International Business Machines Corporation

Kevin B Haverlock

Peter Frederick Haggar

Christopher S Alkov

Suzanne L Estrada

Michael D Whitley

Corville O Allen

William Graham O'Keeffe

Swaminathan Chandrasekaran

Kyle Croutwater

Nicholas James Hill

Roy D Mobley, Jr

Cale R Vardy

Stephan Joseph Roorda

Dan O'Connor

Mark Marrara

David Deidou Taieb

Suneil H Berajawala

Mechanisms are provided in a data processing system for utilizing algorithms based on categories in a question answering system. The mechanisms capture a history of performance and correctness metrics for identifying efficiency of respective algorithms for finding answers to questions in respective question categories in a question answering system. The mechanisms determine sets of algorithms to use for respective question categories according to efficiency and correctness analysis. The mechanisms determine a question category of a given input question and execute a set of algorithms corresponding to the question category of the given input question that meet an efficiency threshold to contribute to finding a correct answer for the given input question.

Learning model for dynamic component utilization in a question answering system

An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.

Method for dynamically assigning question priority based on question extraction and domain dictionary

Mechanisms are provided for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines. The mechanisms receive an input question for processing by the QA system and determine a predicted question difficulty of the input question. The mechanisms select a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty and route the input question to the selected QA system pipeline for processing. In addition, the mechanisms process the input question with the selected QA system pipeline to generate an answer for the input question.

Dynamic load balancing based on question difficulty

Mechanisms are provided for performing an operation based on an identification of similar lines of questioning by input question sources. Question information identifying extracted features of an input question and a first source of the input question is obtained. A clustering operation is performed to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions. An operation is performed based on results of the clustering of the input question with the one or more other questions.

Categorizing users based on similarity of posed questions, answers and supporting evidence

An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.

Information handling system and computer program product for dynamically assigning question priority based on question extraction and domain dictionary

Context data is determined for a web page. The context data includes textual data that describes subject matter displayed on the web page. A client query is retrieved from a client-retrieved instance of the web page. An identifier associated with the web page capable of distinguishing the web page from other web pages is determined. In response to receiving the client query, a plurality of queries are retrieved from a database. Whether one or more of the retrieved queries correlate the determined web page context and the received client query is determined. A set of one or more queries from the plurality of queries is selected based on the selected set of queries each having been determined to correlate the web page context data and the client query. The selected set of the plurality of the queries is sent to the client-retrieved instance of the web page.

Context aware query selection

Mechanisms are provided, in a Question and Answer (QA) system, for identifying a related previously processed question. A first question is processed by the QA system to generate an answer to the first question and a supporting evidence passage that supports the answer to the first question being a correct answer to the first question. The QA system performs a lookup operation in a question repository based on an identifier of the supporting evidence passage to identify an entry corresponding to a previously processed question, different than the first question, that previously had an answer generated based on the supporting evidence passage. The QA system returns the previously processed question and the answer to the first question to a submitter of the first question.

Returning related previously answered questions based on question affinity

An approach is provided for evaluating subject matter experts (SMEs) in a question and answering (QA) system. In the approach, a number of responses are received at the QA system with each of the responses being a response to a common question and each of the responses being received from a SME. One of the responses is selected with the selected response being from one of the SMEs that is being evaluated. The approach evaluates the selected response by comparing the selected response to the responses received from the other SMEs. Based on the evaluation, the approach updates a SME confidence score that corresponds to the selected SME.

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Cary, NC, United States of America

Kevin B Haverlock