeable nonvolatile, or non-transitory, data storage medium in which instructions are stored that define a program or other object that is executed by the processor  610 . The data storage element  618  also may include information that is recorded, on or in, the medium, and that is processed by the processor  610  during execution of the program. More specifically, the information may be stored in one or more data structures specifically configured to conserve storage space or increase data exchange performance. The instructions may be persistently stored as encoded signals, and the instructions may cause the processor  610  to perform any of the functions described herein. The medium may, for example, be optical disk, magnetic disk or flash memory, among others. In operation, the processor  610  or some other controller causes data to be read from the nonvolatile recording medium into another memory, such as the memory  612 , that allows for faster access to the information by the processor  610  than does the storage medium included in the data storage element  618 . The memory may be located in the data storage element  618  or in the memory  612 , however, the processor  610  manipulates the data within the memory, and then copies the data to the storage medium associated with the data storage element  618  after processing is completed. A variety of components may manage data movement between the storage medium and other memory elements and examples are not limited to particular data management components. Further, examples are not limited to a particular memory system or data storage system. 
     Although the computer system  602  is shown by way of example as one type of computer system upon which various aspects and functions may be practiced, aspects and functions are not limited to being implemented on the computer system  602  as shown in  FIG. 6 . Various aspects and functions may be practiced on one or more computers having a different architectures or components than that shown in  FIG. 6 . For instance, the computer system  602  may include specially programmed, special-purpose hardware, such as an application-specific integrated circuit (“ASIC”) tailored to perform a particular operation disclosed herein. While another example may perform the same function using a grid of several general-purpose computing devices running MAC OS System X with Motorola PowerPC processors and several specialized computing devices running proprietary hardware and operating systems. 
     The computer system  602  may be a computer system including an operating system that manages at least a portion of the hardware elements included in the computer system  602 . In some examples, a processor or controller, such as the processor  610 , executes an operating system. Examples of a particular operating system that may be executed include a Windows-based operating system, such as, Windows NT, Windows 2000 (Windows ME), Windows XP, Windows Vista or Windows 7 operating systems, available from the Microsoft Corporation, a MAC OS System X operating system or an iOS operating system available from Apple Computer, one of many Linux-based operating system distributions, for example, the Enterprise Linux operating system available from Red Hat Inc., a Solaris operating system available from Oracle Corporation, or a UNIX operating systems available from various sources. Many other operating systems may be used, and examples are not limited to any particular operating system. 
     The processor  610  and operating system together define a computer platform for which application programs in high-level programming languages are written. These component applications may be executable, intermediate, bytecode or interpreted code which communicates over a communication network, for example, the Internet, using a communication protocol, for example, TCP/IP. Similarly, aspects may be implemented using an object-oriented programming language, such as .Net, SmallTalk, Java, C++, Ada, C# (C-Sharp), Python, or JavaScript. Other object-oriented programming languages may also be used. Alternatively, functional, scripting, or logical programming languages may be used. 
     Additionally, various aspects and functions may be implemented in a non-programmed environment. For example, documents created in HTML, XML or other formats, when viewed in a window of a browser program, can render aspects of a graphical-user interface or perform other functions. Further, various examples may be implemented as programmed or non-programmed elements, or any combination thereof. For example, a web page may be implemented using HTML while a data object called from within the web page may be written in C++. Thus, the examples are not limited to a specific programming language and any suitable programming language could be used. Accordingly, the functional components disclosed herein may include a wide variety of elements (e.g., specialized hardware, executable code, data structures or objects) that are configured to perform the functions described herein. 
     In some examples, the components disclosed herein may read parameters that affect the functions performed by the components. These parameters may be physically stored in any form of suitable memory including volatile memory (such as RAM) or nonvolatile memory (such as a magnetic hard drive). In addition, the parameters may be logically stored in a propriety data structure (such as a database or file defined by a user mode application) or in a commonly shared data structure (such as an application registry that is defined by an operating system). In addition, some examples provide for both system and user interfaces that allow external entities to modify the parameters and thereby configure the behavior of the components. 
     Associative Search Processes 
     As described above with reference to  FIGS. 1 and 2 , several embodiments perform processes that execute associative searches. In some embodiments, these associative search processes are executed by an associative search system, such as the associative search system  100  described above with reference to  FIG. 1 . One example of such an associative search process is illustrated in  FIG. 7 . According to this example, the associative search process  700  includes acts of receiving a primary search query, executing the primary search query, identifying secondary terms from documents included in a primary search result generated by execution of the search query, executing an associative query, and providing search results. 
     In act  702 , a search interface, such as the search interface  104  described above with reference to  FIG. 1 , of the associative search system receives a primary search query from an external entity, such as the user  102  described above with reference to  FIG. 1 . In one embodiment, the primary search query is received via a screen such as the screen  300  described above with reference to  FIG. 3 . 
     In act  704 , an associative search engine of the associative search system, such as the associative search engine  110  described above with reference to  FIGS. 1 and 2 , executes the primary search query. Example processes performed within the act  704  are described further below with reference to  FIG. 8 . 
     In act  706 , the associative search engine identifies secondary terms within one or more seed documents (e.g., documents identified as including secondary terms that may be discovered). The one or more seed documents processed in the act  706  may be automatically identified by the associative search engine after execution of a search query, such as the search queries executed in the act  704  or act  708 . These seed document may also be identified within information (e.g., input) received from an external entity via the search interface, such as documents selected by the external entity within bookmarks, primary search results, or secondary search results. Example processes performed within the act  706  are described further below with reference to  FIG. 9 . 
     In the act  708 , the associative search engine executes a secondary search query that includes one or more of the secondary terms as search terms. These secondary terms may be automatically identified in the act  706  or may be identified within information (e.g., input) received from an external entity via the search interface. In some embodiments the associative search system executes the secondary search query via the same process used to execute primary search query (e.g., the query execution process described below with reference to  FIG. 8 ). 
     In the act  710 , the associative search engine transmits search results to the search interface for presentation to the external entity. These search results may include results from the primary search query, the secondary search query, and documents previously associated with the search query (e.g. documents previously bookmarked). After completion of the act  710 , the associative search system terminates the associative search process  700 . 
     Processes in accord with the associative search process  700  surface associations between search terms and other syntactical constructs and utilizes these associations to enhance the relevance and utility of search results. 
     As described above with reference to the act  704 , some embodiments perform processes through which the associative search system executes a search query. One example of such a query execution process is illustrated in  FIG. 8 . According to this example, the query execution process  800  includes acts of parsing a search query, identifying documents that match the search query, scoring the relevancy of the documents, and identifying relevant documents. 
     In act  802 , a query engine, such as the primary query engine  224  or the secondary query engine  226 , described above with reference to  FIG. 2 , parses information descriptive of a search query to identify the search terms included within the search query. In some embodiments, the query engine filters stop words from the search terms. Examples of common stop words include articles (e.g., “the,” “an,” and “a,”) prepositions (e.g., “at,” “which,” and “one”) and other words with little power to aid the associative search engine in discriminating between documents relevant to the search query and documents not relevant to the search query. 
     In act  804 , the query engine identifies one or more documents that include one or more of the search terms. In some embodiments, the query engine uses an inverted index, such as the index  214  described above with reference to  FIG. 2 , to identify the one or more documents. 
     In act  806 , the query engine scores the set of documents against the filtered search terms to determine the relevancy of the documents to the search query. The query engine may execute a variety of scoring processes. Examples of the factors accounted for by some of these scoring processes include the presence or absence of particular search terms within the document, the frequency with which particular search terms occur within the document, and how closely the sequence of the search terms is mimicked within the document. Within embodiments directed toward email management systems, the factors accounted for may include the logical location of the search term within an email (e.g., whether the search term is disposed within the subject of an email, the body of an email, an attachment to the email, or elsewhere). 
     In act  808 , the query engine identifies one or more documents associated with scores that are higher than scores of other documents. In some embodiments, the query engine limits the number of documents identified in the act  808  to a total that is less than a predefined, configurable parameter. After completion of the act  808 , the associative search system terminates the query execution process  800 . 
     Processes in accord with the query execution process  800  enable a search engine to identify one or more documents relevant to a search query. 
     As described above with reference to the act  706 , some embodiments perform processes through which the associative search system indentifies secondary terms within one or more documents. One example of such a term identification process is illustrated in  FIG. 9 . According to this example, the term identification process  900  includes acts of identifying secondary terms in each of one or more documents, prioritizing the secondary terms across documents, and recording priority secondary terms for subsequent processing. 
     In act  902 , a secondary query engine, such as the secondary query engine  226  described above with reference to  FIG. 2 , of the associative search engine identifies secondary terms in each document. The secondary query engine may account for a variety of factors when determining a correlation between a search term and a secondary term in a document. Examples of these factors include the frequency with which the secondary term occur within the document, the proximity of secondary term relative to the search term within the document, the parts of speech embodied by the search term and the secondary term, and the proclivity of a user to select the secondary term as recorded in a search history for associated with the user (e.g., the user having previously bookmarked documents in association with the query that prominently include the secondary term). Within embodiments directed toward email management systems, the factors accounted for may include the logical location of the search term within an email (e.g., whether the search term is disposed within the subject of an email, the body of an email, an attachment to the email, or elsewhere). Further, when computing term correlation, various embodiments of the secondary query engine may weigh each of these factors differently. 
     It is to be appreciated that, in some embodiments, similarly of meaning is not a factor used to compute term correlation. However, in other embodiments, similarly of meaning may be a factor used to compute term correlation. For example, the secondary query engine  212  may increase the correlation for secondary terms that the secondary query engine is able to identify within a thesaurus as having a meaning similar to a search term. 
     In act  904 , the secondary query engine prioritizes the secondary terms across the documents. In some embodiments, the secondary query engine limits the number of secondary terms identified and prioritized in the act  904  to a total that is less than a predefined, configurable parameter. In some embodiments, the secondary query engine prioritizes the secondary terms by ranking the secondary terms by correlation and selecting a number of highest ranking terms not to exceed the total specified by the configurable parameter. 
     In act  906 , the secondary query engine records the highest priority secondary terms across the documents for subsequent processing. After recording the highest priority secondary terms across the documents, the secondary query engine terminates the term identification process  900 . 
     Processes in accord with the term identification process  900  enable a computer system to determine associations between terms based on their coincidence within one or more documents or a pre-established association between the terms as recorded in search history. 
     Processes  700 - 900  each depict one particular sequence of acts in a particular embodiment. The acts included in these processes may be performed by, or using, one or more computer systems specially configured as discussed herein. Some acts are optional and, as such, may be omitted in accord with one or more embodiments. Additionally, the order of acts can be altered, or other acts can be added, without departing from the scope of the embodiments described herein. Furthermore, as described above, in at least one embodiment, the acts are performed on particular, specially configured machines, namely an associative search system configured according to the examples and embodiments disclosed herein. 
     Usage Scenarios 
       FIGS. 10 and 11  illustrate particular examples of usage scenarios implemented by the embodiments disclosed herein. For example,  FIG. 10  illustrates a sequence of events in which an associative search system, such as the associative search system  100  described above with reference to  FIG. 1 , executes an associative search without requiring a user, such as the user  102 , to identify seed documents. 
     At  1002 , the user submits a search query (e.g., “eggplant farming”) to a query engine, such as the primary query engine  224  described above with reference to  FIG. 2 . In some examples, this submission initiates processes within a number of system components described above with reference to  FIGS. 1 and 2 . These components include the search interface  104 , the computer system  106 , the network  112 , the associative search engine  110 , and, more particularly, the search interface  212 . 
     At  1004 , the query engine searches the document store for documents relevant to the search query. At  1006 , the query engine gathers primary search results of the search query. These primary search results include one or more seed documents relevant to the search query (e.g., documents which frequently and prominently feature the search term “eggplant farming”). 
     At  1008 , a search interface, such as the search interface  212  described above with reference to  FIG. 2 , provides one or more seed documents to a secondary query engine, such as the secondary query engine  226  described above with reference to  FIG. 2 . At  1010 , the secondary query engine automatically identifies secondary terms from seed documents and searches the document store for documents relevant to the secondary terms. For example, the secondary query engine may identify terms such as “aubergine farming,” “aubergine parasites,” “aubergine pests,” “aubergine diseases,” “aubergine marketing,” “aubergine boycott,” “aubergine news,” and “aubergine infestation,” as occurring frequently enough within the seed documents to warrant additional searching and may execute a secondary search query specifying one or more of these secondary terms as the search terms. 
     At  1012 , the secondary query engine gathers the secondary search results of the secondary search query. These secondary search results include one or more documents relevant to the secondary search query (e.g., documents which frequently and prominently feature one or more of the “aubergine” related secondary terms listed above). At  1014 ,  1016 , and  1018 , the search interface provides the primary search results (e.g., documents relevant to “eggplant farming”), the secondary search results (e.g., document relevant to one or more of the “aubergine” related secondary terms listed above), and any bookmarks associated with the search query to the user. In some examples, this presentation initiates processes within a number of system components described above with reference to  FIGS. 1 and 2 . These components include the network  112 , the computer system  106 , and the search interface  104 . 
       FIG. 11  illustrates a sequence of events in which an associative search system, such as the associative search system  100  described above with reference to  FIG. 1 , executes an associative search in which a user, such as the user  102 , to identifies seed documents. 
     At  1102 , the user submits a search query (e.g., “eggplant farming”) to a query engine, such as the primary query engine  224  described above with reference to  FIG. 2 . In some examples, this submission initiates processes within a number of system components described above with reference to  FIGS. 1 and 2 . These components include the search interface  104 , the computer system  106 , the network  112 , the associative search engine  110 , and, more particularly, the search interface  212 . 
     At  1104 , the query engine searches the document store for documents relevant to the search query. At  1106 , the query engine gathers primary search results of the search query. These primary search results include one or more seed documents relevant to the search query (e.g., documents which frequently and prominently feature the search term “eggplant farming”). At  1108  and  1110 , a search interface, such as the search interface  212  described above with reference to  FIG. 2 , provides the primary search results (e.g., documents relevant to “eggplant farming”) and any bookmarks associated with the search query to the user. In some examples, this presentation initiates processes within a number of system components described above with reference to  FIGS. 1 and 2 . These components include the network  112 , the computer system  106 , and the search interface  104 . 
     At  1112 , the user provides an indication of one or more seed documents to a secondary query engine, such as the secondary query engine  226  described above with reference to  FIG. 2 . In some examples, this provision initiates processes within a number of system components described above with reference to  FIGS. 1 and 2 . These components include the search interface  104 , the computer system  106 , the network  112 , the associative search engine  110 , and, more particularly, the search interface  212 . 
     At  1114 , the secondary query engine automatically identifies secondary terms from the seed documents identified and searches the document store for documents relevant to the secondary terms. For example, the secondary query engine may identify terms such as “aubergine farming,” “aubergine parasites,” “aubergine pests,” “aubergine diseases,” “aubergine marketing,” “aubergine boycott,” “aubergine news,” and “aubergine infestation,” as occurring frequently enough within the seed documents to warrant additional searching and may execute a secondary search query specifying one or more of these secondary terms as the search terms. 
     At  1116 , the secondary query engine gathers the secondary search results of the secondary search query. These secondary search results include one or more documents relevant to the secondary search query (e.g., documents which frequently and prominently feature one or more of the “aubergine” related secondary terms listed above). At  1118  and  1120 , the search interface provides the secondary search results (e.g., document relevant to one or more of the “aubergine” related secondary terms listed above) and any bookmarks associated with the search query to the user. In some examples, this presentation initiates processes within a number of system components described above with reference to  FIGS. 1 and 2 . These components include the network  112 , the computer system  106 , and the search interface  104 . At  1122 , the user submits a refined search query to the query engine. 
     Having thus described several aspects of at least one example, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. For instance, examples disclosed herein may also be used in other contexts. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the scope of the examples discussed herein. Accordingly, the foregoing description and drawings are by way of example only.