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Erschienen in:
Do Scaling Algorithms Preserve Word2Vec Semantics? A Case Study for Medical Entities Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarity

verfasst von : Silvio Domingos Cardoso, Marcos Da Silveira, Ying-Chi Lin, Victor Christen, Erhard Rahm, Chantal Reynaud-Delaître, Cédric Pruski

Erschienen in: Data Integration in the Life Sciences

Verlag: Springer International Publishing

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Abstract

The use of similarity measures in various domains is cornerstone for different tasks ranging from ontology alignment to information retrieval. To this end, existing metrics can be classified into several categories among which lexical and semantic families of similarity measures predominate but have rarely been combined to complete the aforementioned tasks. In this paper, we propose an original approach combining lexical and ontology-based semantic similarity measures to improve the evaluation of terms relatedness. We validate our approach through a set of experiments based on a corpus of reference constructed by domain experts of the medical field and further evaluate the impact of ontology evolution on the used semantic similarity measures.

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Vorheriges Kapitel Do Scaling Algorithms Preserve Word2Vec Semantics? A Case Study for Medical Entities
Nächstes Kapitel Construction and Visualization of Dynamic Biological Networks: Benchmarking the Neo4J Graph Database
Fußnoten
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Metadaten
Titel
Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarity
verfasst von
Silvio Domingos Cardoso
Marcos Da Silveira
Ying-Chi Lin
Victor Christen
Erhard Rahm
Chantal Reynaud-Delaître
Cédric Pruski
Copyright-Jahr
2019
Buch
Data Integration in the Life Sciences

Print ISBN: 978-3-030-06015-2

Electronic ISBN: 978-3-030-06016-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-06016-9_2