OntoEnrich: A Platform for the Lexical Analysis of Ontologies

Quesada-Martínez, Manuel; Fernández-Breis, Jesualdo Tomás; Stevens, Robert; Aussenac-Gilles, Nathalie

doi:10.1007/978-3-319-17966-7_25

Cited by 4 publications

(4 citation statements)

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“…We have developed a software tool that provides a command line interface to compute the metrics of any set of ontologies (see https://github.com/fanavarro/huron). This tool was implemented in Java and uses the OWL API [43] for ontology parsing, the ELK reasoner [44] for axiom discovery, and the OntoEnrich framework for lexical regularity extraction [26]. The structural reasoner provided by the OWL API was used for ontologies that could not be processed by ELK.…”

Section: The Huron Software Toolmentioning

confidence: 99%

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HURON: A Quantitative Framework for Assessing Human Readability in Ontologies

Abad-Navarro,

Martínez-Costa,

Fernández-Breis

2023

IEEE Access

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The increasing use of ontologies requires their quality assurance. Ontology quality assurance consists of a set of activities that allow analyzing the ontology, identifying strengths and weaknesses, and proposing improvement actions. Human readability is a quality aspect that improves the use and reuse of ontologies. The human readable content refers to the natural language content consumed by humans and by the growing number of embedding methods applied to ontologies. The ontology community has proposed best practices related to human readability, but there is no standardized framework for its assessment. We aim to provide a framework for analyzing the human readability based on quantitative metrics for supporting ontology developers' decisions. We present the HURON framework, which consists of the specification of five quantitative metrics related to the human readability of ontology content, and a software tool to implement them. The metrics take into account the number of names, descriptions, or synonyms, and also assess the application of systematic naming conventions and the 'lexically suggest, lexically define' principle. Target values are provided for each metric to help to interpret the values. HURON can also be used to assess compliance with best practices. We have applied HURON to a representative set of biomedical ontologies, the OBO Foundry repository. The results showed that, in general, the OBO Foundry ontologies comply with the expected number of descriptions and names in their classes, and both lexical and semantically formalized contents are aligned. However, most of the ontologies did not follow a systematic naming convention. In general, the ontologies of this repository show adherence to some of the best practices, although areas of improvement were identified. A number of recommendations are made for ontology developers and users. INDEX TERMS knowledge engineering, ontologies, quality assurance, readability metrics, semantic web I. INTRODUCTIONOntologies play a key role in knowledge engineering by providing a common conceptualization of a domain. Ontologies have been successfully applied in different domains, but especially in biology and biomedicine, with different purposes [1][2][3][4][5].At the time of writing, repositories like BioPortal [6] had more than 1,000 ontologies and both the Open Biological and Biomedical Ontology (OBO) Foundry [7] and the Ontology Lookup Service (OLS) [8] had more than 250 ontologies. The number of ontologies in these repositories is continuously increasing, which demonstrates their relevance and impact.In contrast to other artifacts used in data management systems, such as relational databases, developed specifically for particular applications, ontologies should be created in a standardized way to facilitate their reuse. The sharing and reuse orientation of ontologies have also made them fundamental for obtaining Findable, Accessible, Interoperable, and Reusable (FAIR) datasets [9]. As a consequence, assuring the quality of ontologies has become a major need.

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Section: The Huron Software Toolmentioning

confidence: 99%

“…An example is the name of the concept 'left atrium endocardium', which can suggest a semantic relation with the concepts 'left' and 'endocardium'. This principle has been exploited for ontology analysis and axiom discovery [26,27].…”

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confidence: 99%

HURON: A Quantitative Framework for Assessing Human Readability in Ontologies

Abad-Navarro,

Martínez-Costa,

Fernández-Breis

2023

IEEE Access

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“…Ontology analysis is a well recognized and useful technique to facilitate ontology understanding and then support ontol-ogy reuse. There are a number of efforts that apply different criteria, addressing different features of ontologies, to extract ontology summaries [34][35][36]46,47,54,60]. The OntoEnrich web platform is used to analyze and detect ontology lexical regularities which might help the detection of hidden semantics.…”

Section: Ontology Analysismentioning

confidence: 99%

“…The OntoEnrich web platform is used to analyze and detect ontology lexical regularities which might help the detection of hidden semantics. The platform provides a series of useful observations about the structure and content of the concepts' labels, which can be helpful for the study of the engineering of the ontology and their axiomatic enrichment [46,47]. The framework proposed in [36] first loads and parses the input ontology and transforms it into a semantic derived model (SDM).…”

Section: Ontology Analysismentioning

confidence: 99%

Ontology Modularization with OAPT

et al. 2020

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Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool (OAPT). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.

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From lexical regularities to axiomatic patterns for the quality assurance of biomedical terminologies and ontologies

Damme

Quesada-Martínez

Cornet

et al. 2018

Journal of Biomedical Informatics

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Ontologies and terminologies have been identified as key resources for the achievement of semantic interoperability in biomedical domains. The development of ontologies is performed as a joint work by domain experts and knowledge engineers. The maintenance and auditing of these resources is also the responsibility of such experts, and this is usually a time-consuming, mostly manual task. Manual auditing is impractical and ineffective for most biomedical ontologies, especially for larger ones. An example is SNOMED CT, a key resource in many countries for codifying medical information. SNOMED CT contains more than 300000 concepts. Consequently its auditing requires the support of automatic methods. Many biomedical ontologies contain natural language content for humans and logical axioms for machines. The 'lexically suggest, logically define' principle means that there should be a relation between what is expressed in natural language and as logical axioms, and that such a relation should be useful for auditing and quality assurance. Besides, the meaning of this principle is that the natural language content for humans could be used to generate the logical axioms for the machines. In this work, we propose a method that combines lexical analysis and clustering techniques to (1) identify regularities in the natural language content of ontologies; (2) cluster, by similarity, labels exhibiting a regularity; (3) extract relevant information from those clusters; and (4) propose logical axioms for each cluster with the support of axiom templates. These logical axioms can then be evaluated with the existing axioms in the ontology to check their correctness and completeness, which are two fundamental objectives in auditing and quality assurance. In this paper, we describe the application of the method to two SNOMED CT modules, a 'congenital' module, obtained using concepts exhibiting the attribute Occurrence - Congenital, and a 'chronic' module, using concepts exhibiting the attribute Clinical course - Chronic. We obtained a precision and a recall of respectively 75% and 28% for the 'congenital' module, and 64% and 40% for the 'chronic' one. We consider these results to be promising, so our method can contribute to the support of content editors by using automatic methods for assuring the quality of biomedical ontologies and terminologies.

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OntoEnrich: A Platform for the Lexical Analysis of Ontologies

Cited by 4 publications

References 4 publications

HURON: A Quantitative Framework for Assessing Human Readability in Ontologies

HURON: A Quantitative Framework for Assessing Human Readability in Ontologies

Ontology Modularization with OAPT

From lexical regularities to axiomatic patterns for the quality assurance of biomedical terminologies and ontologies

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