Abstract: In an approach to personalizing a news feed, a computing device monitors a user accessing content. The computing device determines a personal knowledge graph for the user based on the accessed content. Responsive to receiving a new piece of content that the user has not accessed, the computing device determines a novelty score for the new piece of content based on the personal knowledge graph. The computing device filters the new piece of content based on the novelty score.

Abstract: A technique for instance-specific feature-based cross-document sentiment aggregation includes analyzing input data to determine an entity referenced in the input data. One or more salient features of the entity are identified. Multiple documents that reference the entity are located. The salient features in each of the multiple documents are identified. Respective sentiment values are assigned to each of the salient features identified in the multiple documents. Respective sub-graphs are created for each of the multiple documents. The sub-graphs specify the sentiment values assigned to each of the salient features identified in an associated one of the multiple documents. The assigned sentiment values for each of the salient features are aggregated based on the sub-graphs. Finally, output data that is associated with the input data is generated. The output data provides an indication of the aggregated sentiment value for each of the salient features.

Abstract: An approach is provided for automatically generating user-specific interaction modes for processing question and answers at the information handling system by receiving a question from a user, extracting user context parameters identifying a usage scenario for the user, identifying first input and output presentation modes for the user based on the extracted user context parameters, monitoring user interaction with the system in relation to the question, and adjusting the first input and output presentation modes based on the extracted user context parameters and detected user interaction with the system.

Abstract: Software that uses machine-logic based algorithms to assess the value of idea descriptions by performing the following operations: (i) receiving an idea description pertaining to an idea; (ii) generating a density score for the idea, wherein the density score is based, at least in part, on one or more publication date densities of a set of works related to the idea description; (iii) generating a novelty score for the idea, wherein the novelty score is based, at least in part, on a comparison of the idea description to the set of works related to the idea description using natural language processing; and (iv) assessing the value of the idea based, at least in part, on the generated density score and the generated novelty score.

Abstract: A question answering system that determines whether a question is off-topic by performing the following steps: (i) receiving, by a question answering system, a set of documents; (ii) identifying topical subset(s) for each document of the set of documents using named entity recognition, where each topical subset relates to a corresponding topic; (iii) assigning a set of topic score(s) for each topical subset using natural language processing, where each topic score relates to a corresponding probability associated with the respective topical subset under a probabilistic language model; and (iv) determining, based, at least in part, on the topic score(s) corresponding to the topical subset(s), whether a question input into the question answering system is off-topic.

Abstract: Mechanisms are provided in a question answering (QA) system comprising a QA system pipeline that analyzes an input question and generates an answer to the input question, for pre-processing the input question. The mechanisms receive an input question and input the input question to a pre-processor flow path having one or more pre-processors. The one or more pre-processors transform the input question into a transformed question by correcting errors in a formulation of the input question that are determined to be detrimental to efficient and accurate processing of the input question by a QA system pipeline of the QA system. The transformed question is then input to the QA system pipeline of the QA system which processes the transformed question to generate and output an answer to the input question.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a natural language processing (NLP) system, for identifying content relationship for content copied by a content identification mechanism. The content identification mechanism identifies content from a website and then identifies relationship content information associated with a current web page where the content is found. The content identification mechanism modifies a file structure associated with the content with the relationship content information. The content identification mechanism identifies one or more classification identifiers in order to classify the content. Finally, the content identification mechanism transmits the content and the file structure to a specific corpus based on the one or more classification identifiers.

Abstract: In an approach to personalizing a news feed, a computing device monitors a user accessing content. The computing device determines a personal knowledge graph for the user based on the accessed content. Responsive to receiving a new piece of content that the user has not accessed, the computing device determines a novelty score for the new piece of content based on the personal knowledge graph. The computing device filters the new piece of content based on the novelty score.

Abstract: In an approach to personalizing a news feed, a computing device monitors a user accessing content. The computing device determines a personal knowledge graph for the user based on the accessed content. Responsive to receiving a new piece of content that the user has not accessed, the computing device determines a novelty score for the new piece of content based on the personal knowledge graph. The computing device filters the new piece of content based on the novelty score.

Abstract: Mechanisms are provided to implement a cognitive system collaboration identification and recommendation system (CSCIRS). The CSCIRS stores, in a cognitive system database, entries corresponding to a plurality of cognitive system instances. Each entry identifies operational characteristics of a corresponding cognitive system instance. The CSCIRS receives, from a requestor, a request to identify a potential collaborative cognitive system instance and identifies, based on the request and the cognitive system database, a candidate collaborative cognitive system instance. The CSCIRS generates a collaboration recommendation based on the identification and outputs an output corresponding to the collaboration recommendation.

Abstract: A question answering system that determines whether a question is off-topic by performing the following steps: (i) receiving, by a question answering system, a set of documents; (ii) identifying topical subset(s) for each document of the set of documents using named entity recognition, where each topical subset relates to a corresponding topic; (iii) assigning a set of topic score(s) for each topical subset using natural language processing, where each topic score relates to a corresponding probability associated with the respective topical subset under a probabilistic language model; and (iv) determining, based, at least in part, on the topic score(s) corresponding to the topical subset(s), whether a question input into the question answering system is off-topic.

Abstract: An approach is provided for automatically generating user-specific interaction modes for processing question and answers at the information handling system by receiving a question from a user, extracting user context parameters identifying a usage scenario for the user, identifying first input and output presentation modes for the user based on the extracted user context parameters, monitoring user interaction with the system in relation to the question, and adjusting the first input and output presentation modes based on the extracted user context parameters and detected user interaction with the system.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics. Responsive to receiving an input question, a set of candidate answers is identified from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain. A confidence score is associated with each of the candidate answers and each confidence score associated with each candidate answer is refined based on a set of temporal characteristics identified in the input question. A set of temporally refined candidate answers is then provided to the user.

Abstract: An approach is provided for automatically generating user-specific interaction modes for processing question and answers at the information handling system by receiving a question from a user, extracting user context parameters identifying a usage scenario for the user, identifying first input and output presentation modes for the user based on the extracted user context parameters, monitoring user interaction with the system in relation to the question, and adjusting the first input and output presentation modes based on the extracted user context parameters and detected user interaction with the system.

Abstract: An approach is provided for generating supersets of Q/A action paths based on dynamically generated type sets. In the approach, a corpus of knowledge that is used by the QA system is analyzed. The analyzing is performed according to a natural language processing (NLP) of a number of key words that have been found to exceed an expected frequency, such as a frequency found in general language usage. Runtime code is generated by utilizing the key words. The generated runtime code is able to provide answers from the corpus of knowledge, such as by being in a natural language question or in a structured query language expression, with the provided answers being related to one or more predicted questions.

Abstract: An approach is provided for automatically generating user-specific interaction modes for processing question and answers at the information handling system by receiving a question from a user, extracting user context parameters identifying a usage scenario for the user, identifying first input and output presentation modes for the user based on the extracted user context parameters, monitoring user interaction with the system in relation to the question, and adjusting the first input and output presentation modes based on the extracted user context parameters and detected user interaction with the system.

Abstract: A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for utilizing temporal indicators to weight semantic values. A set of temporal characteristics is identified of a set of initial candidate answers. For each initial candidate answer in the set of initial candidate answers: a distance value is generated for each of the set of temporal characteristics of the set of initial candidate answers, a multiplier value is determined with which to weight an initial confidence score associated with the initial candidate answer using the distance value; a sentiment value is determined of the initial candidate answer, and a final weight value is determined using the multiplier value, the sentiment value, and the initial confidence score associated with the initial candidate answer. A set of temporally refined candidate answers is then provided using the determined final weight values.

Abstract: A mechanism is provided in a computing system for interpreting and distinguishing a lack of an answer to an input question. The mechanism receives an input question from a user. The mechanism generates one or more hypotheses based on a corpus of source information. The mechanism injects a no-answer response into the one or more hypotheses to form a plurality of candidate answers. The mechanism ranks the plurality of candidate answers based on evidence scoring and confidence scoring to form a candidate answer ranking. Responsive to determining the no-answer response is the top candidate answer in the candidate answer ranking, the mechanism presents the no-answer response to the user.

Abstract: A computer-implemented method includes identifying an electronically accessible article. A prospective access profile includes context information. The computer-implemented method further includes identifying, based on the context information, a target headline score range. The computer-implemented method further includes extracting an entity relationship graph from the article, identifying core entities, defining a subgraph, and expanding the subgraph by adding related entities, and selecting a headline template based on the subgraph. The computer-implemented method further includes filling the headline template to yield a headline, based on transforming at least one entity of the subgraph to place the headline within the target headline score range. The computer-implemented method further includes presenting the headline to the prospective access profile, monitoring access to the article to yield an access result, and feeding back information to the said prospective access profile.

Abstract: Mechanisms are provided to implement a cognitive system collaboration identification and recommendation system (CSCIRS). The CSCIRS stores, in a cognitive system database, entries corresponding to a plurality of cognitive system instances. Each entry identifies operational characteristics of a corresponding cognitive system instance. The CSCIRS receives, from a requestor, a request to identify a potential collaborative cognitive system instance and identifies, based on the request and the cognitive system database, a candidate collaborative cognitive system instance. The CSCIRS generates a collaboration recommendation based on the identification and outputs an output corresponding to the collaboration recommendation.