International Business Machines Corporation

Michael D Whitley

Kevin B Haverlock

Vijay Dheap

Corville O Allen

Kyle Croutwater

Robert Leslie Yates

John A Riendeau

Stephan Joseph Roorda

Lee Michael Surprenant

Le Zhang

Nicholas E Poore

Erinc Merdivan

Donna Karen Byron

Ashok Kumar

Mary Swift

Jimmy Ming-Der Hsu

Bridget Briana Beamon

Peter Frederick Haggar

Zhe Zhang

Vikrant Verma

Scott M Lewis

Eric Donald Wayne

Jadran Sirotkovic

Robert C Johnson, Jr

Richard L Stillwell

Dane A Johnson

A method, system, and computer-usable medium are disclosed for identifying areas to improve an interactive conversational system, such as a chatbot. A stream of stream of conversational interactions C (C, C, . . . , C) between users and the interactive conversational system is received. An intent clustering model is periodically applied to the stream to form an incremental clustering based on a set of derived intents to form a mapping from a first set of conversational characteristics to a first set of intents and a second set of conversational characteristics to a first set of unclear intents. Information is provided related to the second set of conversation characteristics.

System and method for alerts for missing coverage of chatbot conversation messages

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

A method, computer program product, and computer system for receiving, by a computing device, a question from a user. A first answer provided by a first subject matter expert is identified. A second answer provided by a second subject matter expert is identified. It is determined that a profile of the user matches a first profile of the first subject matter expert more than a second profile of the second subject matter expert. The first answer provided by the first subject matter expert is sent to the user with a preference over the second answer provided by the second subject matter expert based upon, at least in part, determining that the profile of the user matches the first profile of the first subject matter expert more than the second profile of the second subject matter expert.

System and method for providing more appropriate question/answer responses based upon profiles

Mechanisms are provided for generating an answer to an input question. An input question is received and a set of candidate answers is generated along with, for each candidate answer in the set of candidate answers, a corresponding selection of one or more selected evidence portions from a corpus of information providing evidence in support of the candidate answer being a correct answer for the input question. The candidate answers are ranked based on an application of a justifying passage model (JPM) to the selected evidence portions for each of the candidate answers in the set of candidate answers. The JPM identifies whether a candidate answer is justified by a selected evidence passage corresponding to the candidate answer. A candidate answer is output as the correct answer for the input question based on the ranking of the candidate answers.

Justifying passage machine learning for question and answer systems

An approach is provided in which a knowledge manager identifies a first cohort type and a second cohort type corresponding to an entity included in a question. The knowledge manager determines inferred states to the question by comparing a first set of cohort attributes corresponding to a first cohort type against entity attributes corresponding to the question. In turn, the knowledge manager generates possible answers by comparing the inferred states against a second set of cohort attributes corresponding to a second cohort type.

Inferred facts discovered through knowledge graph derived contextual overlays

Mechanisms for processing a knowledge canvassing request receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Knowledge canvassing using a knowledge graph and a question and answer system

Systems, methods and articles of manufacture allow adjusting the relative weighting associated with evaluation criteria associated with a unified view of dissimilar data. The operation generally includes collecting data regarding attributes of a user interacting with an application, where the collected data has associated evaluation criteria. The data is collectively represented according to the evaluation criteria. The systems, methods and articles of manufacture then allow dynamically modifying the evaluation criteria before evaluating and collectively representing the data according to the adjusted criteria.

Applying relative weighting schemas to online usage data

Mechanisms are provided, in association with a question and answer system, for generating answers to an input question. An input question is received and processed to generate at least one query. The at least one query is applied to the corpus to generate a set of candidate answers and corresponding selections of evidence portions of the corpus of information providing evidence in support of the at least one candidate answer being a correct answer for the input question. A graphical user interface (GUI) is output comprising a first GUI sub-section comprising the set of candidate answers, and a second GUI sub-section comprising the evidence portions of the corpus. User input modifying at least one of set of candidate answers or the evidence portions of the corpus of information is received via the GUI and an operation of the data processing system is adjusted based on the user input.

User collaboration for answer generation in question and answer system

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.

Evaluating expert opinions in a question and answer system

An approach is provided to use natural language processing (NLP) to analyze data logs. In the approach, a data log is analyzed with the data log including a number of entries. The analysis performed includes performance of a sentiment analysis of the entries using natural language processing. The result of the sentiment analysis results is a sentiment score. Entries from the data log are then visually highlighted based on their corresponding sentiment scores.

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Durham, NC, United States of America

Michael D Whitley