Semantic similarity in the biomedical domain: an evaluation across knowledge sources
<p>Abstract</p> <p>Background</p> <p>Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based a...
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| Format: | Article |
| Language: | English |
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BMC
2012-10-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://www.biomedcentral.com/1471-2105/13/261 |
| _version_ | 1818677242248560640 |
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| author | Garla Vijay N Brandt Cynthia |
| author_facet | Garla Vijay N Brandt Cynthia |
| author_sort | Garla Vijay N |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within a corpus to compute similarity; these include corpus IC and context vector methods. Prior evaluations of these measures in the biomedical domain showed that distributional measures outperform knowledge based path finding methods; but more recent studies suggested that intrinsic IC based measures exceed the accuracy of distributional approaches. Limitations of previous evaluations of similarity measures in the biomedical domain include their focus on the SNOMED CT ontology, and their reliance on small benchmarks not powered to detect significant differences between measure accuracy. There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks.</p> <p>Results</p> <p>We evaluated knowledge based and corpus IC based semantic similarity measures derived from SNOMED CT, MeSH, and the UMLS on recently developed semantic similarity benchmarks. Semantic similarity measures based on the UMLS, which contains SNOMED CT and MeSH, significantly outperformed those based solely on SNOMED CT or MeSH across evaluations. Intrinsic IC based measures significantly outperformed path-based and distributional measures. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under <url>http://code.google.com/p/ytex</url>. We provide a publicly-accessible web service to compute semantic similarity, available under <url>http://informatics.med.yale.edu/ytex.web/</url>.</p> <p>Conclusions</p> <p>Knowledge based semantic similarity measures are more practical to compute than distributional measures, as they do not require an external corpus. Furthermore, knowledge based measures significantly and meaningfully outperformed distributional measures on large semantic similarity benchmarks, suggesting that they are a practical alternative to distributional measures. Future evaluations of semantic similarity measures should utilize benchmarks powered to detect significant differences in measure accuracy.</p> |
| first_indexed | 2024-12-17T08:56:15Z |
| format | Article |
| id | doaj.art-bb2bbd9c7b374523a0b5bdeade18f9a6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T08:56:15Z |
| publishDate | 2012-10-01 |
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| series | BMC Bioinformatics |
| spelling | doaj.art-bb2bbd9c7b374523a0b5bdeade18f9a62022-12-21T21:55:57ZengBMCBMC Bioinformatics1471-21052012-10-0113126110.1186/1471-2105-13-261Semantic similarity in the biomedical domain: an evaluation across knowledge sourcesGarla Vijay NBrandt Cynthia<p>Abstract</p> <p>Background</p> <p>Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within a corpus to compute similarity; these include corpus IC and context vector methods. Prior evaluations of these measures in the biomedical domain showed that distributional measures outperform knowledge based path finding methods; but more recent studies suggested that intrinsic IC based measures exceed the accuracy of distributional approaches. Limitations of previous evaluations of similarity measures in the biomedical domain include their focus on the SNOMED CT ontology, and their reliance on small benchmarks not powered to detect significant differences between measure accuracy. There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks.</p> <p>Results</p> <p>We evaluated knowledge based and corpus IC based semantic similarity measures derived from SNOMED CT, MeSH, and the UMLS on recently developed semantic similarity benchmarks. Semantic similarity measures based on the UMLS, which contains SNOMED CT and MeSH, significantly outperformed those based solely on SNOMED CT or MeSH across evaluations. Intrinsic IC based measures significantly outperformed path-based and distributional measures. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under <url>http://code.google.com/p/ytex</url>. We provide a publicly-accessible web service to compute semantic similarity, available under <url>http://informatics.med.yale.edu/ytex.web/</url>.</p> <p>Conclusions</p> <p>Knowledge based semantic similarity measures are more practical to compute than distributional measures, as they do not require an external corpus. Furthermore, knowledge based measures significantly and meaningfully outperformed distributional measures on large semantic similarity benchmarks, suggesting that they are a practical alternative to distributional measures. Future evaluations of semantic similarity measures should utilize benchmarks powered to detect significant differences in measure accuracy.</p>http://www.biomedcentral.com/1471-2105/13/261Semantic similarityInformation contentInformation theoryBiomedical ontologies |
| spellingShingle | Garla Vijay N Brandt Cynthia Semantic similarity in the biomedical domain: an evaluation across knowledge sources BMC Bioinformatics Semantic similarity Information content Information theory Biomedical ontologies |
| title | Semantic similarity in the biomedical domain: an evaluation across knowledge sources |
| title_full | Semantic similarity in the biomedical domain: an evaluation across knowledge sources |
| title_fullStr | Semantic similarity in the biomedical domain: an evaluation across knowledge sources |
| title_full_unstemmed | Semantic similarity in the biomedical domain: an evaluation across knowledge sources |
| title_short | Semantic similarity in the biomedical domain: an evaluation across knowledge sources |
| title_sort | semantic similarity in the biomedical domain an evaluation across knowledge sources |
| topic | Semantic similarity Information content Information theory Biomedical ontologies |
| url | http://www.biomedcentral.com/1471-2105/13/261 |
| work_keys_str_mv | AT garlavijayn semanticsimilarityinthebiomedicaldomainanevaluationacrossknowledgesources AT brandtcynthia semanticsimilarityinthebiomedicaldomainanevaluationacrossknowledgesources |