Similarity as a Quality Indicator in Ontology Engineering

International journal of Web & Semantic Technology

In ontology engineering, there are many cases where assessing similarity between ontologies is required, this is the case of the alignment activities, ontology evolutions, ontology similarities, etc. This paper presents a new method for assessing similarity between concepts of ontologies. The method is based on the set theory, edges and feature similarity. We first determine the set of concepts that is shared by two ontologies and the sets of concepts that are different from them. Then, we evaluate the average value of similarity for each set by using edges-based semantic similarity. Finally, we compute similarity between ontologies by using average values of each set and by using feature-based similarity measure too.

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Semantics and Ontologies - SSO '11, 2011

Semantic similarity measurement has been an active research area in GIScience and the Semantic Web for many years. However, implementations of these measures were largely missing, not publicly available, or tailored to specific application needs. To foster the application of similarity reasoning in information retrieval, ontology engineering, and spatial decision support, we implemented the SIM-DL semantic similarity server as well as a plug-in for the popular Protégé ontology editor. While SIM-DL has been successfully applied to several application areas, the implemented similarity theory was largely structural, could not handle concept and instance similarity within the same framework, and was based on a Protégé version and DIG interface that have been re-engineered over the last years. This paper introduces a new version, called SIM-DLA, engineered from scratch to addresses these shortcomings. It is based on our new similarity theory, can handle inter-instance and interconcept similarity using the same functions and alignments, and is available for the new Protégé version 4.1.

European Conference on Information Systems, 2005

In this paper we present a comprehensive framework for measuring similarity within and between ontologies as a basis for the collaboration across various application fields. In order to define such a framework, we base our work on an abstract ontology model that allows to adhere to various existing and evolv- ing ontology standards. The main characteristics of the framework is

ASK-IT final …, 2008

One of the main stages of the ontology authoring process is the adoption of the appropriate specifications to ensure that the ontology structure and design adheres to a set of common best practices. Existing ontology authoring tools on the one hand, mainly support ontology editing operations as well as visualization facilities. These tools allow the design and implementation of new ontologies but without enforcing restrictions arising from currently known best practices for ontology authoring techniques. Reasoners on the other hand are useful for automatic checking of the logical consistency of ontologies, by performing operations such as subsumption, equivalence and instantiation checking, but assume that the ontology has been properly structured in the first place in order to produce valid results. During the ontology authoring process, a set of conditions should be applied to ensure process validity. These conditions are used to check the degree of completeness of fulfillment of the following criteria: concept and property hierarchy, module subtraction, documentation and visualization, definition of ranges for property values, disjointness restrictions and adherence to naming conventions. This paper presents a set of criteria to ensure ontology validation and introduces a methodological approach for the application of these criteria on existing ontologies. We then demonstrate the application of the proposed methodology through a specific case study. This concerns the refinement of six ontologies which have been developed in the context of the integrated project ASK-IT.

Theory and Applications of Ontology: Computer Applications, 2010

Ontologies are key to information retrieval, semantic integration of datasets, and semantic similarity analyses. Evaluating ontologies (especially defi€ning what constitutes a “good” or “better” ontology) is therefore of central importance for the Semantic Web community. Various criteria have been introduced in the literature to evaluate ontologies, and this article classi€fies them according to their relevance to the design or the implementation phase of ontology development. In addition, the article compiles strategies for ontology evaluation based on ontologies published until 2017 in two outlets: the Semantic Web Journal, and the Journal of Web Semantics. Gaps and opportunities for future research on ontology evaluation are exposed towards the end of the paper.

First Monday, 2007

Ontologies are important knowledge representation and sharing tools in the workflow of biology research. The high cost of creating and maintaining ontologies encourages their sharing and reuse, and an increasing number of ontologies have been made available from different sources, with different models of curation. To enable effective selection, reuse, integration, and maintenance of these ontologies, however, one needs to have a systematic method of evaluating and connecting their quality to the context of an intended use. Based on an analysis of the activity system and Web server logs of the Morphbank biodiversity research data repository (http://www.morphbank.net/), a model was developed to evaluate ontology quality. The model connects the types of quality problems with the types of research activities and suggests relevant metrics. The paper also describes the structure of some of the research activities and the types and patterns of end-user searches in Morphbank. Contents Introduction Related research Research setup and method Analysis Constructing a quality evaluation model Conclusion

The emphasis of reusing domain knowledge in system development makes ontology the most important disciplines in the information system industry at present. Many domain specific ontology editors are currently available for developing ontologies. However, selecting the most appropriate editor is a challenge because each ontology construction initiative requires its own budget, time, and resources. Therefore, to overcome this challenge, we propose a criterion to evaluate ontology construction tools. We define evaluation criteria such as functionality, reusability, data storage, complexity, association, scalability, resilience, reliability, robustness, learnability, availability, efficiency, and visibility. Then, on the basis of criteria, we propose a technique to rank ontology tools and finally, we evaluate three ontology construction editors based on the ranking technique.

2006

The paper proposes a framework to assess the semantic similarity among instances within an ontology. It aims to define a sensitive measurement of semantic similarity, which takes into account different hints hidden in the ontology definition and explicitly considers the application context. The similarity measurement is computed by combining and extending existing similarity measures and tailoring them according to the criteria induced by the context. Experiments and evaluation of the similarity assessment are provided.

Semantic Web, 2006

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account ...