2010 DetectingDupBioEntUsMarRandFiBasEdDist
- (Song & Rudniy, 2010) ⇒ Min Song and Alex Rudniy. “Detecting Duplicate Biological Entities Using Markov Random Field-based Edit Distance.” In: International Journal on Knowledge and Information Systems (KAIS), (25:2). doi:10.1007/s10115-009-0254-7
Subject Headings:Duplicate detection, Markov random field, Edit distance, Biomedical digital libraries.
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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.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2010 DetectingDupBioEntUsMarRandFiBasEdDist | Min Song Alex Rudniy | Detecting Duplicate Biological Entities Using Markov Random Field-based Edit Distance | International Journal on Knowledge and Information System | http://www.springerlink.com/content/c2740p2p412r6j6q/ |