Architecture and usability of OntoKeeper, an ontology evaluation tool

Amith, Muhammad; Manion, Frank J.; Liang, Chunnian; Harris, Marcelline R.; Wang, Dennis; He, Yongqun; Tao, Cui

doi:10.1186/s12911-019-0859-z

Cited by 17 publications

(26 citation statements)

References 24 publications

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“…To attain an initial basic evaluation of FOAF+, we measured FOAF+ (minimal version) in comparison with other similar social network ontologies like VIVO (v.1.7) [23] and the original FOAF using semiotic metrics proposed by Burton-Jones and colleagues [24]. Table 1 presents the semiotic metrics produced by our automated tool (OntoKeeper-an iteration of our work [25,26]) that implements the aforementioned metrics-syntactic (lawfulness, richness), semantic (interpretability, consistency, clarity), pragmatic (comprehensiveness). The syntactic metric produces a measure that describes the quality of the syntax through the diverse use of ontological features or axiom types (richness) and minimal syntactic violations (lawfulness).…”

Section: Resultsmentioning

confidence: 99%

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

Amith

Fujimoto

Mauldin

et al. 2020

BMC Med Inform Decis Mak

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Background Dyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data. Methods The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope. Results Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data. Conclusion Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.

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Section: Resultsmentioning

confidence: 99%

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

Amith

Fujimoto

Mauldin

et al. 2020

BMC Med Inform Decis Mak

Self Cite

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“…Ontology evaluation frameworks by Duque-Ramos et al [24] and Gangemi et al [18] are well-known frameworks [25], which divide ontology evaluation criteria into three dimensions: structural, functional, and usability. There are also different tools for automatic evaluation of ontologies, such as OntoMetric [26], TOMM [27], Protégé [28], and OntoKeeper [29].…”

Section: Skos/ontology Quality Evaluation Metrics and Approachesmentioning

confidence: 99%

FAIR Ontologies for Transparent and Accountable AI: A Hospital Adverse Incidents Vocabulary Case Study

Basereh

Caputo

Brennan

2021

2021 Third International Conference on Transdisciplinary AI (TransAI)

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In this paper, the relation between the FAIR (Findable, Accessible, Interoperable, Reusable) ontologies and accountability and transparency of ontology-based AI systems is analysed. Also, governance-related gaps in ontology quality evaluation metrics were identified by examining their relation with FAIR principles and FAcct (Fairness, Accountability, Transparency) governance aspects. A simple SKOS vocabulary, titled "Hospital Adverse Incidents Classification Scheme" (HAICS) has been used as a use case for this study. Theoretically, we found that there is a straight relation between FAIR principles and FAccT AI, which means that FAIR ontologies enhance transparency and accountability in ontology-based AI systems. We suggest that "FAIRness" should be assessed as one of the ontology quality evaluation aspects.

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“…The community has proposed quantitative approaches based on metrics, which provide information about the engineering of the ontology [ 15 , 18 , 20 , 21 , 24 , 28 – 34 ]. For example, Yao et al [ 18 ], Tartir and Arpinar [ 20 , 21 ], and Lantow and Birger [ 26 ] defined a series of metrics for evaluating the structural properties in the ontology.…”

Section: State Of the Artmentioning

confidence: 99%

Analysis of readability and structural accuracy in SNOMED CT

Abad-Navarro

Quesada-Martínez

Duque-Ramos

et al. 2020

BMC Med Inform Decis Mak

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Background The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontologies, such as the Gene Ontology or SNOMED CT, have their own quality assurance processes. These have demonstrated their usefulness for the maintenance of the resources but are unable to detect all of the modelling flaws in the ontologies. Consequently, the development of efficient and effective quality assurance methods is needed. Methods Here, we propose a series of quantitative metrics based on the processing of the lexical regularities existing in the content of the ontology, to analyse readability and structural accuracy. The readability metrics account for the ratio of labels, descriptions, and synonyms associated with the ontology entities. The structural accuracy metrics evaluate how two ontology modelling best practices are followed: (1) lexically suggest locally define (LSLD), that is, if what is expressed in natural language for humans is available as logical axioms for machines; and (2) systematic naming, which accounts for the amount of label content of the classes in a given taxonomy shared. Results We applied the metrics to different versions of SNOMED CT. Both readability and structural accuracy metrics remained stable in time but could capture some changes in the modelling decisions in SNOMED CT. The value of the LSLD metric increased from 0.27 to 0.31, and the value of the systematic naming metric was around 0.17. We analysed the readability and structural accuracy in the SNOMED CT July 2019 release. The results showed that the fulfilment of the structural accuracy criteria varied among the SNOMED CT hierarchies. The value of the metrics for the hierarchies was in the range of 0–0.92 (LSLD) and 0.08–1 (systematic naming). We also identified the cases that did not meet the best practices. Conclusions We generated useful information about the engineering of the ontology, making the following contributions: (1) a set of readability metrics, (2) the use of lexical regularities to define structural accuracy metrics, and (3) the generation of quality assurance information for SNOMED CT.

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Architecture and usability of OntoKeeper, an ontology evaluation tool

Cited by 17 publications

References 24 publications

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

FAIR Ontologies for Transparent and Accountable AI: A Hospital Adverse Incidents Vocabulary Case Study

Analysis of readability and structural accuracy in SNOMED CT

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