Abstract: Mechanisms are provided for training and operating a Question and Answer (QA) system pipeline. A corpus of information is received which comprises historical data to which one or more filter criteria are applied to extract filtered historical data relevant to a training objective for training the QA system pipeline. Attribute data, action data, and temporal characteristic data are captured from the filtered historical data. An answer key entry is automatically generated in an automatically generated training answer key data structure based on the attribute data, action data, and temporal characteristic data. The correct answer associated with the answer key entry is an action specified by the action data. The temporal characteristic data provides a historical context for the answer key entry. The QA system pipeline is trained using the automatically generated training answer key data structure.

Abstract: Mechanisms are provided for training and operating a Question and Answer (QA) system pipeline. A corpus of information is received which comprises historical data to which one or more filter criteria are applied to extract filtered historical data relevant to a training objective for training the QA system pipeline. Attribute data, action data, and temporal characteristic data are captured from the filtered historical data. An answer key entry is automatically generated in an automatically generated training answer key data structure based on the attribute data, action data, and temporal characteristic data. The correct answer associated with the answer key entry is an action specified by the action data. The temporal characteristic data provides a historical context for the answer key entry. The QA system pipeline is trained using the automatically generated training answer key data structure.

Abstract: Mechanisms are provided for training and operating a Question and Answer (QA) system pipeline. A corpus of information is received which comprises historical data to which one or more filter criteria are applied to extract filtered historical data relevant to a training objective for training the QA system pipeline. Attribute data, action data, and temporal characteristic data are captured from the filtered historical data. An answer key entry is automatically generated in an automatically generated training answer key data structure based on the attribute data, action data, and temporal characteristic data. The correct answer associated with the answer key entry is an action specified by the action data. The temporal characteristic data provides a historical context for the answer key entry. The QA system pipeline is trained using the automatically generated training answer key data structure.

Abstract: A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.

Abstract: A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.

Abstract: A cohort analysis mechanism analyzes cohorts to infer one or more additional attributes for an input case to provide a refined input case to answer a question in a question answering system. The refined input case is then used to answer a question in the question answering system. The refined input case can be used in a traditional question answering flow or in a flow that again uses cohort analysis to extract relevant data to answer the question. The cohort analysis mechanism analyzes cohorts to find common attributes in the cohorts and then determines whether to infer the common attributes into the refined input case. The cohort analysis mechanism may determine to dialog with a user to confirm an inferred attribute.

Abstract: A cohort analysis mechanism analyzes cohorts to infer one or more additional attributes for an input case to provide a refined input case to answer a question in a question answering system. The refined input case is then used to answer a question in the question answering system. The refined input case can be used in a traditional question answering flow or in a flow that again uses cohort analysis to extract relevant data to answer the question. The cohort analysis mechanism analyzes cohorts to find common attributes in the cohorts and then determines whether to infer the common attributes into the refined input case. The cohort analysis mechanism may determine to dialog with a user to confirm an inferred attribute.

Abstract: A cohort analysis mechanism analyzes cohorts to infer one or more additional attributes for an input case to provide a refined input case to answer a question in a question answering system. The refined input case is then used to answer a question in the question answering system. The refined input case can be used in a traditional question answering flow or in a flow that again uses cohort analysis to extract relevant data to answer the question. The cohort analysis mechanism analyzes cohorts to find common attributes in the cohorts and then determines whether to infer the common attributes into the refined input case. The cohort analysis mechanism may determine to dialog with a user to confirm an inferred attribute.

Abstract: A cohort analysis mechanism analyzes cohorts to infer one or more additional attributes for an input case to provide a refined input case to answer a question in a question answering system. The refined input case is then used to answer a question in the question answering system. The refined input case can be used in a traditional question answering flow or in a flow that again uses cohort analysis to extract relevant data to answer the question. The cohort analysis mechanism analyzes cohorts to find common attributes in the cohorts and then determines whether to infer the common attributes into the refined input case. The cohort analysis mechanism may determine to dialog with a user to confirm an inferred attribute.

Abstract: Mechanisms are provided for training and operating a Question and Answer (QA) system pipeline. A corpus of information is received which comprises historical data to which one or more filter criteria are applied to extract filtered historical data relevant to a training objective for training the QA system pipeline. Attribute data, action data, and temporal characteristic data are captured from the filtered historical data. An answer key entry is automatically generated in an automatically generated training answer key data structure based on the attribute data, action data, and temporal characteristic data. The correct answer associated with the answer key entry is an action specified by the action data. The temporal characteristic data provides a historical context for the answer key entry. The QA system pipeline is trained using the automatically generated training answer key data structure.

Abstract: A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.

Abstract: A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.