Abstract: Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising receiving a question in a target language; determining the question cannot be answered using a target-language only corpus; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: An approach is provided in which a knowledge manager analyzes parent/child hierarchies between semi-structured items included in a semi-structured resource. The knowledge manager generates synthetic events based upon the parent/child hierarchy analysis and creates a structured resource utilizing the synthetic events. In one embodiment, the structured resource includes structured resource entries, which include a first semi-structured item, a second semi-structured item, and one of the synthetic events. In turn, the knowledge manager queries the structured resource based upon a question received from a user interface.

Abstract: A mechanism is provided in a data processing system configured with a natural language processing pipeline executing on at least one processor of the data processing system, for class narrowing for answer lookups. The natural language processing pipeline annotates an input question with a verb in the input question and a syntactic relation between the verb and a focus of the question. A class narrowing component of the natural language processing pipeline finds an entry in a verb/type lookup resource corresponding to the verb and the syntactic relation. The entry comprises the verb, the syntactic relation, and a set of classes. Each given class in the set of classes has a corresponding frequency value representing a frequency the given class occurs with the syntactic relation to the verb. The class narrowing component performs class-narrowed type-restricted answer lookup from a topology based on the entry in the verb/type lookup resource to generate a set of candidate answers.

Abstract: Mechanisms are provided for implementing a proximity based candidate answer pre-processor engine that outputs a sub-set of candidate answers to a question and answer (QA) system. The mechanisms receive a lexical answer type (LAT) and an entity specified in an input natural language question as well as an ontology data structure representing a corpus of natural language content. The mechanisms identify a set of candidate answers having associated nodes in the ontology data structure that are within a predetermined proximity of a node corresponding to the entity, and a sub-set of candidate answers in the set of candidate answers having an entity type corresponding to the LAT. The mechanisms output, to the QA system, the sub-set of candidate answers as candidate answers to the input natural language question for evaluation and selection of a final answer to the input natural language question.

Abstract: A method, executed by a computer, includes receiving a triple comprising a predicate, a first entity and an un-typed second entity, scanning a collection of triples for triples that match the predicate of the triple to produce similar triples, determining frequency information for second entity types referenced by the similar triples, and processing the un-typed second entity using the frequency information. A computer system and computer program product corresponding to the method are also disclosed herein.

Abstract: A method, executed by a computer, includes determining within a collection of triples, triples that have a common first entity type and a common predicate to produce similar triples, determining similar triples that have a particular second entity type to produce matching triples, counting the similar triples and the matching triples to produce a similar triple count and a matching triple count, computing probability information based on the matching triple count and the similar triple count, and processing data based on the probability information. A computer system and computer program product corresponding to the method are also disclosed herein.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: An approach is provided in which a knowledge manager identifies training entities in a training question answer pair and identifies multiple database paths a database that link the training entities. The knowledge manager then generates multiple generalized queries based upon the identified multiple database paths and assigns query scores to the multiple generalized queries. In addition, the knowledge manager generates a generalized syntactic pattern based upon syntactic relationships between the training entities in the training question answer pair. When the knowledge manager receives a question and matches the syntactic pattern of the received question to the generalized syntactic pattern, the knowledge manager generates custom queries from the generalized queries and queries the database using the custom queries. The knowledge manager, in turn, scores answers generated from the custom queries based upon the custom queries' corresponding query scores.

Abstract: An approach is provided in which a knowledge manager identifies training entities in a training question answer pair and identifies database paths a database that link the training entities. The knowledge manager then generates a generalized query based upon the identified database paths. In addition, the knowledge manager generates a generalized syntactic pattern based upon syntactic relationships between the training entities in the training question answer pair. When the knowledge manager receives a question and matches the syntactic pattern of the received question to the generalized syntactic pattern, the knowledge manager generates a custom query from the generalized query and queries the database using the custom query.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: An approach is provided in which a knowledge manager analyzes parent/child hierarchies between semi-structured items included in a semi-structured resource. The knowledge manager generates synthetic events based upon the parent/child hierarchy analysis and creates a structured resource utilizing the synthetic events. In one embodiment, the structured resource includes structured resource entries, which include a first semi-structured item, a second semi-structured item, and one of the synthetic events. In turn, the knowledge manager queries the structured resource based upon a question received from a user interface.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Abstract: One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.