Abstract: Mechanisms are provided for implementing a virtual corpus engine that receives an inquiry to be processed and analyzes the inquiry to extract one or more features of the inquiry. The virtual corpus engine selects a weight matrix associated with a virtual corpus based on the extracted one or more features of the inquiry. The virtual corpus comprises a plurality of actual corpora of information. The weight matrix comprises a separate weight value for each actual corpus in the plurality of actual corpora. The virtual corpus engine processes the inquiry using a set of selected actual corpora selected from the plurality of actual corpora based on the weight values in the weight matrix and receives results of the processing of the inquiry using the set of selected actual corpora. The virtual corpus engine outputs the results of the processing of the inquiry.

Abstract: Clustering a set of natural language queries NLQs based on a set of significant events retrieved from a corpus stored in a computer system is described. A set of NLQs is used by a search engine for searching a selected corpus to retrieve respective sets of significant events. The set of NLQs is clustered into a plurality of NLQ clusters according to a number of common significant events being returned by the search engine for respective members of an NLQ cluster.

Abstract: A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.

Abstract: A method, system and computer-usable medium are disclosed for improving likelihood of user to purchase a product or service. Interactions of a person, not necessarily the user, are monitored related to the product or service, along with actions by a business related to the person and the product or service. A trained Generative Adversarial Network model is applied to the monitored interactions to form recommend actions that the business should take to achieve ameliorative actions by the user. A reward feedback iterative adjustment of the GAN model is used to facilitate purchase of the product or service by the user.

Abstract: An approach is provided in which an information handling system performs multiple tests using a cognitive service and multiple trained machine learning models on user data corresponding to a user application. For each of the multiple tests, a different one of the trained machine learning models is utilized. The information handling system generates results from the tests and then selects at least one of the trained machine learning models based on the test results. In turn, the information handling system assigns the cognitive service and the selected trained machine learning models to the user application.

Abstract: A technique for caching evidence for answering questions in a cache memory of a data processing system (that is configured to answer questions) includes receiving a first question. The first question is analyzed to identify a first set of characteristics of the first question. A first set of evidence for answering the first question is loaded into the cache memory. A second question is received. The second question is analyzed to identify a second set of characteristics of the second question. A portion of the first set of evidence, whose expected usage in answering the second question is below a determined threshold, is unloaded from the cache memory.

Abstract: A first seed concept term may be identified. The first seed concept term may be to train a cognitive computing system. The cognitive computing system may analyze the first seed concept term to generate a first set one or more concept terms that are candidates for being conceptually related to the first seed concept term. A first plurality of individual characters and the first seed concept term may be provided. A first user of a client computing device may be prompted to generate a second set of one or more concept terms that are conceptually related to the first seed concept term using one or more of the first plurality of individual characters.

Abstract: A method, system and computer-usable medium are disclosed for using table-based groundtruth to train a question-answer (QA) system, comprising: receiving a corpus of text comprising unstructured text and a table; performing QA pair generation operations on the unstructured text and the table to generate QA pairs, the QA pairs being based upon information obtained from the table; assigning a score to each QA pair, the score corresponding to an indicator of user interest.

Abstract: A technique for caching evidence for answering questions in a cache memory of a data processing system (that is configured to answer questions) includes receiving a first question. The first question is analyzed to identify a first set of characteristics of the first question. A first set of evidence for answering the first question is loaded into the cache memory. A second question is received. The second question is analyzed to identify a second set of characteristics of the second question. A portion of the first set of evidence, whose expected usage in answering the second question is below a determined threshold, is unloaded from the cache memory.

Abstract: Sentiment scores for a first set of text can be normalized based on a statistical measure of sentiment of a corpus of text authored by a set of authors having respective personality profiles similar to a personality profile of an author of the first set of text. The set of authors can be grouped in a first cluster based on a range of at least one metric of a personality profile. A plurality of respective sentiment scores for portions of the corpus can be generated for the first cluster. A statistical measure of the plurality of respective sentiment scores can be generated. The plurality of respective sentiment scores can be normalized based on the statistical measure. The plurality of respective normalized sentiment scores can be applied to the first set of text to generate one or more sentiment scores for the first set of text.

Abstract: An approach is provided that analyzes electronic document sets, each of the sets written by a different author. The analysis includes performing a normalized sentiment analysis of the documents which results in normalized sentiment scores that pertain to each of the authors. The normalize sentiment scores are stored in a data store that is accessible from a question answering (QA) system. The question answering system then receives a sentiment-based question. Responsively, the QA system generates a qualitative set of candidate answers, with the candidate answers based at least in part on the normalized sentiment scores retrieved from the data store.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for neural activity interpretation. The method, computer program product and computer system may include a computing device that presents a first user with a stimulus and monitors and maps the neural activity of the first user. The computing device may receive the first user's verbal reaction to the stimulus and map the linguistic data of the first user's verbal reaction to form a high dimensional vector based on the relationships of the mapped neural activity and the mapped verbal reaction of the first user. The computing device may associate the high dimensional vector with the stimulus presented resulting in a thoughts model. The computing device may receive a second user's neural activity and compare that second user's neural activity to the thoughts model to identify the neural activity in the second user.

Abstract: A method, system and computer-usable medium are disclosed for improving likelihood of user to purchase a product or service. Interactions of a person, not necessarily the user, are monitored related to the product or service, along with actions by a business related to the person and the product or service. A trained Generative Adversarial Network model is applied to the monitored interactions to form recommend actions that the business should take to achieve ameliorative actions by the user. A reward feedback iterative adjustment of the GAN model is used to facilitate purchase of the product or service by the user.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for neural activity interpretation. The method, computer program product and computer system may include a computing device that presents a first user with a stimulus and monitors and maps the neural activity of the first user. The computing device may receive the first user's verbal reaction to the stimulus and map the linguistic data of the first user's verbal reaction to form a high dimensional vector based on the relationships of the mapped neural activity and the mapped verbal reaction of the first user. The computing device may associate the high dimensional vector with the stimulus presented resulting in a thoughts model. The computing device may receive a second user's neural activity and compare that second user's neural activity to the thoughts model to identify the neural activity in the second user.

Abstract: A knowledge graph is built based on a corpus stored in the computer system. The corpus includes a set of searchable events and each event includes a respective set of entities. A set of entities is identified in a first set of significant events returned by natural language query (NLQ). The knowledge graph determines which ones of the set of entities are related to the entities in the NLQ to produce a filtered set of entities. The filtered set of entities is used to identify a second set of significant events in the selected corpus. Members of the first and second set of significant events are presented to a user as a search result.

Abstract: Clustering a set of natural language queries NLQs based on a set of significant events retrieved from a corpus stored in a computer system is described. A set of NLQs is used by a search engine for searching a selected corpus to retrieve respective sets of significant events. The set of NLQs is clustered into a plurality of NLQ clusters according to a number of common significant events being returned by the search engine for respective members of an NLQ cluster.

Abstract: A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.

Abstract: Clustering a set of natural language queries NLQs based on a set of significant events retrieved from a corpus stored in a computer system is described. A set of NLQs is used by a search engine for searching a selected corpus to retrieve respective sets of significant events. The set of NLQs is clustered into a plurality of NLQ clusters according to a number of common significant events being returned by the search engine for respective members of an NLQ cluster.

Abstract: Resource provisioning information links to resource provisioning information of at least one reusable component resource that satisfies at least a portion of user-specified resource development constraints of a new resource under development are identified within a resource provisioning-link registry. Using the identified resource provisioning information links, the resource provisioning information of the at least one reusable component resource is programmatically collected from at least one data provider repository that stores reusable resources and that publishes the resource provisioning information links to the resource provisioning-link registry. The programmatically-collected resource provisioning information of the at least one reusable component resource is analyzed.

Abstract: Resource provisioning information links to resource provisioning information of at least one reusable component resource that satisfies at least a portion of user-specified resource development constraints of a new resource under development are identified within a resource provisioning-link registry. Using the identified resource provisioning information links, the resource provisioning information of the at least one reusable component resource is programmatically collected from at least one data provider repository that stores reusable resources and that publishes the resource provisioning information links to the resource provisioning-link registry. The programmatically-collected resource provisioning information of the at least one reusable component resource is analyzed.