Knowledge Base Evaluation for Semantic Knowledge Discovery

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Links to Files

https://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery

Permanent Link

http://hdl.handle.net/11603/12110

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-6593-1792

Date

2008-11-14

Type of Work

conference papers and proceedings
preprints
Text

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Program

Citation of Original Publication

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Knowledge Base Evaluation
Semantic Knowledge Discovery
UMBC Ebiquity Research Group

Abstract

Semantic knowledge discovery has traditionally been evaluated at the text level. For example, evaluations such as MUC and ACE evaluate the information extraction of particular types of semantic roles and relations primarily at the mention level. We suggest that evaluating at the level of a knowledge base (KB) extracted from the text has significant advantages over evaluation at the text level. By knowledge base, we mean the combination of a database, a descriptive schema for the contents of the database, a collection of background knowledge, and an inference capability.