Overview - Knowledge Base Acceleration 2012

Proceedings | Data | Runs | Participants

The Knowledge Base Acceleration track in TREC 2012 focused on a single task: filter a time-ordered corpus for documents that are highly relevant to a predefined list of entities. KBA differs from previous filtering evaluations in two primary ways: the stream corpus is >100x larger than previous filtering collections, and the use of entities as topics enables systems to incorporate structured knowledge bases (KB), such as Wikipedia, as external data sources. A successful KBA system must do more than resolve the meaning of entity mentions by linking documents to the KB: it must also distinguish centrally relevant documents that are worth citing in the entity’s WP article. This combines thinking from natural language processing (NLP) and information retrieval (IR).

Track coordinator(s):

• John R. Frank, Massachusetts Institute of Technology
• Max Kleiman-Weiner, Massachusetts Institute of Technology
• Daniel A. Roberts, Massachusetts Institute of Technology
• Feng Niu, University of Wisconsin-Madison
• Ce Zhang, University of Wisconsin-Madison
• Christopher Re, University of Wisconsin-Madison
• Ian Soboroff, National Institute of Standards and Technology (NIST)

Track Web Page: https://trec-kba.org/

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