Abstract
This paper presents a novel and context-sensitive Shortest Path Edit Distance (SPED) applied to duplicate entity detection in biological data. SPED is an extension of Markov Random Field-based Edit Distance. It transforms the edit distance computational problem to the calculation of the shortest path among two selected vertices of a graph. The experimental results show that SPED produces competitive outcomes. Soft-SPED, the combination of SPED with TFIDF, achieves superior performance in most cases.
| Original language | English |
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| Title of host publication | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 |
| Pages | 442-444 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - 2010 Oct 25 |
| Event | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 - Niagara Falls, NY, United States Duration: 2010 Aug 2 → 2010 Aug 4 |
Publication series
| Name | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 |
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Other
| Other | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 |
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| Country | United States |
| City | Niagara Falls, NY |
| Period | 10/8/2 → 10/8/4 |
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Health Information Management
Cite this
Rudniy, A., Song, M., & Geller, J. (2010). Shortest Path Edit Distance for detecting duplicate biological entities. In 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 (pp. 442-444). (2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010). https://doi.org/10.1145/1854776.1854851