Abstract: A method, computer system, and a computer program product for converting a plurality of factoid answers into a plurality of structured relations for storage in a structured knowledge base is provided. The present invention may include receiving a query from a user. The present invention may also include generating a plurality of possible factoid answers. The present invention may then include determining a plurality of confidence scores for the plurality of possible factoid answers. The present invention may then include generating a plurality of certain factoid answers from the plurality of possible factoid answers. The present invention may then include identifying a plurality of special target items. The present invention may further include generating a plurality of structured relations for each certain factoid answer and the identified plurality of special target items. The present include may also include storing the generated plurality of structured relations into the structured knowledge base.

Abstract: Embodiments are directed to a watched questions threshold filtering system that functions to determine and deliver to a user relevant and significant data changes with respect to a user's goals, as defined by a notification threshold value provided by the user. The user is provided with an option to flag one or more queries for automatic re-querying. Confidence scores are processed on new data (i.e., data ingested after the original question was asked) by utilizing a confidence threshold for indicating if the new data warrants alerting a user.

Abstract: Mechanisms are provided for evaluating a temporal relevance of a candidate answer to an input natural language question. An input natural language question is received and analyzed to identify a temporal focus of the input natural language question. A corpus of documents is processed based on the input natural language question to generate candidate answers to the input natural language question, where each candidate answer is processed to identify at least one contextual temporal focus associated with the candidate answer. The at least one contextual temporal focus is compared with the temporal focus of the input natural language question and a measure of temporal relevance of the candidate answer based on results of the comparison is generated. A final answer to the input natural language question is output based on the measure of temporal relevance.

Abstract: Embodiments are directed to a watched questions threshold filtering system that functions to determine and deliver to a user relevant and significant data changes with respect to a user's goals, as defined by a notification threshold value provided by the user. The user is provided with an option to flag one or more queries for automatic re-querying. Confidence scores are processed on new data (i.e., data ingested after the original question was asked) by utilizing a confidence threshold for indicating if the new data warrants alerting a user.

Abstract: A method, computer system, and a computer program product for collecting related factoid answers into a single object is provided. The present invention may include identifying an informativeness criteria. The present invention may also include identifying a query. The present invention may then include receiving a plurality of answer terms. The present invention may then include generating a plurality of informative factoid answers. The present invention may then include identifying a plurality of relation-bearing elements. The present invention may then include grouping the plurality of informative factoid answers into a single object. The present invention may then include generating a plurality of relations from the plurality of informative factoid answers and the plurality of relation-bearing elements. The present invention may further include creating a plurality of knowledge base entries from the plurality of relations.

Abstract: A method, computer system, and a computer program product for converting a plurality of factoid answers into a plurality of structured relations for storage in a structured knowledge base is provided. The present invention may include receiving a query from a user. The present invention may also include generating a plurality of possible factoid answers. The present invention may then include determining a plurality of confidence scores for the plurality of possible factoid answers. The present invention may then include generating a plurality of certain factoid answers from the plurality of possible factoid answers. The present invention may then include identifying a plurality of special target items. The present invention may further include generating a plurality of structured relations for each certain factoid answer and the identified plurality of special target items. The present include may also include storing the generated plurality of structured relations into the structured knowledge base.

Abstract: Large lists of domain-specific terms are classified as a particular kind of linguistic object, e.g., lexical answer type T versus canonical answer E, based on features from a domain-specific corpus which have been found to distinguish between the linguistic objects. The distinguishing features can be identified in the corpus based on sets of the linguistic objects derived from question-and-answer pairs. A classifier can be trained using the distinguishing features, and the classification carried out using that classifier. The distinguishing features can include one or more syntactic features or one or more lexical features. The linguistic objects (the T and E training sets) can be extracted from the question-and-answer pairs automatically via text analysis if manually curated lists are not available. The classified terms can be included in a domain-specific lexicon which facilitates a deep question answering system to yield an answer to a question.

Abstract: Large lists of domain-specific terms are classified as a particular kind of linguistic object, e.g., lexical answer type T versus canonical answer E, based on features from a domain-specific corpus which have been found to distinguish between the linguistic objects. The distinguishing features can be identified in the corpus based on sets of the linguistic objects derived from question-and-answer pairs. A classifier can be trained using the distinguishing features, and the classification carried out using that classifier. The distinguishing features can include one or more syntactic features or one or more lexical features. The linguistic objects (the T and E training sets) can be extracted from the question-and-answer pairs automatically via text analysis if manually curated lists are not available. The classified terms can be included in a domain-specific lexicon which facilitates a deep question answering system to yield an answer to a question.

Abstract: Large lists of domain-specific terms are classified as a particular kind of linguistic object, e.g., lexical answer type T versus canonical answer E, based on features from a domain-specific corpus which have been found to distinguish between the linguistic objects. The distinguishing features can be identified in the corpus based on sets of the linguistic objects derived from question-and-answer pairs. A classifier can be trained using the distinguishing features, and the classification carried out using that classifier. The distinguishing features can include one or more syntactic features or one or more lexical features. The linguistic objects (the T and E training sets) can be extracted from the question-and-answer pairs automatically via text analysis if manually curated lists are not available. The classified terms can be included in a domain-specific lexicon which facilitates a deep question answering system to yield an answer to a question.

Abstract: Large lists of domain-specific terms are classified as a particular kind of linguistic object, e.g., lexical answer type T versus canonical answer E, based on features from a domain-specific corpus which have been found to distinguish between the linguistic objects. The distinguishing features can be identified in the corpus based on sets of the linguistic objects derived from question-and-answer pairs. A classifier can be trained using the distinguishing features, and the classification carried out using that classifier. The distinguishing features can include one or more syntactic features or one or more lexical features. The linguistic objects (the T and E training sets) can be extracted from the question-and-answer pairs automatically via text analysis if manually curated lists are not available. The classified terms can be included in a domain-specific lexicon which facilitates a deep question answering system to yield an answer to a question.

Abstract: A system includes a deep question answering system executed by a computer, a processor, and a memory coupled to the processor. The memory is encoded with instructions that when executed cause the processor to provide a document ingestion system for ingesting user specific documents into the deep question answering system. The document ingestion system is configured to initialize a user specific document collection, the user specific document collection being specific to a user, obtain an indication that the user is interested in a first document based on an interaction between the user and a shallow analytic system, include the first document in the user specific document collection, and ingest the user specific document collection into the deep question answering system.

Abstract: Embodiments are directed to a watched questions threshold filtering system that functions to determine and deliver to a user relevant and significant data changes with respect to a user's goals, as defined by a notification threshold value provided by the user. The user is provided with an option to flag one or more queries for automatic re-querying. Confidence scores are processed on new data (i.e., data ingested after the original question was asked) by utilizing a confidence threshold for indicating if the new data warrants alerting a user.

Abstract: Mechanisms are provided for evaluating a temporal relevance of a portion of content to a cognitive operation request. A cognitive operation request is received that comprises a portion of input text and the input text is analyzed, by a temporal relevance evaluation engine, to identify a temporal focus of the input text. A corpus of content is processed based on the input portion of text to generate candidate results each of which are processed to identify at least one contextual temporal focus associated with the candidate result. The at least one contextual temporal focus is compared with the temporal focus of the input text and a measure of temporal relevance of the candidate result is generated based on results of the comparison. The cognitive operation is performed based on the measure of temporal relevance.

Abstract: Mechanisms are provided for evaluating a temporal relevance of a candidate answer to an input natural language question. An input natural language question is received and analyzed to identify a temporal focus of the input natural language question. A corpus of documents is processed based on the input natural language question to generate candidate answers to the input natural language question, where each candidate answer is processed to identify at least one contextual temporal focus associated with the candidate answer. The at least one contextual temporal focus is compared with the temporal focus of the input natural language question and a measure of temporal relevance of the candidate answer based on results of the comparison is generated. A final answer to the input natural language question is output based on the measure of temporal relevance.

Abstract: Embodiments are directed to a watched questions threshold filtering system that functions to determine and deliver to a user relevant and significant data changes with respect to a user's goals, as defined by a notification threshold value provided by the user. The user is provided with an option to flag one or more queries for automatic re-querying. Confidence scores are processed on new data (i.e., data ingested after the original question was asked) by utilizing a confidence threshold for indicating if the new data warrants alerting a user.

Abstract: A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a question answering system, for answer scoring based on a combined informativity and specificity score. The question answering system, executing on the processor of the computing device and configured with a question answering machine learning model, generates a set of candidate answers for a user-generated input question. For each given candidate answer in the set of candidate answers, an informativity and specificity scorer of the question answering system determines a specificity value of each term in the given candidate answer based on a position of the term in a taxonomy data structure and determining a specificity score of the given candidate answer based on the specificity value of the terms in the given candidate answer.

Abstract: A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a question answering system for answer scoring based on a specificity score. The question answering system, executing on the processor of the computing device and configured with a question answering machine learning model, generates a set of candidate answers for a user-generated input question. For each given candidate answer in the set of candidate answers, a specificity scorer of the question answering system determines a specificity value of each term in the given candidate answer based on a position of the term in a taxonomy data structure and determines a specificity score of the given candidate answer based on the specificity value of the terms in the given candidate answer. The question answering system, determines a confidence score for each candidate answer within the set of candidate answers based on its specificity score.

Abstract: A workload associated with a task is assessed with respect to each of a plurality of computing paradigms offered by a cloud computing environment. Adaptive learning is employed by maintaining a table of Q-values corresponding to the computing paradigms and the workload is distributed according to a ratio of Q-values. The Q-values may be adjusted responsive to a performance metric and/or a value, reward, and/or decay function. The workload is then assigned to available computing paradigms to be performed with improved utilization of resources.

Abstract: An approach that provides hierarchical and index based watermarks represented as trees is described. In one embodiment, a watermark tree is formed from feature watermarks generated from a natural language processing (NLP) stack having NLP analytics. The watermark tree represents a hierarchical relationship between each of the feature watermarks. In particular, the watermark tree defines hierarchical pointers that point out inherited watermarks that exist between the feature watermarks according to the hierarchical relationship. Further, the watermark tree includes a time stamp specifying a time that a data set content residing in a corpus was accessed.

Abstract: An approach that provides hierarchical and index based watermarks represented as trees is described. In one embodiment, a watermark tree is formed from feature watermarks generated from a natural language processing (NLP) stack having NLP analytics. The watermark tree represents a hierarchical relationship between each of the feature watermarks. In particular, the watermark tree defines hierarchical pointers that point out inherited watermarks that exist between the feature watermarks according to the hierarchical relationship. Further, the watermark tree includes a time stamp specifying a time that a data set content residing in a corpus was accessed.