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

Abstract: A computing device and method is usable to determine a cognitive geofence according to a plurality of data points. The computing device may analyze the plurality of data points to determine an emotional state of a user of a user equipment and a cognitive environment in an area proximate to the user of the user equipment. The computing device may determine the cognitive geofence based at least in part on the emotional state of the user of the user equipment and the cognitive environment in the area proximate to the user of the user equipment. After determining the cognitive geofence, the computing device may transmit the determined cognitive geofence to a terminal device.

Abstract: A computing device and method is usable to determine a cognitive geofence according to a plurality of data points. The computing device may analyze the plurality of data points to determine a cognitive dimension associated with each of the data points and a location associated with each of the data points. The computing device may determine the cognitive geofence based at least in part on the determined cognitive dimension and the determined location associated with at least some of the plurality of data points. After determining the cognitive geofence, the computing device may transmit the determined cognitive geofence to a terminal device.

Abstract: A search space can be reduced using a pruned ontology that comprises entities from an initial ontology. A document corpus having a plurality of documents is received for processing. An ontology pruner determines a set of entities in the plurality of documents. For each entity in the set of entities, the ontology pruner determines a link for the entity in the initial ontology. The ontology pruner determines a score for the entity based on information obtained through the link for the entity in the initial ontology. The ontology pruner omits the entity from the pruned ontology if the score is below a predetermined or configurable threshold value.

Abstract: Aspects of the present disclosure, in certain embodiments, are directed toward providing a visual representation of question quality in a question answering system. More particularly, aspects are directed toward receiving, by a question answering system, an input question having a set of query attributes. The set of query attributes may be user-influenced characteristics of the input question. Aspects of the present disclosure are also directed toward evaluating, by comparing the set of query attributes to a set of assessment criteria, the quality of the input question. Aspects of the present disclosure are also directed toward assigning, in response to evaluating the quality of the input question, a set of quality values to the set of query attributes. Using the assigned set of quality values and the set of query attributes, an icon that indicates a visual representation of the quality of the input question may be generated.

Abstract: An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.

Abstract: A computing device and method is usable to determine a cognitive geofence according to a plurality of data points. The computing device may analyze the plurality of data points to determine an emotional state of a user of a user equipment and a cognitive environment in an area proximate to the user of the user equipment. The computing device may determine the cognitive geofence based at least in part on the emotional state of the user of the user equipment and the cognitive environment in the area proximate to the user of the user equipment. After determining the cognitive geofence, the computing device may transmit the determined cognitive geofence to a terminal device.

Abstract: A computing device and method is usable to determine a cognitive geofence according to a plurality of data points. The computing device may analyze the plurality of data points to determine a cognitive dimension associated with each of the data points and a location associated with each of the data points. The computing device may determine the cognitive geofence based at least in part on the determined cognitive dimension and the determined location associated with at least some of the plurality of data points. After determining the cognitive geofence, the computing device may transmit the determined cognitive geofence to a terminal device.

Abstract: An approach is provided for identifying entity relationships based on word classifications extracted from business documents stored in a plurality of corpora. In the approach, performed by an information handling system, a plurality of cluster classifications are identified for the business documents so that entity information from the business documents can be classified or assigned to the cluster classifications, such as by performing natural language processing (NLP) analysis of the business documents. The approach applies semantic analysis to identify and score entity relationships between the entity information classified in the cluster classifications, and based on the scored entity relationships, cluster relationships between the cluster classifications are identified.

Abstract: An approach is provided for identifying entity relationships based on word classifications extracted from business documents stored in a plurality of corpora. In the approach, performed by an information handling system, a plurality of cluster classifications are identified for the business documents so that entity information from the business documents can be classified or assigned to the cluster classifications, such as by performing natural language processing (NLP) analysis of the business documents. The approach applies semantic analysis to identify and score entity relationships between the entity information classified in the cluster classifications, and based on the scored entity relationships, cluster relationships between the cluster classifications are identified.

Abstract: An approach is provided for identifying entity relationships based on word classifications extracted from business documents stored in a plurality of corpora. In the approach, performed by an information handling system, a plurality of cluster classifications are identified for the business documents so that entity information from the business documents can be classified or assigned to the cluster classifications, such as by performing natural language processing (NLP) analysis of the business documents. The approach applies semantic analysis to identify and score entity relationships between the entity information classified in the cluster classifications, and based on the scored entity relationships, cluster relationships between the cluster classifications are identified.

Abstract: A technique for resolving entities provided in a question includes creating respective entity context vectors (ECVs) for respective entities in an applicable knowledge graph (KG). A question is received from a user. A first entity is identified in the question. The first entity is associated with a matching one of the entities in the KG. An ECV for the matching one of the entities in the KG is modified. An answer to the question is generated based on the modified ECV.

Abstract: A computer-implemented method for creating question-answer pairs is provided. The computer-implemented method includes leveraging domain specific resources including, at least one or more of lexicons, glossaries, or knowledge bases for constructing templates for creating the question-answer pairs. The computer implemented method further includes leveraging user experiences of a plurality of users for constructing templates. The computer implemented method further includes eliminating erroneous question-answer pairs based on templates specifications of a heuristic process of the constructed templates.

Abstract: In response to receipt of a process-level input request that is subject to business-level requirements, multiple sets of attributes are identified. The sets of attributes are each from one of multiple informational domains that represent processing factors associated with at least the process-level input request, contemporaneous infrastructure processing capabilities, and historical process performance of similar processes. The multiple sets of attributes from the multiple informational domains are hashed as a vector into an initial process prioritization. The attributes of the hashed vector of the multiple sets of attributes from the multiple informational domains are weighted in the initial process prioritization into a hashed-weighted resulting process prioritization. The process-level input request is assigned to a process category based upon the hashed-weighted resulting process prioritization.

Abstract: A method for adjusting aspects of an application in a user interface is provided. A user profile is received, wherein the user profile corresponds to a first user of a mobile device. Data corresponding to an application is received, wherein the application is compatible with the mobile device. An application is determined to be likely to be accessed by the first user based on the data corresponding to the application. At least one aspect of the application is adjusted on a user interface of the mobile device.

Abstract: A natural language question and answer system analyzes a question to determine key characteristics (such as focus and lexical answer type), and matches those characteristics to business processes from business process repositories. The matching business processes are ranked and at least one is presented as a recommended answer to the user. The system can offer the user a trigger to invoke the particular business process. The analysis includes examining a user profile to determine an attribute relevant to the question, and further includes named entity searching and fuzzy string matching against the business process repositories. Each business process in a repository is designated as either idempotent, non-binding or retrieve-only. The matching can include performing a factorial LDA algorithm on both extracted named entities and latent factors of the business processes in the repositories.

Abstract: In response to receipt of a process-level input request that is subject to business-level requirements, multiple sets of attributes are identified. The sets of attributes are each from one of multiple informational domains that represent processing factors associated with at least the process-level input request, contemporaneous infrastructure processing capabilities, and historical process performance of similar processes. The multiple sets of attributes from the multiple informational domains are hashed as a vector into an initial process prioritization. The attributes of the hashed vector of the multiple sets of attributes from the multiple informational domains are weighted in the initial process prioritization into a hashed-weighted resulting process prioritization. The process-level input request is assigned to a process category based upon the hashed-weighted resulting process prioritization.

Abstract: Aspects of the present disclosure, in certain embodiments, are directed toward providing a visual representation of question quality in a question answering system. More particularly, aspects are directed toward receiving, by a question answering system, an input question having a set of query attributes. The set of query attributes may be user-influenced characteristics of the input question. Aspects of the present disclosure are also directed toward evaluating, by comparing the set of query attributes to a set of assessment criteria, the quality of the input question. Aspects of the present disclosure are also directed toward assigning, in response to evaluating the quality of the input question, a set of quality values to the set of query attributes. Using the assigned set of quality values and the set of query attributes, an icon that indicates a visual representation of the quality of the input question may be generated.

Abstract: A natural language question and answer system analyzes a question to determine key characteristics (such as focus and lexical answer type), and matches those characteristics to business processes from business process repositories. The matching business processes are ranked and at least one is presented as a recommended answer to the user. The system can offer the user a trigger to invoke the particular business process. The analysis includes examining a user profile to determine an attribute relevant to the question, and further includes named entity searching and fuzzy string matching against the business process repositories. Each business process in a repository is designated as either idempotent, non-binding or retrieve-only. The matching can include performing a factorial LDA algorithm on both extracted named entities and latent factors of the business processes in the repositories.

Abstract: An approach is provided for generating actionable content ingestion recommendations based on an interaction history that is mined to extract interaction context parameters from questions and answer results that meet specified answer deficiency criteria by searching one or more content sources using the extracted interaction context parameters to identify new content that is relevant to improving the first answer, and then presenting the new content in an actionable content ingestion recommendation list for display and review by a domain expert, where the actionable content ingestion recommendation fist recommends the new content for ingestion in a knowledge base corpus.