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Introduction to "This is Watson"
IBM Journal of Research and Development
Question analysis: how watson reads a clue
IBM Journal of Research and Development
IBM Journal of Research and Development
Automatic knowledge extraction from documents
IBM Journal of Research and Development
Finding needles in the haystack: search and candidate generation
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Relation extraction and scoring in DeepQA
IBM Journal of Research and Development
A framework for merging and ranking of answers in DeepQA
IBM Journal of Research and Development
Introduction to "This is Watson"
IBM Journal of Research and Development
Question analysis: how watson reads a clue
IBM Journal of Research and Development
IBM Journal of Research and Development
Textual resource acquisition and engineering
IBM Journal of Research and Development
Finding needles in the haystack: search and candidate generation
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Relation extraction and scoring in DeepQA
IBM Journal of Research and Development
Special questions and techniques
IBM Journal of Research and Development
Identifying implicit relationships
IBM Journal of Research and Development
A framework for merging and ranking of answers in DeepQA
IBM Journal of Research and Development
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Although the majority of evidence analysis in DeepQA is focused on unstructured information (e.g., natural-language documents), several components in the DeepQA system use structured data (e.g., databases, knowledge bases, and ontologies) to generate potential candidate answers or find additional evidence. Structured data analytics are a natural complement to unstructured methods in that they typically cover a narrower range of questions but are more precise within that range. Moreover, structured data that has formal semantics is amenable to logical reasoning techniques that can be used to provide implicit evidence. The DeepQA system does not contain a single monolithic structured data module; instead, it allows for different components to use and integrate structured and semistructured data, with varying degrees of expressivity and formal specificity. This paper is a survey of DeepQA components that use structured data. Areas in which evidence from structured sources has the most impact include typing of answers, application of geospatial and temporal constraints, and the use of formally encoded a priori knowledge of commonly appearing entity types such as countries and U.S. presidents. We present details of appropriate components and demonstrate their end-to-end impact on the IBM Watsoni system.