International Business Machines Corporation

Timothy A Bishop

Stephen Arthur Boxwell

Stanley John Vernier

Benjamin L Brumfield

Nirav P Desai

Corville O Allen

Leah Rae Smutzer

Sue S Schmidt

Michael Terry Payne

Mechanisms are provided for implementing a disambiguation engine for disambiguating content. Electronic content is received from a corpus of electronic content, and analyzed to identify an ambiguous portion of content. The ambiguous portion of content is a portion of the electronic content whose meaning is not made explicit in the ambiguous portion of content. A context associated with the ambiguous portion of content is determined and a set of one or more context based ambiguous content interpretation rules associated with the determined context is applied to the ambiguous portion of content to generate an interpretation of the ambiguous portion of content. The ambiguous portion of content is annotated based on the interpretation to generate disambiguated electronic content which is stored for processing as part of a subsequent operation.

Disambiguation of ambiguous portions of content for processing by automated systems

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.

LAT based answer generation using anchor entities and proximity

Cognitive medical treatment recommendation mechanisms are provided. The mechanisms ingest a corpus of medical treatment content that comprises drug-to-drug interaction information. The mechanisms generate a set of drug interaction insight data structures and an initial set of candidate treatments for a medical condition of a patient based on an analysis of an electronic medical record associated with the patient. The mechanisms rank candidate treatments in the initial set of candidate treatments based on the set of drug interaction insight data structures to generate a final set of candidate treatments, where the ranking reduces rankings of candidate treatments in which drug interactions are identified in the set of drug interaction insight data structures. The mechanisms output a treatment recommendation based on the final set of candidate treatments.

Treatment recommendations based on drug-to-drug interactions

Mechanisms are provided for collecting and applying drug-to-drug interaction information from natural language documents. The mechanisms perform natural language processing of natural language documents in a corpus of natural language documents, to identify content in the natural language documents that describe a drug-to-drug interaction. The mechanisms categorize the drug-to-drug interaction and calculate, for a first drug in the drug-to-drug interaction, an exclusion weighting factor for use in evaluating treatments for patients that include a second drug of the drug-to-drug interaction based on the categorization of the drug-to-drug interaction. The mechanisms store the drug-to-drug interaction as a drug-to-drug interaction data structure that specifies the first drug, the second drug, the categorization of the drug-to-drug interaction, and the exclusion weighting factor associated with the drug-to-drug interaction. The mechanisms generate a treatment recommendation for treating a medical condition of a patient based on one or more drug-to-drug interaction data structures.

Identifying drug-to-drug interactions in medical content and applying interactions to treatment recommendations

Mechanisms are provided to implement a medical condition base cartridge generator. The mechanisms ingest an electronic corpus of medical content and generate a medical condition base cartridge for a medical condition. The medical condition base cartridge is a pluggable cartridge comprising insight data structures that specify an association of clinical attributes of patients with the medical condition and a treatment for the medical condition. The mechanisms install the medical condition base cartridge as a resource for performing a cognitive operation.

Cognitive building of medical condition base cartridges for a medical system

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.

Mapping questions to complex database lookups using synthetic events

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.

Scoring automatically generated language patterns for questions using synthetic events

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.

Processing un-typed triple store data

An approach is provided in which a knowledge manager matches a question to multiple natural language contexts that each correspond to relations associated with entities in a structured resource. The knowledge manager identifies database queries corresponding to the multiple natural language contexts and assigns priority scores to the database queries based upon their relative specificity. In turn, the knowledge manager invokes one of the database queries based upon the assigned priority scores.

Using priority scores for iterative precision reduction in structured lookups for questions

An approach is provided in which a knowledge manager receives a question and selects candidate answers based upon a lexical answer type included in the question. The knowledge manager evaluates the candidate answers against conditions detected in the question and generates condition fulfillment scores accordingly. In turn, the knowledge manager ranks the candidate answers based upon the corresponding condition fulfillment scores.

Identification and evaluation of lexical answer type conditions in a question to generate correct answers

An approach is provided in which a knowledge manager analyzes a corpus of documents based upon relations corresponding to entities in a structured resource and constructs a natural language context associated with a set of the relations. The knowledge manager maps the natural language context to a database query and, in turn, invokes the database query when the knowledge manager matches a question to the natural language context.

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Minneapolis, MN, United States of America

Timothy A Bishop