IBM Watson on Jeopardy! (2011) › Knowledge Representation and Retrieval
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Watson indexed a large collection that included encyclopedias, news articles, books, reference works, and other factual sources. The system had to retrieve relevant passages quickly and turn their evidence into candidate features.
Passage retrieval found text likely to contain an answer, document ranking prioritized the evidence, and evidence-combination components aggregated support from multiple sources.
The knowledge base was structured to support rapid retrieval. Unlike traditional databases with rigid schemas, Watson's knowledge sources included unstructured text that required natural language processing to extract information. The system needed to understand what information was present, how to find it, how relevant it was, and how to combine it with other evidence to answer questions.
Different source types contributed different evidence. Encyclopedias supplied broad factual coverage, news articles covered events, books provided longer explanations, and reference works supplied specialized facts. Source-related features helped the scoring model weigh that evidence.