International Business Machines Corporation

Joseph M Kaufmann

Lakshminarayanan Krishnamurthy

Niyati Parameswaran

Swaminathan Chandrasekaran

Ryan R Anderson

Eddy Hudson

Edward Eugene Seabolt

Pablo N Mendes

Embodiments provide a computer implemented method for generating a domain-specific thesaurus on a cognitive system, comprising: receiving data of the domain-specific corpus and a plurality of terms of interest from a user; splitting the data of the domain-specific corpus into a plurality of sentences using natural language processing techniques; for each term in the plurality of terms of interest, retrieving a plurality of candidate sentences containing a corresponding term, from the plurality of sentences; for each candidate sentence, providing a list of synonyms of the corresponding term, wherein the synonyms are contextual alternatives in the corresponding candidate sentence; for each term in the plurality of terms of interest, tracking a frequency of each synonym, and forming a frequency map including all the synonyms of a corresponding term and the frequency of each synonym; and generating a domain-specific thesaurus based on a combination of all the synonyms in the frequency map.

Generation of domain thesaurus

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.

Search space reduction for knowledge graph querying and interactions

Embodiments are directed to a method of utilizing an ensemble of distributional semantics systems in conjunction with a domain term extractor for generating domain-specific synonyms. The method allows for extraction of high-quality, domain-specific synonyms that can be used in an information handling system, such as a question-answer system or in an information retrieval (IR) system, capable of processing natural language. According to embodiments, the domain term extractor identifies the words for which synonyms are sought, and the ensemble of distributional semantics systems determines the synonyms.

System and method of inferring synonyms using ensemble learning techniques

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.

Techniques for resolving entities in received questions

A natural language query (NLQ) is translated to a structured data query (e.g., a SQL statement) by extracting entities from the NLQ and replacing them with generic variables to form a generic query. The generic query is associated with a structured question type which includes structured data variables using natural language classifiers (NLCs). Specific data is inserted in the structured question type in relation to the structured data variables based on the extracted entities to form the structured data query. An ensemble of NLCs trained with different ground truths can be used to yield multiple candidate question types. One of the candidate question types is selected based on confidence levels. The multiple NLCs can include an NLC which is optimized according to a focus of the generic query. For example, an NLC can be optimized for a specific data structure (such as SQL), or for comparative queries.

Using multiple natural language classifier to associate a generic query with a structured question type

An approach is provided that improves a question answering (QA) computer system by automatically generating relationship templates. Event patterns are extracted from data in a corpus utilized by the QA computer system. The extracted event patterns are analyzed with the analysis resulting in a number of clusters of related event patterns. Relationship templates are then created from the plurality of clusters of related event patterns and these relationship templates are then utilized to visually interact with the corpus.

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Austin, TX, United States of America

Joseph M Kaufmann