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

Harispe, Sébastien; Sánchez, David; Ranwez, Sylvie; Janaqi, Stefan; Montmain, Jacky

doi:10.1016/j.jbi.2013.11.006

Cited by 115 publications

(73 citation statements)

References 39 publications

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“…During last decade, many measures have been proposed to utilize GO ontology advantages to measure semantic Table 5 similarities between biological entities. The state-of-theart semantic similarity measures are classified into three groups: node-based, edge-based and hybrids of edge-and node based measures [3,4].…”

Section: Discussionmentioning

confidence: 99%

“…A semantic similarity measure is defined as a function that given two biological terms (or two sets of terms) estimates their functional similarity according to the taxonomical structure of concepts in the ontology [2]. The state-of-the-art semantic measures of GO ontology terms can be classified into three groups: node-based, edge-based and a hybrid of edge-and node based [3,4].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Lakizadeh

Jalili

2017

Zahedan J Res Med Sci

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Background: Gene ontology (GO) is a well-structured knowledge of biological terms that describes roles of genes and their products in a standardized and organized controlled vocabulary format. Over the last decade, many measures are developed to exploit GO advantages to determine semantic similarities between biological entities. Using GO ontologies, there are some constraints that existing GO-based semantic similarity measures try to address them. For instance, (1) edges in a GO graph, do not indicate uniform distances and also have different densities, and (2) ignoring term levels in an ontology makes "shallow annotation" drawback, i.e., two terms with a certain distance near the root of GO graph have equal semantic similarity with two terms with the same distance but far from the root.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Lakizadeh

Jalili

2017

Zahedan J Res Med Sci

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“…This dataset has also been used to compare concepts defined into MeSH or SNOMED-CT -correspondences between labels and concept identifiers are provided by Batet et al (2014) and Harispe et al (2013c). This dataset provides scores of semantic similarity and relatedness between pairs of medical termsterms refer to UMLS concepts.…”

Section: -Concept Relatednessmentioning

confidence: 99%

“…However, several initiatives have proposed theoretical tools to ease the characterisation of measures, for instance by means of measure unification (Cross et al, 2013;Cross, 2006;Cross and Yu, 2010;Pirró and Euzenat, 2010a;Mazandu and Mulder, 2013;Cross et al, 2013;Harispe et al, 2013c), and by means of semantic model unification in distributional semantics, e.g. (Baroni and Lenci, 2010).…”

Section: Develop Theoretical Toolsmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

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166

102

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Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

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“…For a theoretical framework for SS measures please refer to [ 8 ], where the core elements shared by most SS measures are identifi ed and a foundation for the comparison, selection, and development of novel measures is laid out.…”

Section: Ss Measuresmentioning

confidence: 99%

Semantic Similarity in the Gene Ontology

Pesquita

2016

Methods in Molecular Biology

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Gene Ontology-based semantic similarity (SS) allows the comparison of GO terms or entities annotated with GO terms, by leveraging on the ontology structure and properties and on annotation corpora. In the last decade the number and diversity of SS measures based on GO has grown considerably, and their application ranges from functional coherence evaluation, protein interaction prediction, and disease gene prioritization.Understanding how SS measures work, what issues can affect their performance and how they compare to each other in different evaluation settings is crucial to gain a comprehensive view of this area and choose the most appropriate approaches for a given application.In this chapter, we provide a guide to understanding and selecting SS measures for biomedical researchers. We present a straightforward categorization of SS measures and describe the main strategies they employ. We discuss the intrinsic and external issues that affect their performance, and how these can be addressed. We summarize comparative assessment studies, highlighting the top measures in different settings, and compare different implementation strategies and their use. Finally, we discuss some of the extant challenges and opportunities, namely the increased semantic complexity of GO and the need for fast and effi cient computation, pointing the way towards the future generation of SS measures.

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

Cited by 115 publications

References 39 publications

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Semantic Similarity from Natural Language and Ontology Analysis

Semantic Similarity in the Gene Ontology

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