Abstract: A system for characterizing content relating to a desired outcome is disclosed. The disclosed system can be configured to identify context included in content collected from various content sources, map the identified context into graph nodes and graph edges connecting the graph nodes, identify one or more features of the identified context and adjust at least one of: a graph node and a graph edge based on the identified one or more features, identify a graph incorporating the graph nodes, the graph edges, and at least one of an adjusted graph node and an adjusted graph edge, and provide a recommendation for at least one action for achieving the desired outcome based on the identified graph.

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 for providing discovery and realization of business measurement concepts may include providing at least one interface configured to receive an input from an operator associated with an organization, determining a selected set of glossary terms from a repository including a plurality of glossaries based at least in part on the input where the glossaries relate to different performance indicator components that are combinable to define a measurable performance indicator, and generating at least one performance indicator of the organization based on the selected set of glossary terms.

Abstract: A system for characterizing content relating to a desired outcome is disclosed. The disclosed system can be configured to identify context included in content collected from various content sources, map the identified context into graph nodes and graph edges connecting the graph nodes, identify one or more features of the identified context and adjust at least one of: a graph node and a graph edge based on the identified one or more features, identify a graph incorporating the graph nodes, the graph edges, and at least one of an adjusted graph node and an adjusted graph edge, and provide a recommendation for at least one action for achieving the desired outcome based on the identified graph.

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: 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: 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: 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: 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 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: An approach is provided for automatically predicting an event occurrence based on a question from an end user presented using a near-real-time natural language processing (NLP) analysis to generate, score and rank a plurality of event occurrences based on a plurality of question context parameters extracted from the question, one or more user profile parameters for the end user, and the one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the question which are identified by an information handling system. In the approach, performed by an information handling system, a top ranked event occurrence from the ranked plurality of event occurrences is selected for inclusion in a notification message that is communicated or broadcast to the end user, as well as other users engaged with the information handling system and/or first responders in the affected area.

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: An approach is provided for automatically predicting an event occurrence based on a question from an end user presented using a near-real-time natural language processing (NLP) analysis to generate, score and rank a plurality of event occurrences based on a plurality of question context parameters extracted from the question, one or more user profile parameters for the end user, and the one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the question which are identified by an information handling system. In the approach, performed by an information handling system, a top ranked event occurrence from the ranked plurality of event occurrences is selected for inclusion in a notification message that is communicated or broadcast to the end user, as well as other users engaged with the information handling system and/or first responders in the affected area.

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: 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 method for providing discovery and realization of business measurement concepts may include providing at least one interface configured to receive an input from an operator associated with an organization, determining a selected set of glossary terms from a repository including a plurality of glossaries based at least in part on the input where the glossaries relate to different performance indicator components that are combinable to define a measurable performance indicator, and generating at least one performance indicator of the organization based on the selected set of glossary terms.

Abstract: A method for providing discovery and realization of business measurement concepts may include providing at least one interface configured to receive an input from an operator associated with an organization, determining a selected set of glossary terms from a repository including a plurality of glossaries based at least in part on the input where the glossaries relate to different performance indicator components that are combinable to define a measurable performance indicator, and generating at least one performance indicator of the organization based on the selected set of glossary terms.

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.

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.