On the similarity relation within fuzzy ontology components (original) (raw)

2007 IEEE International Fuzzy Systems Conference, 2007

Ontology reuse is an important research issue. Ontology merging, integration, mapping, alignment and versioning are some of its subprocesses. A considerable research work has been conducted on them. One common issue to these subprocesses is the problem of defining similarity relations among ontologies components. Crisp ontologies become less suitable in all domains in which the concepts to be represented have vague, uncertain and imprecise definitions. Fuzzy ontologies are developed to cope with these aspects. They are equally concerned with the problem of ontology reuse. Defining similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontologies components or may be deduced from their intentional definitions. The latter approach needs to be dealt with differently in crisp and fuzzy ontologies. This is the scope of this paper.

Resolution of conflicts among ontology mappings: a fuzzy approach

The 7th International …, 2008

Ontology matching is a key interoperability enabler for the semantic web, since it takes the ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, query answering, data translation, or for navigation on the semantic web. Thus, matching ontologies allows the knowledge and data expressed in the matched ontologies to interoperate.

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.

A Fuzzy Rule-Based System for Ontology Mapping

Lecture Notes in Computer Science, 2009

Ontologies are a crucial tool for formally specifying the vocabulary and the concepts of agent platforms, so, to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. The treatment of uncertainty plays a key role in the ontology mapping, as the degree of overlapping between concepts can not be represented logically. This paper aims to provide mechanisms to support experts in the first steps of the ontology mapping process using fuzzy logic techniques to determine the similarity between concepts from different ontologies. For each pair of concepts, two types of similarity are calculated: the first using the Jaccard coefficient, based on relevant documents taken from the web, and the second based on the linguistic relationship of concepts. Finally, the similarity is calculated through a fuzzy rule-based system. The ideas presented in this work are validated using two real-world ontologies.

FuzzyAlign - A Fuzzy Method for Ontology Alignment

Proceedings of the International Conference on Knowledge Engineering and Ontology Development, 2012

The need of sharing information and services makes data integration as one of the most requested issues in the Semantic Web. Ontologies are crucial for formally specifying the vocabulary and the concepts within a domain, so, for better interoperability is important to translate data from one ontological framework to another. Ontology matching is the process of finding correspondences between the concepts of different ontologies. This problem is being addressed in many studies but has not managed to automate the matching process fully considering all the complex structure of the ontologies. This paper aims to provide mechanisms to support experts in the ontology matching process by using fuzzy logic techniques to determine the similarity between entities from different ontologies. We propose FuzzyAlign, a Multi-Layer fuzzy rule-based system, which obtains the alignments by taking into account both the lexical and semantic elements of names, and the relational and the internal structures of the ontologies to obtain the alignments. The ideas presented in this work were validated using the OAEI evaluation tests for ontology alignment systems in which we have obtained good results.

Fuzzy Clustering based Approach for Ontology Alignment

Proceedings of the 18th International Conference on Enterprise Information Systems, 2016

Recently, several ontologies have been proposed for real life domains, where these propositions are large and voluminous due to the complexity of the domain. Consequently, Ontology Aligning has been attracting a great deal of interest in order to establish interoperability between heterogeneous applications. Although, this research has been addressed, most of existing approaches do not well capture suitable correspondences when the size and structure vary vastly across ontologies. Addressing this issue, we propose in this paper a fuzzy clustering based alignment approach which consists on improving the ontological structure organization. The basic idea is to perform the fuzzy clustering technique over the ontology's concepts in order to create clusters of similar concepts with estimation of medoids and membership degrees. The uncertainty is due to the fact that a concept has multiple attributes so to be assigned to different classes simultaneously. Then, the ontologies are aligned based on the generated fuzzy clusters with the use of different similarity techniques to discover correspondences between conceptual entities.

Fuzzy ontologies in semantic similarity measures

2016 IEEE Congress on Evolutionary Computation (CEC), 2016

Ontologies are a fundamental part of the development of short text semantic similarity measures. The most known ontology used within the field was developed from the lexical database known as WordNet which is used as a semantic resource for determining word similarity using the semantic distance between words. The original WordNet does not include in its hierarchy fuzzy words-those which are subjective to humans and often context dependent. The recent development of fuzzy semantic similarity measures requires research into the development of different ontological structures which are suitable for the representation of fuzzy categories of words where quantification of words is undertaken by human participations. This paper proposes two different fuzzy ontology structures which are based on a human quantified scale for a collection of fuzzy words across six fuzzy categories. The methodology of ontology creation utilizes human participants to populate fuzzy categories and quantify fuzzy words. Each ontology is evaluated within a known fuzzy semantic similarity measure and experiments are conducted using human participants and two benchmark fuzzy word datasets. Correlations with human similarity ratings show only one ontological structure was naturally representative of human perceptions of fuzzy words.

Description logic-based knowledge merging for concrete- and fuzzy-domain ontologies

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2015

Enterprises, especially virtual enterprises (VEs) are nowadays getting more knowledge intensive and adopting efficient Knowledge management (KM) systems to boost their competitiveness. The major challenge for KM for VEs is to acquire, extract and integrate new knowledge with the existing source. Ontologies have been proved to be one of the best tools for representing knowledge with class, role and other characteristics. It is imperative to accommodate the new knowledge in the current ontologies with logical consistencies as it is tedious and costly to construct new ontologies every time after acquiring new knowledge. This paper introduces a mechanism and a process to integrate new knowledge in to the current system (ontology). Separate methods have been adopted for fuzzy and concrete domain ontologies. The process starts by finding the semantic and structural similarities between the concepts using Wordnet and Description logic (DL). DL-based reasoning is used next to determine the position and relationships between the incoming and existing knowledge. The experimental results provided show the efficacy of the proposed Method.

On the similarity relation within fuzzy ontology components (original) (raw)

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