J Biomed Inform - Semantic similarity estimation in the biomedical domain: an ontology-based information-theoretic perspective.

Tópicos

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Resumo

Semantic similarity estimation is an important component of analysing natural language resources like clinical records. Proper understanding of concept semantics allows for improved use and integration of heterogeneous clinical sources as well as higher information retrieval accuracy. Semantic similarity has been the focus of much research, which has led to the definition of heterogeneous measures using different theoretical principles and knowledge resources in a variety of contexts and application domains. In this paper, we study several of these measures, in addition to other similarity coefficients (not necessarily framed in a semantic context) that may be useful in determining the similarity of sets of terms. In order to make them easier to interpret and improve their applicability and accuracy, we propose a framework grounded in information theory that allows the measures studied to be uniformly redefined. Our framework is based on approximating concept semantics in terms of Information Content (IC). We also propose computing IC in a scalable and efficient manner from the taxonomical knowledge modelled in biomedical ontologies. As a result, new semantic similarity measures expressed in terms of concept Information Content are presented. These measures are evaluated and compared to related works using a benchmark of medical terms and a standard biomedical ontology. We found that an information-theoretical redefinition of well-known semantic measures and similarity coefficients, and an intrinsic estimation of concept IC result in noticeable improvements in their accuracy.

Resumo Limpo

semant similar estim import compon analys natur languag resourc like clinic record proper understand concept semant allow improv use integr heterogen clinic sourc well higher inform retriev accuraci semant similar focus much research led definit heterogen measur use differ theoret principl knowledg resourc varieti context applic domain paper studi sever measur addit similar coeffici necessarili frame semant context may use determin similar set term order make easier interpret improv applic accuraci propos framework ground inform theori allow measur studi uniform redefin framework base approxim concept semant term inform content ic also propos comput ic scalabl effici manner taxonom knowledg model biomed ontolog result new semant similar measur express term concept inform content present measur evalu compar relat work use benchmark medic term standard biomed ontolog found informationtheoret redefinit wellknown semant measur similar coeffici intrins estim concept ic result notic improv accuraci

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