Literature DB >> 29462669

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.

Muhammad Amith1, Zhe He2, Jiang Bian3, Juan Antonio Lossio-Ventura3, Cui Tao4.   

Abstract

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Biomedical ontologies; Knowledge representation; Ontology evaluation; Quality assurance

Mesh:

Year:  2018        PMID: 29462669      PMCID: PMC5882531          DOI: 10.1016/j.jbi.2018.02.010

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  72 in total

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Journal:  Methods Inf Med       Date:  2006       Impact factor: 2.176

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3.  Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities.

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4.  MaPLE: A MapReduce Pipeline for Lattice-based Evaluation and Its Application to SNOMED CT.

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Authors:  Christopher Ochs; James Geller; Yehoshua Perl; Mark A Musen
Journal:  J Biomed Inform       Date:  2016-06-23       Impact factor: 6.317

6.  Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

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7.  Consumers' Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites.

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8.  Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT.

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9.  Dissecting the Ambiguity of FMA Concept Names Using Taxonomy and Partonomy Structural Information.

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Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2013-03-18

10.  HuPSON: the human physiology simulation ontology.

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Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

3.  Leveraging non-lattice subgraphs for suggestion of new concepts for SNOMED CT.

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5.  Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia.

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6.  Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.

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7.  Architecture and usability of OntoKeeper, an ontology evaluation tool.

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8.  Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019).

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Review 10.  A review of auditing techniques for the Unified Medical Language System.

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