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Cited by 142Biomedical Text Mining and OntologiesSemantic Web and OntologiesBioinformatics and Genomic Networks

A framework for unifying ontology-based semantic similarity measures: A study in the biomedical domain

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Harispe, Sébastien, Sánchez, David, Ranwez, Sylvie, Janaqi, Stefan, Montmain, Jacky·2014·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2013.11.006
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Abstract

Ontologies are widely adopted in the biomedical domain to characterize various resources (e.g. diseases, drugs, scientific publications) with non-ambiguous meanings. By exploiting the structured knowledge that ontologies provide, a plethora of ad hoc and domain-specific semantic similarity measures have been defined over the last years. Nevertheless, some critical questions remain: which measure should be defined/chosen for a concrete application? Are some of the, a priori different, measures indeed equivalent? In order to bring some light to these questions, we perform an in-depth analysis of existing ontology-based measures to identify the core elements of semantic similarity assessment. As a result, this paper presents a unifying framework that aims to improve the understanding of semantic measures, to highlight their equivalences and to propose bridges between their theoretical bases. By demonstrating that groups of measures are just particular instantiations of parameterized functions, we unify a large number of state-of-the-art semantic similarity measures through common expressions. The application of the proposed framework and its practical usefulness is underlined by an empirical analysis of hundreds of semantic measures in a biomedical context.

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  • In the field of semantic similarities of texts, artificial intelligence methods, natural language processing methods, data mining, methods of stylometric analysis, methods of extraction and presentation of knowledge and meaning from documents, data and natural languages (graphical methods such as BG and NOK, data models, semantic networks, neural networks, MultiNets, HSF) are being intensively开发的.

    “In the field of the semantic similarities of texts (Harispe et al., 2014; Marsi & Krahmer, 2010; Zervanou et al., 2014), artificial intelligence methods are being intensively developed, natural language processing methods, data mining, methods of stylometric analysis of text, methods of extraction and presentation of knowledge and meaning from documents (Jakupović et al., 2013; Rau”

    From Taxonomy of academic plagiarism methods (2021) · cites this paper as Harispe et al., 2014

    The Introduction (page unavailable) discusses ontology-based semantic similarity measures and says they are receiving more attention, including for applications that disambiguate texts. It concerns similarity between ontology concepts, however, not semantic similarity between texts generally; it does not support attributing the listed range of developing methods to this paper. (LLM, conf=0.81)

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Research works printed but not linked: 41

  1. Gruber T. A translation approach to portable ontology specifications. Knowledge Acquisition 1993;5(2):199-220.
  2. Hliaoutakis A, Varelas G, Voutsakis E, Petrakis EGM, Milios E. Information retrieval by semantic similarity. Int J Semant Web Inf Syst 2006;2:55-73.
  3. Leroy G, Rindflesch TC. Effects of information and machine learning algorithms on word sense disambiguation with small datasets. Int J Med Inform 2005;74:573-85.
  4. Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011;7:496.
  5. Pesquita C, Faria D, Falcão AO, Lord P, Couto FM. Semantic similarity in biomedical ontologies. PLoS Comput Biol 2009;5:12.
  6. Sánchez D, Batet M. Semantic similarity estimation in the biomedical domain: an ontology-based information-theoretic perspective. J Biomed Inform 2011;44:749-59.
  7. Pedersen T, Pakhomov SVS, Patwardhan S, Chute CG. Measures of semantic similarity and relatedness in the biomedical domain. J Biomed Inform 2007;40:288-99.
  8. Bizer C, Heath T, Berners-Lee T. Linked data -the story so far. Int J Semant Web Inf Syst 2009;5:1-22.
  9. Guzzi PH, Mina M, Guerra C, Cannataro M. Semantic similarity analysis of protein data: assessment with biological features and issues. Brief Bioinform 2012;13:569-85.
  10. Resnik P. Using Information content to evaluate semantic similarity in a taxonomy. In: Proc 14th Int Jt Conf Artif Intell IJCAI; 1995. p. 448-53.
  11. Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 1989;19:17-30.
  12. Spackman KA. SNOMED CT milestones: endorsements are added to already- impressive standards credentials. Healthc Informatics Bus Mag Inf Commun Syst 2004;21:54-6.
  13. Leacock C, Chodorow M. Combining local context and WordNet similarity for word sense identification. In: Fellbaum C, editor. WordNet an electron, Lex database. MIT Press; 1998. p. 265-83.
  14. Pekar V, Staab S. Taxonomy learning: factoring the structure of a taxonomy into a semantic classification decision. In: COLING '02 Proc 19th Int Conf Comput Linguist Association for, Computational Linguistics; 2002. p. 1-7.
  15. Tversky A. Features of similarity. Psychol Rev 1977;84:327-52.
  16. Sánchez D, Batet M, Isern D, Valls A. Ontology-based semantic similarity: a new feature-based approach. Expert Syst Appl 2012;39:7718-28.
  17. Rodríguez A, Egenhofer MJ. Determining semantic similarity among entity classes from different ontologies. IEEE Trans Knowl Data Eng 2003;15:442-56.
  18. Pirró G, Euzenat J. A feature and information theoretic framework for semantic similarity and relatedness. In: Proc 9th Int Semant Web Conf ISWC 2010, Springer; 2010. p. 615-30.
  19. Lee JH, Kim MH, Lee YJ. Information retrieval based on conceptual distance in is-a hierarchies. J Doc 1993;49:188-207.
  20. Couto FM, Silva MJ, Coutinho PM. Semantic similarity over the gene ontology: family correlation and selecting disjunctive ancestors. In: Conf Inf Knowl Manag, ACM; 2005. p. 343-4.
  21. Couto FM, Silva MJ. Disjunctive shared information between ontology concepts: application to gene ontology. J Biomed Semantics 2011;2:5.
  22. Sánchez D, Batet M, Isern D. Ontology-based information content computation. Knowledge-Based Syst 2011;24:297-303.
  23. Seco N, Veale T, Hayes J. An intrinsic information content metric for semantic similarity in WordNet. In: 16th Eur Conf Artif Intell. IOS Press; 2004. p. 1-5.
  24. Zhou Z, Wang Y, Gu J. A new model of information content for semantic similarity in WordNet. In: FGCNS '08 Proc 2008 Second Int Conf Futur Gener Commun Netw Symp -vol. 03, IEEE Computer Society; 2008. p. 85-9.
  25. Wang JZ, Du Z, Payattakool R, Yu PS, Chen C-F. A new method to measure the semantic similarity of GO terms. Bioinformatics 2007;23:1274-81.
  26. Othman RM, Deris S, Illias RM. A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences. J Biomed Inform 2008;41:65-81.
  27. Li Y, Bandar ZA, McLean D. An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans Knowl Data Eng 2003;15:871-82.
  28. Cross V, Yu X, Hu X. Unifying ontological similarity measures: a theoretical and empirical investigation. Int J Approx Reason 2013;54:861-75.
  29. Cross V. Fuzzy semantic distance measures between ontological concepts. In: IEEE Annu Meet Fuzzy Information, 2004. Process NAFIPS '04, IEEE, vol. 2; 2004. p. 635-40.
  30. Resnik P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 1999;11:95-130.
  31. Yang H, Nepusz T, Paccanaro A. Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty. Bioinformatics 2012;28:1383-9.
  32. Lin D. Automatic retrieval and clustering of similar words. In: Proc 17th Int Conf Comput Linguist -vol. 2. Association for Computational Linguistics, Stroudsburg, PA, USA; 1998. p. 768-74.
  33. Pakhomov S, McInnes B, Adam T, Liu Y, Pedersen T, Melton GB. Semantic similarity and relatedness between clinical terms: an experimental study. AMIA Annu Symp Proc 2010;2010:572-6.
  34. Pakhomov SVS, Pedersen T, McInnes B, Melton GB, Ruggieri A, Chute CG. Towards a framework for developing semantic relatedness reference standards. J Biomed Inform 2011;44:251-65.
  35. Nagar A, Al-Mubaid H. A new path length measure based on GO for gene similarity with evaluation using SGD pathways. In: 2008 21st IEEE Int Symp Comput Med Syst, IEEE; 2008. p. 590-5.
  36. Ranwez S, Ranwez V, Villerd J, Crampes M. Ontological distance measures for information visualisation on conceptual maps. Lect Notes Comput Sci 2006;4278(2006):1050-61.
  37. Batet M, Sánchez D, Valls A. An ontology-based measure to compute semantic similarity in biomedicine. J Biomed Inform 2010;4:39-52.
  38. Petrakis E, Varelas G, Hliaoutakis A, Raftopoulou P. X-similarity: computing semantic similarity between concepts from different ontologies. J Digit Inf Manage 2006;4:233.
  39. Schlicker A, Domingues FS, Rahnenführer J, Lengauer T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 2006;7:302.
  40. Al-Mubaid H, Nguyen HA. A cluster-based approach for semantic similarity in the biomedical domain. In: Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Conf; 2006. p. 2713-7.
  41. Alvarez M, Yan C. A graph-based semantic similarity measure for the gene ontology. J Bioinform Comput Biol 2011;9:681-95.

Cited by

Sorted by citations

Authors
Harispe, S., Sánchez, D., Ranwez, S., Janaqi, S., & Montmain, J.
Affiliations
Universitat Rovira i Virgili (ES)
Corresponding author
Harispe, Sébastien
ORCID
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2014-04
Volume
48
Pages
38-53
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2013.11.006
OpenAlex
W2078990125
Cited by
142
References
0
Citations by year
2026: 1 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 7 · 2021: 9 · 2020: 12 · 2019: 15 · 2018: 20 · 2017: 14 · 2016: 18 · 2015: 21 · 2014: 13
FWCI
7.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • Rovira & Virgili repository (submittedVersion, cc-by)
  • HAL (submittedVersion, other-oa)
Funders
Government of Catalonia (2009 SGR 1135)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
semantic similarity measures, ontology (information science), biomedical ontologies, unified framework, measurement invariance, ontology-based similarity, knowledge representation
MeSH terms
Algorithms, Humans, Medical Informatics, Models, Theoretical, Natural Language Processing, Terminology as Topic, Semantics, Software, Reproducibility of Results, Vocabulary, Controlled, Systematized Nomenclature of Medicine

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