The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies

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

doi:10.1093/bioinformatics/btt581

Cited by 111 publications

(98 citation statements)

References 12 publications

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“…To evaluate the success of the three ontologies in disease gene prioritization, we obtain mouse model phenotypes associated with loss-of-function mutations in single genes from the MGI database [23] as well as human disease phenotypes associated with Mendelian diseases from the HPO database [12], and apply a semantic similarity measure [24, 41] to compare the phenotypic similarity between phenotypes associated with mouse mutants and human disease. We systematically compute phenotypic similarity between 9131 loss-of-function mouse mutants and 7066 diseases.…”

Section: Resultsmentioning

confidence: 99%

Integrating phenotype ontologies with PhenomeNET

et al. 2017

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BackgroundIntegration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.ResultsHere, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.ConclusionsPhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0167-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Integrating phenotype ontologies with PhenomeNET

et al. 2017

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“…We also recall that DKPro Similarity and NLTK implement measures that can be used to compare two synsets. (Harispe et al, 2014a) OWL, RDF, OBO, etc. CLI, LIB P, G Java FastSemSim (Guzzi et al, 2012) OBO and others CLI, LIB P, G Python SimPack (Bernstein et al, 2005) OWL, RDF LIB P Java SemMF (Oldakowski and Bizer, 2005) OWL, RDF LIB P, G Java OntoSim (David and Euzenat, 2008) OWL, RDF LIB P Java…”

Section: Tools For Knowledge-based Semantic Measuresmentioning

confidence: 99%

“…SML stands for Semantic Measures Library (Harispe et al, 2014a). This is a Java library dedicated to semantic measure computation, development and analysis.…”

Section: Tools For Knowledge-based Semantic Measuresmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

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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“…Options include R packages (e.g., GoSemSim [ 34 ]) or standalone programs (GOssTo [ 33 ]), which give the user more freedom in terms of ontology and annotation versions as well as in programmatic access or the computation of SS for larger datasets. A Java library has been recently developed for ontology-based SS calculations [ 35 ], which includes over 50 different SS measures and accepts input ontologies in a number of formats, including OWL, OBO, and RDF. This library is well suited for large input datasets, being able to run over 100 million comparisons in under 1 h. In the case of webtools, we advise readers to check their update frequency to ensure that recent versions of GO and the annotations are in use.…”

Section: Toolsmentioning

confidence: 99%

“…In addition, the growth in size of biomedical datasets spurred by genomic scale studies in the last few years, also places further computational constraints on SS measures. The challenge of handling very large datasets is increasingly recognized, and recent implementations of SS measures allow for parallel computation [ 35 ], but the development of SS measures is not taking this issue into consideration a priori.…”

Section: Challenges and Future Directionsmentioning

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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The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies

Abstract: Downloads, documentation, tutorials, evaluation and support are available at http://www.semantic-measures-library.org.

Cited by 111 publications

References 12 publications

Integrating phenotype ontologies with PhenomeNET

Integrating phenotype ontologies with PhenomeNET

Semantic Similarity from Natural Language and Ontology Analysis

Semantic Similarity in the Gene Ontology

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