Patent Publication Number: US-11640430-B2

Title: Custom semantic search experience driven by an ontology

Description:
BACKGROUND 
     The present invention generally relates to computer systems, and more specifically, to a custom semantic search experience driven by an ontology. 
     Natural language processing (NLP) is concerned with the interactions between computers and human (natural) languages and how computers process and analyze large amounts of natural language data. This natural language data is sometimes referred to as a corpus or corpora. In linguistics, a corpus or text corpus is a language resource consisting of a large and structured set of texts. NLP processing can occur on large corpora resulting in many annotations associated with the corpora. Semantic search of a corpus denotes searching with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them without understanding the overall meaning of the query. Semantic search seeks to improve search accuracy by understanding the searcher&#39;s intent and the contextual meaning of terms as they appear in the searchable dataspace to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching, and natural language queries to provide relevant search results. Some regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies. An ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject. 
     SUMMARY 
     Embodiments of the present invention are directed to a custom semantic search experience driven by an ontology. A non-limiting example computer-implemented method includes updating a semantic search function with a custom ontology, the semantic search function initially supporting a separate ontology having been used to enrich a corpus. The method includes using the custom ontology to augment input of a search query for the semantic search function, thereby providing a custom user experience for searching the corpus. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where the custom ontology is different from the separate ontology. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where the custom ontology is received from a user and is curated independently from the separate ontology. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where using the custom ontology to augment the input of the search query for the semantic search function comprises generating suggestions associated with the input of the search query. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where the custom user experience for searching the corpus comprises generating the suggestions using the custom ontology. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where the custom user experience for searching the corpus comprises generating the suggestions using the custom ontology and the separate ontology. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where the custom user experience for searching the corpus comprises generating the suggestions using the custom ontology while avoiding execution of natural langue processing (NLP) on the corpus with the custom ontology. 
     In addition to one or more of the features described above or below, or as an alternative, further embodiments could include where software is provided as a service in a cloud environment for providing the custom user experience for searching the corpus using the custom ontology to augment the input of the search query. 
     Other embodiments of the present invention implement features of the above-described method in computer systems and computer program products. 
     Additional technical features and benefits are realized through the techniques of the present invention. Embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the embodiments of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG.  1    depicts a block diagram of an example computer system for use in conjunction with one or more embodiments of the present invention; 
         FIG.  2    depicts a block diagram of a system for replacing mappings within a semantic search application over a commonly enriched corpus in accordance with one or more embodiments of the present invention; 
         FIGS.  3 A and  3 B  together depict a flowchart of a process for a custom semantic search experience driven by the user&#39;s ontology which includes replacing mappings within a semantic search application over a commonly enriched corpus in accordance with one or more embodiments of the present invention; 
         FIG.  4    is a flowchart of a process for a custom semantic search experience driven by the user&#39;s ontology over a commonly enriched corpus continuing from, responsive to, and/or concurrent with the process in  FIGS.  3 A and  3 B  in accordance with one or more embodiments of the present invention; 
         FIG.  5    is a flowchart of a computer-implemented method employing a user ontology to support a customized search experience over a corpus that was enriched with a different ontology in accordance with one or more embodiments of the present invention; 
         FIG.  6    is a flowchart of a computer-implemented method for a custom semantic search experience driven by a user ontology in accordance with one or more embodiments of the present invention; 
         FIG.  7    depicts a block diagram of providing custom semantic suggestions from a user ontology concurrent with user input for a search query in accordance with one or more embodiments of the present invention; 
         FIG.  8    depicts a cloud computing environment according to one or more embodiments of the present invention; 
         FIG.  9    depicts abstraction model layers according to one or more embodiments of the present invention; 
         FIG.  10    depicts a system for semantic linkage qualification of ontologically related entities according to one or more embodiments of the present invention; 
         FIG.  11    depicts a block diagram representation of a parse tree for an exemplary passage according to one or more embodiments of the invention; and 
         FIG.  12    depicts a flow diagram of a method for semantic linkage qualification of ontologically related entities according to one or more embodiments of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     One or more embodiments of the present invention provide a technique of employing a user ontology for use in aiding a customized search experience over a corpus that was previously enriched with a different ontology. The user ontology is a custom ontology specific to the user as oppose to a public ontology commonly available with the corpus. As an example, one or more embodiments mimic a custom enrichment search experience without the computational cost in terms of processors, memory, time, expense, etc., of constructing and running a custom enrichment of the corpus. 
     Although it is recognized as challenge to provide an enriched corpus that can meet the needs of a wide variety of consumers, this is because custom enrichment of the entire corpus is a costly endeavor which requires rerunning the natural language processing (NLP) processor over the entire corpus. However, one or more embodiments deliver the benefits of a customized semantic search experience using a commonly enriched corpus without requiring the costly endeavor of rerunning the NLP processor over the entire corpus using the custom ontology. As noted herein, one or more embodiments provide the integration of a custom ontology with a semantic search user experience (e.g., typeahead) over a corpus enriched with a separate ontology, thereby affording users/consumers the ability to view a commonly enriched corpus through the lens of the custom ontology of his/her choice. 
     Turning now to  FIG.  1   , a computer system  100  is generally shown in accordance with one or more embodiments of the invention. The computer system  100  can be an electronic, computer framework comprising and/or employing any number and combination of computing devices and networks utilizing various communication technologies, as described herein. The computer system  100  can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others. The computer system  100  may be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer system  100  may be a cloud computing node. Computer system  100  may be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system  100  may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     As shown in  FIG.  1   , the computer system  100  has one or more central processing units (CPU(s))  101   a ,  101   b ,  101   c , etc., (collectively or generically referred to as processor(s)  101 ). The processors  101  can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The processors  101 , also referred to as processing circuits, are coupled via a system bus  102  to a system memory  103  and various other components. The system memory  103  can include a read only memory (ROM)  104  and a random access memory (RAM)  105 . The ROM  104  is coupled to the system bus  102  and may include a basic input/output system (BIOS) or its successors like Unified Extensible Firmware Interface (UEFI), which controls certain basic functions of the computer system  100 . The RAM is read-write memory coupled to the system bus  102  for use by the processors  101 . The system memory  103  provides temporary memory space for operations of said instructions during operation. The system memory  103  can include random access memory (RAM), read only memory, flash memory, or any other suitable memory systems. 
     The computer system  100  comprises an input/output (I/O) adapter  106  and a communications adapter  107  coupled to the system bus  102 . The I/O adapter  106  may be a small computer system interface (SCSI) adapter that communicates with a hard disk  108  and/or any other similar component. The I/O adapter  106  and the hard disk  108  are collectively referred to herein as a mass storage  110 . 
     Software  111  for execution on the computer system  100  may be stored in the mass storage  110 . The mass storage  110  is an example of a tangible storage medium readable by the processors  101 , where the software  111  is stored as instructions for execution by the processors  101  to cause the computer system  100  to operate, such as is described herein below with respect to the various Figures. Examples of computer program product and the execution of such instruction is discussed herein in more detail. The communications adapter  107  interconnects the system bus  102  with a network  112 , which may be an outside network, enabling the computer system  100  to communicate with other such systems. In one embodiment, a portion of the system memory  103  and the mass storage  110  collectively store an operating system, which may be any appropriate operating system to coordinate the functions of the various components shown in  FIG.  1   . 
     Additional input/output devices are shown as connected to the system bus  102  via a display adapter  115  and an interface adapter  116 . In one embodiment, the adapters  106 ,  107 ,  115 , and  116  may be connected to one or more I/O buses that are connected to the system bus  102  via an intermediate bus bridge (not shown). A display  119  (e.g., a screen or a display monitor) is connected to the system bus  102  by the display adapter  115 , which may include a graphics controller to improve the performance of graphics intensive applications and a video controller. A keyboard  121 , a mouse  122 , a speaker  123 , etc., can be interconnected to the system bus  102  via the interface adapter  116 , which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit. Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI) and the Peripheral Component Interconnect Express (PCIe). Thus, as configured in  FIG.  1   , the computer system  100  includes processing capability in the form of the processors  101 , and, storage capability including the system memory  103  and the mass storage  110 , input means such as the keyboard  121  and the mouse  122 , and output capability including the speaker  123  and the display  119 . 
     In some embodiments, the communications adapter  107  can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network  112  may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An external computing device may connect to the computer system  100  through the network  112 . In some examples, an external computing device may be an external webserver or a cloud computing node. 
     It is to be understood that the block diagram of  FIG.  1    is not intended to indicate that the computer system  100  is to include all of the components shown in  FIG.  1   . Rather, the computer system  100  can include any appropriate fewer or additional components not illustrated in  FIG.  1    (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the embodiments described herein with respect to computer system  100  may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various embodiments. 
       FIG.  2    is a block diagram of a system  200  for replacing mappings within a semantic search application over a commonly enriched corpus in accordance with one or more embodiments of the present invention.  FIG.  2    depicts one or more computers systems  202  coupled to computer system  220 . Computer systems  202  can be representative of numerous computers in a datacenter servicing various users. Computer system  220  can be representative of numerous user computers requesting customized access to resources on computer systems  202 . Elements of computer system  100  may be used in and/or integrated into computers system  202  and computer system  220 .  FIGS.  3 A and  3 B  illustrate a flowchart of a process  300  for a custom semantic search experience driven by the user&#39;s ontology which includes replacing mappings within a semantic search application over a commonly enriched corpus in accordance with one or more embodiments of the present invention. Process  300  in  FIGS.  3 A and  3 B  will be described with reference to  FIG.  2   . 
     At block  302 , software application  204  on computer system  202  is configured to receive a request  230  for a customized semantic search from computer system  220 . Computer system  220  is the system for the user, who is may also be referred to as the customer, tenant, etc. Computer system  220  can communicate with computer systems  202  over a wired and/or wireless network. Using computer system  220 , the user can interface directly with software application  204  of computer system  202  and/or use a client application  222  to interface with software application  204 . Software application  204  may be implemented as software  111  executed on one or more processors  101 , as discussed in  FIG.  1   . Similarly, client application  222  may be implemented using software  111  configured to execute on one or more processors  101 . Client application  222  may include cookies, plug-ins, etc., and client application  222  may serve as a piece of computer software that accesses the customized semantic search service for corpus  260  made available by computer system  202 . 
     Corpus  260  on computer system  202  is available to the public for semantic search in which one or more ontologies  240  are used for the semantic search. Corpus  260  has been enriched by one or more natural language processing (NLP) services  212  using one or more ontologies  240 . Corpus  260  includes databases of numerous documents  208  and annotations  210  about those documents  208 . Corpus  260  may contain hundreds, thousands, and/or millions of documents, also referred to as “big data”. In accordance with one or more embodiments, the enormous size of corpus  260  requires management, processing, and search by a machine (such as computer system  202 ), for example, using computer-executable instructions, and corpus  260  could not be practically managed, stored, analyzed, and/or processed as discussed herein within the human mind. For corpus  260 , NLP processing via one or more NLP services  212  using annotators  250  has occurred on documents  208  resulting in annotations  210  associated with the text of documents  208 . NLP services  212  used one or more ontologies  240  to generate annotations  210  thereby enriching corpus  260 . Ontologies  240  represent one or more public ontologies commonly available with corpus  260 . To enrich corpus  260 , NLP services  212  are configured to index the documents  208 , and while using the index of documents  208  along with public ontologies  240 , NLP services  212  are configured to find insights and relationships in the text of documents  208  and output this information as annotations  210  (or metadata) associated with documents  208 . Oftentimes, a semantic search application will provide a public, multi-tenant, enriched corpus including an ontology that maps out all the relationships between the NLP-extracted entities. “Public” with respect to the ontology means provided and available to all tenants. Embodiments of the invention enable different users (i.e., tenants) with the ability to provide their own ontologies, such that the users can each perform semantic searches based on their ontological view of the world (i.e., their own entities and relationships therein). Although software applications  204  can include a semantic search application and are able to perform a semantic search over the NLP enriched corpus  260  using public ontologies  240 , software applications  204  are also configured to perform a customized semantic search using a user ontology  224  in place of and/or in addition to public ontologies  240 . The user ontology  224  is a custom ontology specific/personal to the user of computer system  220  as oppose to the public ontology  240  commonly available with corpus  260 . User ontology  224  was curated independently from the public ontology  240  that was leveraged for the NLP enrichment process. For example, public ontologies  240  may include entities and the relationships between those entities for medial information of general medicine. User ontology  224  may include entities and the relationships between those entities for medical information on particular specializations and disciplines of medicine, such as internal medicine, pediatrics, immunology, cardiology, etc. The request  230  includes the user ontology  224 , and the request  230  may also include a search query concurrently with the user ontology  224  and/or responsive to sending user ontology  224  to computer system  202  such as after computer system  202  prompts the user to input the search query. The request  230  may include a unique identification (ID) such as a numeric ID, alphanumeric ID, a unique name, etc., which uniquely identifies corpus  260  from other corpora on computer systems  202 . Software application  204  is configured to upload user ontology  224  to be associated with corpus  260  identified by the unique identification. As such, a cloned copy of user ontology  224  is stored in memory  206  and shown with dashed lines. Software application  204  may include, be integrated with, and/or call another software application tool to index user ontology  224 , thereby generating user ontology index  226 . The user ontology index  226  is a listing of all text/words (i.e., surface forms) in user ontology  224  along with their associated locations within user ontology  224 . Each user desiring a custom semantic search experience will have his/her own user ontology index  226  correlating to his/her own user ontology  224 . The user ontology index  226  is a database index and/or other search index (i.e., Lucene or elastic search index) which allow for quick look-up by a typeahead search function  232  (including software application  204 ). In one or more embodiments, the ontology index  226  can be a Lucene-style index that is searched using a Lucene-style query. Lucene is an inverted full-text index. This means that it takes all the documents, splits them into words, and then builds an index for each word. Since the index is an exact string-match, the query can be very fast. 
     At block  304 , software application  204  on computer system  202  is configured to update the typeahead search function  232  with user ontology  224 , particularly user ontology index  226 . The typeahead search function  232  is updated to use ontology index  226  in place of and/or in addition to an index  241  of public ontologies  240 . Software application  204  may include, be integrated with, and/or call typeahead search function/application  232 . Typeahead search or simply typeahead, which is also known as autocomplete or autosuggest, is a language prediction tool used to predict and provide suggestions for users as they type in a search query using, for example, a search index such as user ontology search index  226 . By updating the typeahead search function  232  with user ontology  224  particularly the ontology index  226 , as the user types his/her search query, software application  204  is configured to autocomplete and autosuggest terms and/or phrases based on entities and relationships in user ontology  224  in place of entities and relationships in public ontologies  240  and/or in addition to public ontologies  240 . The suggested terms and/or phrases will be specific and unique to user ontology  224  each time the user begins entering a search query to search enriched public corpus  260  on computer system  202 . In one or more embodiments,  FIG.  7    depicts a block diagram of software application  204  providing custom semantic suggestions from user ontology  224  concurrent with user input for a search query, such that the suggestions are displayed/rendered for display to the user as he/she types in the search query. In one or more embodiments, custom semantic suggestions are provided from user ontology  224 , along with non-custom semantic suggestions from public ontologies  240  having been used to enrich corpus  260 ; the non-custom semantic suggestions from public ontologies  240  would have been used exclusively if the custom semantic search experience were not supported. 
     Returning to  FIGS.  3 A and  3 B , at block  306 , software application  204  on computer system  202  is configured to check whether any entities and relationships in user ontology  224  are congruent with entities and relationships in public ontologies  240 , as part of mapping. In one or more embodiments, software application  204  is configured to find and identify entities and relationships in user ontology  224  which match entities and relationships in public ontologies  240 . In one or more embodiments, congruent entities and relationships in user ontology  224  and public ontologies  240  can be found by using various techniques. To find congruent entities, common techniques to identify the degree of text similarity between words and phrases may be employed by software application  204 , such as cosine similarity, Euclidean distance, Jaccard distance, word movers distance, etc., over a vector representation of the text (e.g., word embedding model). The entities being evaluated for similarity are the entities between the public/provided ontology and the user-provided ontology. Some entities can be mapped between the two ontologies (i.e., the same concept exists in both), but some entities in the user ontology may not be represented in the public/provided ontology, thereby requiring additional entity detection to be performed in that instance based on the seed entity name(s) in the user ontology and common concept expansion techniques performed therein to identify word variations of this new entity that are not represented in the public/provided ontology or the supported NLP annotators behind the public/provided ontology. With regard to finding congruent relations (similar to finding congruent entities), relations in the public/provided ontology are mapped to the user ontology where applicable, but in cases where relations in the user-provided ontology are not represented in the public/provided ontology, additional relation detection may be used to support these new user-provided relations employed for semantic search over a corpus. To detect new relations expressed in the user-provided ontology, relation names are first broken down into valid words or tokens, for example, “mayTreat” is split into “may treat” (2 words/tokens). This type of pre-processing is performed as necessary to arrive at a natural language phrase that can be used to evaluate against intervening parse tree nodes between co-occurring entities as detailed in  FIG.  11    (herein). The ontology informs the software application  204  as to which entities are eligible/applicable for a given relation, so that software application  204  is not blindly matching every co-occurring entity against every possible relationship, rather just the eligible candidate relations based on the co-occurring entities that have an expressed relationship within the ontology. For each of the matches found between entities and relationships in both user ontology  224  and public ontologies  240 , software application  204  is configured to link/map these related entities and relationships in mapping  246  at block  308 . 
     Once software application  204  determines that no more entities and/or relationships in user ontology  224  are congruent with entities and relationships in public ontologies  240 , software application  204  on computer system  202  is configured to identify new entities and/or relationships in user ontology  224 , which are in need of detection in and/or which are not represented by existing entities and/or relationships in public ontologies  240  at block  310 . To find the new entities in user ontology  224  which were not previously matched/congruent to existing entities in public ontologies  240 , software application  204  on computer system  202  is configured to generate synonymous terms and phrases in both the entities in user ontology  224  and the entities in public ontologies  240  at block  312 . At block  314 , software application  204  on computer system  202  is configured to identify matches/congruences between new entities in user ontology  224  and existing entities in public ontologies  240  using, for example, synonymous terms and phrases for entities in user ontology  224  and synonymous terms and phrases for entities in public ontologies  240 . Once the matches and/or congruences are found between entities in user ontology  224  and public ontology  240 , software application  204  is configured to link/map (new) entities in user ontology  224  to existing entities in public ontology  240  in mapping  246 . Software application  204  can include, use, and/or call a combination of various software application tools to identify and find matches and/or congruences between entities in user ontology  224  and public ontology  240 . 
     For example, software application  204  may include functionality of and/or use one or more software application tools (such as, e.g., WordNet®) having lexical databases of semantic relations between words. The software application tool links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms can be grouped into synsets with short definitions and usage examples. The software application tool can be a combination and extension of a dictionary and thesaurus. The software application tool can use automatic text analysis and artificial intelligence. Additionally, software application  204  may include functionality of and/or use one or more software application tools for word embedding. Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. Word embedding may involve mathematical embedding from a space with many dimensions per word to a continuous vector space with a much lower dimension. Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear. 
     At block  316 , to find the new relationships in user ontology  224  which were not previously matched/congruent to existing relationships in public ontologies  240 , software application  204  on computer system  202  is configured to inspect each of the new relationships versus the existing relationships using parse tree analysis, predicate frames, etc., in addition to using the software application tools discussed above for lexical databases of semantic relations between words and word embedding. At block  318 , software application  204  on computer system  202  is configured to identify matches/congruences between the new relationships of user ontology  224  and existing relationships of public ontologies  240  and link/map the matched/congruent (new) relationships in user ontology  224  to existing relationships in public ontology  240  in mapping  246 . In addition to employing user ontology index  226 , the typeahead search function  232  is updated with and/or linked to mapping  246  to take advantage of the matches/congruences in entities and relationships between user ontology  224  and entities and relationships in public ontologies  240 . Since the links and connections in mapping  246  are associated with terms (i.e., entities and relationships) of user ontology  224 , this allows a seamless customized search experience over corpus  260  for the user of computer system  220  based on his/her own user ontology  224  in place of and/or in addition to public ontology  240 . Further, software application  204  can utilize one or more portions of annotations  210  when performing blocks  312 ,  314 ,  316 , and  318 . According to one or more embodiments, all or part of one or more processes in blocks  312 ,  314 ,  316 , and  318  may be performed using any part of the examples discussed in  FIGS.  10 - 12    below in order to find matches/congruences between entities and/or relationships in user ontology  224  and public ontology  240  for block  310  (e.g., for the new entities and/or relationships in user ontology  224  which are in need of detection in and/or which are not (initially) found to be represented by existing entities and/or relationships in public ontology  240 ). 
       FIG.  4    is a flowchart of a process  400  for a custom semantic search experience driven by the user&#39;s ontology over a commonly enriched corpus  260  which continues from, is responsive to, and/or concurrent with process  300  discussed in  FIGS.  3 A and  3 B  in accordance with one or more embodiments of the present invention. Although not explicitly shown in  FIG.  4   , one or more blocks in process  400  of  FIG.  4    can be simultaneously and/or nearly simultaneously processed with one or more blocks in  FIGS.  3 A and  3 B . At block  402 , software application  204  is configured to receive request  230  which can further include a search query (such as the search query depicted in  FIG.  7   ) in addition to and/or after receiving other information such as user ontology  224  discussed herein, where the request  230  is for custom semantic search experience to search public corpus  260  using user ontology  224 . The corpus  260  has been enriched by a separate public ontology  240  different from user ontology  224 . As text of the search query is being entered by the user of computer system  220 , for example, using client application  222  coupled to software application  204  and/or directly using software application  204 , software application  204  using typeahead search function  232  is configured to suggest terms and phrases to the user in accordance with user ontology  224  and user ontology index  226  at block  404 . For example,  FIG.  7    illustrates that software application  204  can display custom semantic suggestions from user ontology  224  to the user solely and/or along with non-custom semantic suggestions from public ontologies  240 . At block  406 , software application  204  is configured to generate search results  242  from corpus  260  based on the user input search query in request  230 . As depicted in  FIG.  7   , software application  204  can utilize mapping  246  to map/link search terms in the user search query corresponding to user ontology  224  back to public ontologies  240  when searching corpus  260 , and/or software application  204  can search for one or more search terms of user search query in corpus  260  without mapping back to public ontologies  240 . The search results  242  from the semantic search of corpus  260  are displayed/rendered to the user and transmitted from computer system  202  to the user on computer system  220 . 
     As technical advantages and benefits, one or more embodiments mimic a custom enrichment search experience without the computational cost (in terms of processors, memory, time, expense, etc.) of constructing and running a custom enrichment of the corpus which would include rerunning the NLP service/NLP processor over the entire corpus. Therefore, one or more embodiments offer a customized semantic search experience using the commonly enriched corpus  260  by integrating the user (custom) ontology  224  with a semantic search user experience (e.g., typeahead search function  232 ) over corpus  260  having been previously enriched with the separate public ontology  240 , thereby affording users/consumers the ability to view a commonly enriched corpus through the lens of the custom ontology of his/her choice. 
     Further technical advantages and benefits allow multiple users to each apply their own ontologies (e.g., although one user ontology  224  for a particular user is illustrated in  FIG.  2   , user ontology  224  is representative of numerous custom ontologies for respective users in which each user can individually apply his/her own ontology to the corpus  260  as discussed herein) to a public corpus  260  enriched by the common public ontology  240  for the purposes of users being able to construct their own semantic search queries based on the constructs they have defined in their own ontology. One or more embodiments allow multiple users to apply one or more of their own ontologies for the purpose of semantically searching a public corpus through their point of view (ontology), thereby avoiding and not requiring a custom enriched corpus per ontology, which would be prohibitively computational expensive in terms of processors, memory, bandwidth, etc., and time consuming. By supporting multiple custom ontologies over the public shared corpus  260 , system  200  is configured to individually customize the semantic search experience for each customer. In system  200 , custom user ontologies are explicit, thereby being defined within the user ontology itself rather than a query that mimics the association. One or more embodiments provide the ability to customize the entities as well as the associations (relations/relationships) between those entities, again explicitly through a custom ontology itself rather than mimicking the behavior via a query. 
       FIG.  5    is a flowchart of a computer-implemented method  500  employing a user ontology for use in aiding a customized search experience over a corpus that was enriched with a different ontology in accordance with one or more embodiments of the present invention. At block  502 , software application  204  is configured to integrate a custom ontology (e.g., user ontology  224 ) into a semantic search function (e.g., typeahead search function  232 ), the semantic search function being configured to perform a semantic search over a corpus  260  enriched with a separate ontology  240 . At block  504 , software application  204  is configured to execute the semantic search function using the custom ontology (e.g., user ontology  224 ) to perform the semantic search of the corpus  260 . For example, software application  204  is configured to parse corpus  260  and semantically search for terms in the search query from the user while using user ontology  224 , without requiring additional NLP processing by NLP services  212  with user ontology  224 . At block  506 , software application  204  is configured to provide/generate search results  242  from the semantic search of the corpus  260  based on user input (e.g., from computer system  220 ) received by the semantic search function on computer system  202 . 
     The semantic search function uses a typeahead search function  232  associated with the custom ontology (e.g., user ontology  224 ). The semantic search function uses a typeahead search function  232  to generate and display suggestions based on the custom ontology (e.g., user ontology  224 ) as an alternative to the separate ontology  240 . The semantic search function uses a typeahead search function  232  to generate and display suggestions based on the custom ontology (e.g., user ontology  224 ) in addition to the separate ontology  240 . 
     The separate ontology  240  is used to explicitly enrich the corpus  260 . Software application  204  is configured to index the custom ontology (e.g., user ontology  224 ). The semantic search function uses the user ontology index  226  of the custom ontology (e.g., user ontology  224 ) to generate suggestions for a user entering the input (via computer system  220  into computer system  202 ) as a search query. Integrating the custom ontology (e.g., user ontology  224 ) into the semantic search function (e.g., typeahead search function  232 ) comprises determining congruences (which are linked/mapped in mapping  246 ) between entities and relationships in the custom ontology and the separate ontology (e.g., public ontology  240 ), the semantic search function (e.g., typeahead search function  232 ) employing the congruences (via mapping  246 ) to support the input received by the semantic search function. The integrating and the executing enable unilaterally provisioning computing capabilities for providing a customized search experience over the corpus  260  that was enriched with the separate ontology  240  different from the custom ontology (e.g., user ontology  224 ). 
       FIG.  6    is a flowchart of a computer-implemented method  600  a custom semantic search experience driven by an ontology in accordance with one or more embodiments of the present invention. At block  602 , software application  204  is configured to update a semantic search function (e.g., typeahead search function  232 ) with a custom ontology (e.g., user ontology  224 ), the semantic search function initially supporting a separate ontology (e.g., public ontology  240 ) having been used to enrich a corpus  260 . At block  604 , software application  204  is configured to use the custom ontology (e.g., user ontology  224 ) to augment input of a search query for the semantic search function, thereby providing a custom user experience for searching the corpus  260 . 
     The custom ontology (e.g., user ontology  224 ) is different from the separate ontology (e.g., public ontology  240 ). The custom ontology is received by computer system  202  from a user using computer system  220  and is curated independently from the separate ontology. Using the custom ontology to augment the input of the search query for the semantic search function comprises generating suggestions associated with the input of the search query. The custom user experience for searching the corpus  260  includes generating the suggestions using the custom ontology (e.g., custom semantic suggestions using user ontology  224 ). The custom user experience for searching the corpus  260  includes generating the suggestions using the custom ontology (e.g., custom semantic suggestions specific to user ontology  224 ) and the separate ontology (e.g., non-custom semantic suggestions specific to public ontology  240 ). The custom user experience for searching the corpus  260  includes generating the suggestions using the custom ontology while avoiding performing/execution of natural langue processing (NLP) (via NLP services  212 ) on the corpus  260  with the custom ontology (e.g., user ontology  224 ). Software is provided as a service in a cloud environment for providing the custom user experience for searching the corpus  260  using the custom ontology to augment the input of the search query. 
     One or more embodiments of the invention provide a relation annotator that produces relation annotations between co-occurring entities linked within an ontology. This annotator evaluates a passage where two ontologically linked entities co-occur to determine whether there exist any semantic linkages within the passage that are congruent with the relationship expressed within the ontology. That is to say, the surrounding neighborhood within a passage, document, and the like are analyzed to determine whether the ontological relation annotation can be confirmed by the existing words and phrases in the surrounding neighborhood of the co-occurring entities.  FIG.  10    depicts a block diagram of a system for semantic linkage qualification of ontologically related entities according to one or more embodiments of the present invention. It is expected that any new entities and/or relationships in user ontology  224  will be detected and/or found to be represented by existing entities and/or relationships in public ontology  240  as discussed above in  FIG.  3   . Further, to assist with processes performed in blocks  312 ,  314 ,  316 , and  318  such as, for example, when one or more new entities and/or relationships in user ontology  224  may not have been (initially) detected and/or found to be represented by existing entities and/or relationships in public ontology  240 , system  1000  may be utilized as discussed herein. One or more software applications  204  on computer system  202  can be utilized to execute and process functions/processes discussed in  FIGS.  10 - 12    and/or call other software applications to execute and process functions/processes discussed in  FIGS.  10 - 12   . 
     Referring to  FIG.  10   , the system  1000  includes a semantic linkage engine  1002  that is configured and operable to analyze a set of passages  1006  (e.g., the passages  1006  include/correspond to the new entities and/or relationships in user ontology  224  which are in need of detection in and/or which are not (initially) found to be represented by existing entities and/or relationships in public ontology  240 ; also, the set of passages  1006  include/correspond to passages (e.g., existing entities and/or relationships) in public ontology  240 ) and utilize either an existing ontology  1020  or a defined ontology having ontological relationship annotations for existing entities/concepts that are of interest. The ontology  1020  is an ontology different from user ontology  224  and public ontology  240  but accessed by computer system  202 . The ontology  1020  may be stored on and/or coupled to computer system  202 . Computer system  202  may access ontology  1020  over a network such as the Internet and/or an intranet. The semantic linkage engine  1002  is further configured and operable to generate relationship annotations  1012  for co-occurring entities that exists in passages in the set of passages  1006 . These relationship annotations  1012  are generated as a confirmation of the ontological relationship annotation from the ontology  1020  after a semantic analysis is performed on the passage to determine a congruency score between the ontological relationship and the other words, phrases, entities, and concepts found in the passage. In one or more embodiments of the invention, the passages described herein are natural language text and can vary in size and subject matter. For ease of description, the subject matter will be described herein for usage in the medical field, but this is not intended to limit the scope of the present invention to this field. 
     In one or more embodiments of the invention, pre-processing of the set of passages  1006  can occur prior to analysis by the semantic linkage engine  1002  utilizing a dictionary  1018  or set of dictionaries. This pre-processing can include, but is not limited to, entity detection which can identify and define entities/concepts that exist in the set of passages  1006  that are relevant. The entity detection can be performed utilizing techniques such as machine-learned or rule-based entity detection annotators. The semantic linkage engine  1002  can automatically or through operation by a domain expert identify co-occurring entities having an ontological relation defined by the ontology  1020  that are of interest to the domain expert. For example, co-occurring entities could be a diagnosis and an associated medication with an ontological relation being defined as treatment or prescription. As mentioned before, the pre-processing can perform entity detection to determine passages that have the exemplary co-occurring entities. The other words and phrases in the passage can be analyzed to determine a congruency for these words and phrases in the passage using semantic analysis. Semantic analysis refers to measuring contextual similarity between words and phrases in a passage. The semantic analysis is performed by the semantic linkage engine  1002 . During an analysis of a passage, the semantic linkage engine  1002  can determine a congruency score between an ontological relation and the words and phrases in the passage. This congruency score can be compared to a pre-defined threshold to either confirm or reject the ontological relation taken from the ontology  1020 . If confirmed (i.e., the congruency score exceeds the threshold), the semantic linkage engine  1002  can generate a relation annotation for the co-occurring entities in the passage and apply this relation annotation to the passage. 
     Semantic analysis can include parsing rules.  FIG.  11    depicts a block diagram representation of a parse tree for an exemplary passage according to one or more embodiments of the invention. In the parse tree  1100  there is an exemplary passage that states, “Patient was prescribed cisplatin for treatment of her lung cancer.” The parse tree  1100  parse the exemplary sentence into nodes representing either words or phrases (e.g., patient, lung cancer, etc.). The nodes are in a hierarchical structure and delineated by parts of speech (i.e., verb, noun, prepositional phrase, and determiner). In the exemplary passage, the two co-occurring entities are cisplatin (entity  1 ) and lung cancer (entity  2 ). The ontological relation for these two co-occurring entities can be “treats” and “prescribedfor”. The ontology may store these ontological relations in the following format: &lt;ENTITY1&gt;-&lt;RELATION&gt;-&lt;ENTITY2&gt;. With that, the two co-occurring entities would show as Cisplatin-Treats-Lung Cancer and Cisplatin PrescribedFor-Lung Cancer. Not that PrescribedFor is an ontological relation which can be further broken down into “Prescribed For” or simply “Prescribed.” For the exemplary passage, a semantic analysis can be performed to determine that the ontological relations are congruent with the words and phrases of the passage. This can be performed using a variety of techniques including, but not limited to, any suitable vector formation and clustering technique to represent each training/validation set phrase in vector form and then determine a similarity or grouping of different vectors, such as by using a neural network language model representation techniques (e.g., Word2Vec, Doc2Vec, or similar tool) to convert words and phrases to vectors which are then input to a clustering algorithm to place words and phrases with similar meanings close to each other in a Euclidean space. The intervening nodes of the parse tree  1100  constitute a set of tokens or words that can then be matched against the relation name, which may constitute one or more other words. Relation names such as “mayTreat” can be pre-processed to isolate the unique tokens/words therein. Now we have two sets of text (1. Intervening tokens from co-occurring entities in the passage) and (2. Tokens from the relation name), with which to analyze the degree of congruency or meaning. Techniques such as word movers distance, cosine similarity, and the like can be employed to assess the degree of similarity between the two text excerpts. Furthermore, stop words may be removed to reduce noise and polarity may be explicitly factored in as a penalty to the score—‘polarity’ in the sense that a token or word is negated in one set of text, but not the other (thus, the text may be highly similar, but the presence of the term ‘no’ can drastically change the meaning). 
     In one or more embodiments of the invention, the semantic linkage engine  1002  can be implemented on the processing system  100  found in  FIG.  1   . The processing steps described with reference to the elements of  FIG.  10    can be performed utilizing the processing system  100  in  FIG.  1   . Additionally, the cloud computing system  10  can be in wired or wireless electronic communication with one or all of the elements of the system  1000 . Cloud  50  (discussed below) can supplement, support or replace some or all of the functionality of the elements of the system  1000 . Additionally, some or all of the functionality of the elements of system  1000  can be implemented as a node  10  (shown in  FIGS.  8  and  9   ) of cloud  50 . Cloud computing node  10  is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. 
     In embodiments of the invention, the semantic linkage engine  1002  can also be implemented as so-called classifiers (described in more detail below). In one or more embodiments of the invention, the features of the various engines/classifiers ( 1002 ) described herein can be implemented on the processing system  100  shown in  FIG.  1   , or can be implemented on a neural network (not shown). In embodiments of the invention, the features of the engines/classifiers  1002  can be implemented by configuring and arranging the processing system  100  to execute machine learning (ML) algorithms. In general, ML algorithms, in effect, extract features from received data (e.g., inputs to the engines  1002 ) in order to “classify” the received data. Examples of suitable classifiers include but are not limited to neural networks (described in greater detail below), support vector machines (SVMs), logistic regression, decision trees, hidden Markov Models (HMMs), etc. The end result of the classifier&#39;s operations, i.e., the “classification,” is to predict a class for the data. The ML algorithms apply machine learning techniques to the received data in order to, over time, create/train/update a unique “model.” The learning or training performed by the engines/classifiers  1002  can be supervised, unsupervised, or a hybrid that includes aspects of supervised and unsupervised learning. Supervised learning is when training data is already available and classified/labeled. Unsupervised learning is when training data is not classified/labeled so must be developed through iterations of the classifier. Unsupervised learning can utilize additional learning/training methods including, for example, clustering, anomaly detection, neural networks, deep learning, and the like. 
     In embodiments of the invention where the engines/classifiers  1002  are implemented as neural networks, a resistive switching device (RSD) can be used as a connection (synapse) between a pre-neuron and a post-neuron, thus representing the connection weight in the form of device resistance. Neuromorphic systems are interconnected processor elements that act as simulated “neurons” and exchange “messages” between each other in the form of electronic signals. Similar to the so-called “plasticity” of synaptic neurotransmitter connections that carry messages between biological neurons, the connections in neuromorphic systems such as neural networks carry electronic messages between simulated neurons, which are provided with numeric weights that correspond to the strength or weakness of a given connection. The weights can be adjusted and tuned based on experience, making neuromorphic systems adaptive to inputs and capable of learning. For example, a neuromorphic/neural network for handwriting recognition is defined by a set of input neurons, which can be activated by the pixels of an input image. After being weighted and transformed by a function determined by the network&#39;s designer, the activations of these input neurons are then passed to other downstream neurons, which are often referred to as “hidden” neurons. This process is repeated until an output neuron is activated. Thus, the activated output neuron determines (or “learns”) which character was read. Multiple pre-neurons and post-neurons can be connected through an array of RSD, which naturally expresses a fully-connected neural network. In the descriptions here, any functionality ascribed to the system  1000  can be implemented using the processing system  100  applies. 
     The semantic linkage engine  1002  can perform natural language processing (NLP) analysis techniques on the sets of passages  1006  which are composed of natural language text. NLP is utilized to derive meaning from natural language. The semantic linkage engine  1002  can analyze the set of passages  1006  by parsing, syntactical analysis, morphological analysis, and other processes including statistical modeling and statistical analysis. The type of NLP analysis can vary by language and other considerations. The NLP analysis is utilized to generate a first set of NLP structures and/or features which can be utilized by the semantic linkage engine  1002  to determine congruency between words and phrases in a passage. These NLP structures include a translation and/or interpretation of the natural language input, including synonymous variants thereof. The semantic linkage engine  1002  can analyze the features to determine a context for the features. NLP analysis can be utilized to extract attributes (features) from the natural language. These extracted attributes can be analyzed by the semantic linkage engine  1002  to determine a congruency score and compare this score to a pre-defined threshold to determine whether to generate a relation annotation for the passage being analyzed. 
       FIG.  12    depicts a flow diagram of a method for semantic linkage qualification of ontologically related entities according to one or more embodiments of the invention. The method  1200  includes determining, by a processor, an ontology, the ontology comprising a plurality of ontological relationships, as shown in block  1202 . Determining includes receiving an ontology or creating an ontology that defines ontological relationships between entities of interest to a domain expert. The ontological relationships are chosen to be easily associated with the subject matter of the application of this method. For example, in the medical field, utilizing certain terms or jargon for the defined ontological relationships in the ontology assists with applying it to natural language passages being analyzed. The method  1200 , at block  1204 , includes receiving, by a processor, a plurality of passages. As noted above, the plurality of passages can be natural language text of a given subject matter or can be any natural language text depending on the scope of the ontology. At block  1206  of the method  1200 , the method  1200  includes determining, by the processor, a target set of co-occurring entities comprising a first entity and a second entity. The target co-occurring entities can be determined by a domain expert that is interested in these entities and looking to apply annotations for these entities. Also, at block  1208 , the method  1200  includes determining a first passage in the plurality of passages that includes the first entity and the second entity. The first passage can be a sentence, paragraph, and document based on the application. The method  1200 , at block  1210 , includes determining, from the ontology, a first ontological relationship between the first entity and the second entity. Also, the method  1200 , at block  1212 , includes analyzing the first passage to determine a congruency score for the first ontological relationship. And at block  1214 , the method  1200  includes generating a relationship annotation between the first entity and the second entity in the first passages based on the congruency score being within a threshold. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG.  8   , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described herein above, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG.  8    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  9   , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG.  8   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  9    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and software applications (e.g., software applications  204 , typeahead search functions  232 , and NLP services  212 ) implemented in workloads and functions  96 . Also, software applications can function with and/or be integrated with Resource provisioning  81 . 
     Various embodiments of the invention are described herein with reference to the related drawings. Alternative embodiments of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein. 
     One or more of the methods described herein can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc. 
     For the sake of brevity, conventional techniques related to making and using aspects of the invention may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and/or process details. 
     In some embodiments, various functions or acts can take place at a given location and/or in connection with the operation of one or more apparatuses or systems. In some embodiments, a portion of a given function or act can be performed at a first device or location, and the remainder of the function or act can be performed at one or more additional devices or locations. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated. 
     The diagrams depicted herein are illustrative. There can be many variations to the diagram or the steps (or operations) described therein without departing from the spirit of the disclosure. For instance, the actions can be performed in a differing order or actions can be added, deleted or modified. Also, the term “coupled” describes having a signal path between two elements and does not imply a direct connection between the elements with no intervening elements/connections therebetween. All of these variations are considered a part of the present disclosure. 
     The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus. 
     Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. The terms “at least one” and “one or more” are understood to include any integer number greater than or equal to one, i.e. one, two, three, four, etc. The terms “a plurality” are understood to include any integer number greater than or equal to two, i.e. two, three, four, five, etc. The term “connection” can include both an indirect “connection” and a direct “connection.” 
     The terms “about,” “substantially,” “approximately,” and variations thereof, are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ±8% or 5%, or 2% of a given value. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instruction by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments described herein.