Measures of semantic similarity and relatedness in the biomedical domain
By Ted Pedersen, Serguei V. S. Pakhomov, Siddharth Patwardhan, Christopher G. Chute. Journal of Biomedical Informatics, 2007
615 citations recorded by the source corpus.
Measures of semantic similarity between concepts are widely used in Natural Language Processing. In this article, we show how six existing domain-independent measures can be adapted to the biomedical domain. These measures were originally based on WordNet, an English lexical database of concepts and relations. In this research, we adapt these measures to the SNOMED-CT ontology of medical concepts. The measures include two path-based measures, and three measures that augment path-based measures with information content statistics from corpora. We also derive a context vector measure based on medical corpora that can be used as a measure of semantic relatedness. These six measures are evaluated against a newly created test bed of 30 medical concept pairs scored by three physicians and nine medical coders. We find that the medical coders and physicians differ in their ratings, and that the context vector measure correlates most closely with the physicians, while the path-based measures and one of the information content measures correlates most closely with the medical coders. We conclude that there is a role both for more flexible measures of relatedness based on information derived from corpora, as well as for measures that rely on existing ontological structures.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/585d69e342a64bc245b411b4a6c0c61833724781
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Citations in the corpus
- Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature (2018, methodology): “Pedersen et al. [34] state: “ semantically similar concepts are deemed to be related on the basis of their likeness ” .”
- Evolution of Semantic Similarity—A Survey (2020, background): “• SRS [72]: Pedersen et al., attempted to build a domain specific semantic similarity dataset for the biomedical domain.”
- Medical Semantic Similarity with a Neural Language Model (2014, methodology): “A number of previous corpus-based approaches have been employed for semantic similarity measurements and have been evaluated by how well they correlate with human-judged similarity [15, 9].”
- Wikidata: A large-scale collaborative ontological medical database (2019, background): “This taxonomy is a key point as it allows computation of semantic similarity and relatedness in medical field (Pedersen, Pakhomov, Patwardhan, & Chute, 2007).”
- Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards (2015, methodology): “For instance, while developing a new semantic relatedness algorithm designed specifically for the bioinformatics domain, Pedersen et al.[30], tangentially noted that SR judgements from doctors and medical coders differed, remarking that “by all means, more experimentation is necessary” in this area.”
- A Comparison of Word Embeddings for the Biomedical Natural Language Processing (2018, background): “The intrinsic evaluation directly tested semantic relationships between medical words using four published datasets for measuring semantic similarity between medical terms, i.e., Pedersen [29], Hliaoutakis [30], MayoSRS [31], and UMNSRS [32], [33].”