Learning Co-reference Relations for FOAF Instances

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https://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances

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http://hdl.handle.net/11603/12069

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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Date

2010-11-09

Type of Work

conference papers and proceedings preprints
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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

The friend of a friend (FOAF)
machine learning
linked data
UMBC Ebiquity Research Group

Abstract

FOAF is widely used on the Web to describe people, groups and organizations and their properties. Since FOAF does not require unique IDs, it is often unclear when two FOAF instances are co-referent, i.e., denote the same entity in the world. We describe a prototype system that identifies sets of co-referent FOAF instances using logical constraints (e.g., IFPs), strong heuristics (e.g., FOAF agents described in the same file are not co-referent), and a Support Vector Machine (SVM) generated classifier.