Detecting duplicate biological entities using Markov random field-based edit distance

Document Type

Article

Publication Date

1-1-2010

Abstract

Detecting duplicate entities in biological data is an important research task. In this paper, we propose a novel and context-sensitive Markov random field-based edit distance (MRFED) for this task. We apply the Markov random field theory to the Needleman-Wunsch distance and combine MRFED with TFIDF, a token-based distance algorithm, resulting in SoftMRFED. We compare SoftMRFED with other distance algorithms such as Levenshtein, SoftTFIDF, and Monge-Elkan for two matching tasks: biological entity matching and synonym matching. The experimental results show that SoftMRFED significantly outperforms the other edit distance algorithms on several test data collections. In addition, the performance of SoftMRFED is superior to token-based distance algorithms in two matching tasks. © 2009 Springer-Verlag London Limited.

Identifier

78049440735 (Scopus)

Publication Title

Knowledge and Information Systems

External Full Text Location

https://doi.org/10.1007/s10115-009-0254-7

e-ISSN

02193116

ISSN

02191377

First Page

371

Last Page

387

Issue

2

Volume

25

Grant

0434581

Fund Ref

National Science Foundation

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