Abstract: Mechanisms for processing a knowledge canvassing request receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Abstract: The temporal stability of an answer from a deep question answering system is predicted using a natural language classifier. A training corpus is divided into time-ordered slices having uniform granularity. A series of candidate answers to a training question is generated based on the slices, and a temporal profile for the series is identified by associating candidate answers with respective temporal intervals. The temporal profile is translated to a temporal stability value (representing a time period) using a temporal stability model. The classifier is trained using such training questions correlated with respective temporal stability values. Thereafter, when a user submits a natural language query to the deep question answering system, the query is also applied to the classifier which determines its temporal stability. The temporal stability is presented to the user with the answer to give a sense of how long the answer can be deemed reliable.

Abstract: An approach is provided in which a knowledge manager identifies a first cohort type and a second cohort type corresponding to an entity included in a question. The knowledge manager determines inferred states to the question by comparing a first set of cohort attributes corresponding to a first cohort type against entity attributes corresponding to the question. In turn, the knowledge manager generates possible answers by comparing the inferred states against a second set of cohort attributes corresponding to a second cohort type.

Abstract: A dialog system receives a multi-intent input from a user, wherein the multi-intent input comprises a selection of multiple intents in a single conversational input. The dialog system splits the multi-intent input into multiple segments, wherein each of the segments comprises a subsequence of the multi-intent input. The dialog system applies a classifier to classify each segment of the multiple segments by at least one pair of a plurality of pairs in a matrix, each pair of a separate class of multiple classes and a separate confidence level of classification, each of the multiple classes associated with a separate intent from among the multiple intents. The dialog system selects one or more outputs for each separate class in each separate pair, in view of the separate confidence level. The dialog system outputs a response comprising a concatenation of the one or more outputs to the user.

Abstract: Embodiments are directed to a computer implemented method of choreographic editing of multimedia and other streams. The method includes receiving, one or more multimedia clips. The multimedia clips are displayed to a user, who can then select one or more of the clips. The user creates one or more synchronization points. An embodiment automatically adjusts the selected multimedia clips using the synchronization points. Thereafter a finished media file is created.

Abstract: A system, computer program product, and method are provided for use with an intelligent computer platform to identify intent and convert the intent to one or more physical actions. The aspect of converting intent includes receiving content, identifying potential variants, and statistically analyzing the variants with a confidence assessment. The variants are sorted based on a protocol associated with the confidence assessment. A variant from the sort is selected and applied to a physical device, which performs a physical action and an associated hardware transformation based on the variant.

Abstract: A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.

Abstract: A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.

Abstract: Embodiments are directed to a computer implemented method of choreographic editing of multimedia and other streams. The method includes receiving, one or more multimedia clips. The multimedia clips are displayed to a user, who can then select one or more of the clips. The user creates one or more synchronization points. An embodiment automatically adjusts the selected multimedia clips using the synchronization points. Thereafter a finished media file is created.

Abstract: Mechanisms are provided for processing a knowledge canvassing request. The mechanisms receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Abstract: Mechanisms are provided for processing a knowledge canvassing request. The mechanisms receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Abstract: An approach is provided in which a knowledge manager identifies a first cohort type and a second cohort type corresponding to an entity included in a question. The knowledge manager determines inferred states to the question by comparing a first set of cohort attributes corresponding to a first cohort type against entity attributes corresponding to the question. In turn, the knowledge manager generates possible answers by comparing the inferred states against a second set of cohort attributes corresponding to a second cohort type.

Abstract: An approach is provided in which a knowledge manager identifies a first cohort type and a second cohort type corresponding to an entity included in a question. The knowledge manager determines inferred states to the question by comparing a first set of cohort attributes corresponding to a first cohort type against entity attributes corresponding to the question. In turn, the knowledge manager generates possible answers by comparing the inferred states against a second set of cohort attributes corresponding to a second cohort type.