Matching records in a national medical patient index
Communications of the ACM
Also by the same author: AKTiveAuthor, a citation graph approach to name disambiguation
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Applying Semantic Social Graphs to Disambiguate Identity References
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Unsupervised web name disambiguation using semantic similarity and single-pass clustering
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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A major stumbling block preventing machines from understanding text is the problem of entity disambiguation. While humans find it easy to determine that a person named in one story is the same person referenced in a second story, machines rely heavily on crude heuristics such as string matching and stemming to make guesses as to whether nouns are coreferent. A key advantage that humans have over machines is the ability to mentally make connections between ideas and, based on these connections, reason how likely two entities are to be the same. Mirroring this natural thought process, we have created a prototype framework for disambiguating entities that is based on connectedness. In this article, we demonstrate it in the practical application of disambiguating authors across a large set of bibliographic records. By representing knowledge from the records as edges in a graph between a subject and an object, we believe that the problem of disambiguating entities reduces to the problem of discovering the most strongly connected nodes in a graph. The knowledge from the records comes in many different forms, such as names of people, date of publication, and themes extracted from the text of the abstract. These different types of knowledge are fused to create the graph required for disambiguation. Furthermore, the resulting graph and framework can be used for more complex operations. © 2012 Wiley Periodicals, Inc.