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 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 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: 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 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 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 set of attributes are identified within a received input request of a workflow process. The attributes at least in part represent historical process performance of similar workflow processes. Each of the attributes are weighted into a weighted process prioritization data set based upon the historical workflow process performance. The input request is assigned to a process priority based upon the weighted process prioritization data set.

Abstract: Input data of an input request is received. The input data includes a set of attributes. For each attribute of the set of attributes, a weight is assigned to the attribute based, at least in part, upon historical data relating to workflow performance, to form a respective corresponding weighted attribute. A process priority value is assigned to the input data of the input request based, at least in part, upon on the weighted attributes.

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: 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, such as 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: 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: 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: 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: An approach is provided to receive, at a question answering (QA) system, a question and identify a politeness corresponding to a number of terms corresponding to the question that are included in a corpus of the QA system. The approach identifies the politeness of one or more terms included in each of a set of candidate answers responsive to the question. Finally, the approach scores each of the candidate answers, with the scoring being based, in part, on the politeness identified for each of the terms.

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 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: 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 threshold number of common significant events being returned by the search engine for respective members of an NLQ cluster.