The Use of Foundational Ontologies in Ontology Development: An Empirical Assessment

Keet, C. Maria

doi:10.1007/978-3-642-21034-1_22

Cited by 36 publications

(29 citation statements)

References 16 publications

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“…It is known one can improve on one's low precision-i.e., the ontology admits more models than it should-by using a more expressive language and adding more class expressions, but this is easier said than done (except for the new tool to add more disjointness axioms (Ferré and Rudolph, 2012)). For domain ontologies, another option that influences to notion of being wellknown and mature is its linking to a foundational ontology and that therewith less modelling issues occur (Keet, 2011), but this has to do with the knowledge that is represented, not with, e.g., language feature misunderstandings. We leave a more detailed investigation in this direction for future works.…”

Section: Discussionmentioning

confidence: 99%

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The Current Landscape of Pitfalls in Ontologies

Keet

Suárez-Figueroa

Poveda‐Villalón

2013

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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Abstract:A growing number of ontologies are already available thanks to development initiatives in many different fields. In such ontology developments, developers must tackle a wide range of difficulties and handicaps, which can result in the appearance of anomalies in the resulting ontologies. Therefore, ontology evaluation plays a key role in ontology development projects. OOPS! is an on-line tool that automatically detects pitfalls, considered as potential errors or problems, and thus may help ontology developers to improve their ontologies. To gain insight in the existence of pitfalls and to assess whether there are differences among ontologies developed by novices, a random set of already scanned ontologies, and existing well-known ones, data of 406 OWL ontologies were analysed on OOPS!'s 21 pitfalls, of which 24 ontologies were also examined manually on the detected pitfalls. The various analyses performed show only minor differences between the three sets of ontologies, therewith providing a general landscape of pitfalls in ontologies.

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Section: Discussionmentioning

confidence: 99%

“…Distinguishing between file name and URI. This is related to naming issues where the .owl file has a meaningful name, but the ontology URI has a different name (also observed in (Keet, 2011)). C3.…”

Section: Candidate Pitfallsmentioning

confidence: 99%

The Current Landscape of Pitfalls in Ontologies

Keet

Suárez-Figueroa

Poveda‐Villalón

2013

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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“…Effectively, our approach is defined and specified by the following statements: 1) Modularize the ontology into small reusable modules or fragments (upper, core, domain and domain-specific) which are themselves ontologies. 2) Use of foundational ontologies, from the start of the ontology building process, to facilitate the ontology development by preventing to reinvent the wheel concerning basic categories and relations [14] and to improve overall quality and semantic interoperability of conceptual models [15]. 3) Use of legal-core ontologies, such as LKIF-Core [7], in the development process of the ontology specifically for modeling the core module.…”

Section: Proposed Middle-out Approachmentioning

confidence: 99%

Towards a Middle-out Approach for Building Legal Domain Reference Ontology

Ghosh¹,

Naja²,

Abdulrab³

et al. 2016

IJKE

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Abstract-This article presents a middle-out approach to build legal domain reference ontology for a Legal Knowledge Based System (LKBS). The proposed approach is a combination of top-down and bottom-up strategies. In particular, we propose to develop legal domain reference ontology, splitted into modules or fragments, based on merging two processes: Conceptual Modeling Process, by reusing foundational ontologies (top-down strategy) and Ontology Learning Process from textual resources (bottom-up strategy).Index Terms-Conceptual modeling, domain reference ontology, legal ontology, modularization, ontology learning.

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“…There are multiple good reasons to use a foundational ontology in theory, and it has been shown to improve the ontology quality, understandability, and interoperability in praxis [17]. It comes at the 'cost' for figuring out how to align a domain ontology with it, and it can have implications for the language used for the overall ontology.…”

Section: Alignment Of Dmop With a Foundational Ontologymentioning

confidence: 99%

The Data Mining OPtimization Ontology

et al. 2015

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The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used as a reference by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

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The Use of Foundational Ontologies in Ontology Development: An Empirical Assessment

Cited by 36 publications

References 16 publications

The Current Landscape of Pitfalls in Ontologies

The Current Landscape of Pitfalls in Ontologies

Towards a Middle-out Approach for Building Legal Domain Reference Ontology

The Data Mining OPtimization Ontology

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