Empirical Merging of Ontologies — A Proposal of Universal Uncertainty Representation Framework

Nováček, Vít; Smrž, Pavel

doi:10.1007/11762256_8

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Techniques that PROMPT uses to direct a client to the following conceivable purpose of blending or arrangement, to recommend what activities ought to be performed there, and to perform specific activities naturally. Nováček and Smrž [38] presents a novel portrayal of indeterminate learning in the space of programmed ontology securing that contains help for Semantic Web applications. The primary target of the ontology procurement stage OLE is to execute a framework that can consequently make and refreshing space specific ontologies for a given area of the scientific learning.…”

Section: Ontology Mergingmentioning

confidence: 99%

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

Makwana¹,

Ganatra²

2018

IJIES

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A significant aspect of any system is how we present the knowledge. Ontology is one of the methods to present shared knowledge of the particular domain. Ontology can be designed and developed on specific domain or subdomain by many groups and researchers, which will create heterogeneity. To solve this problem ontology integration is necessary. To integrate shared knowledge, we require calculating the similarity between two ontologies, selected from a corpus using ontology matching techniques. Here we define a procedure to create a group of ontologies. Ontology comparison can be made using tools to find similar classes. As a similarity measure, Jaccard Similarity Index (JSI) is used create a group of ontology by an algorithm named k means. For each cluster, we generate buckets of ontologies, and from the bucket, all ontologies are grouped in one ontology reducing the attempt of exploring in enquiring understanding in multiple ontologies. Here we have check performance of ontology matching; clustering and merging algorithms script using standard benchmarking techniques of agriculture domain and conference track. Also, compare response time performance of SPARQL (SPARQL Protocol and RDF Query Language) query on merged ontology using this method. For experimentation, we have selected ontology corpus from agriculture domain and conference tack domain of OAEI (Ontology Alignment Evaluation Initiative). At the end of experimentation through our proposed approach we could achieve improvement in average loading and match time in ontology matching process compare to an existing tool. Also, we could achieve significance result in ontology merging process though benchmark parameter of coverage, compactness, and average merge time of two ontologies. Finally, in SPARQL query experimentation, we got success in improvement in reducing search space and response time of the query.

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Section: Ontology Mergingmentioning

confidence: 99%

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

Makwana¹,

Ganatra²

2018

IJIES

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“…Tafazzoli and Sadjadi (2008) developed a framework to generate malware fuzzy ontologies from malwares. Nováček and Smrž (2006) introduced a novel representation of uncertain knowledge in the domain of automatic ontology acquisition. Nováček and Smrž (2006) introduced a novel representation of uncertain knowledge in the domain of automatic ontology acquisition.…”

Section: Construction Of Fuzzy Ontologiesmentioning

confidence: 99%

“…The detailed introduction about ontologies can be found in Chandrasekaran et al . (1999), Nieto (2003), and Ding et al . (2005).…”

Section: Ontologies and Fuzzy Set Theorymentioning

confidence: 99%

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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“…A limited number of approaches were proposed for merging ontologies. They can be classified into two main categories: the approaches based on semantic web technologies [24,25,26,27,28] and the approaches based on algebraic specification and Category Theory [29,5,7].…”

Section: Related Workmentioning

confidence: 99%

Algebraic Graph Transformations for Merging Ontologies

Mahfoudh

Thiry

Forestier

et al. 2014

Model and Data Engineering

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The conception of an ontology is a complex task influenced by numerous factors like the point of view of the authors or the level of details. Consequently, several ontologies have been developed to model identical or related domains leading to partially overlapping representations. This divergence of conceptualization requires the study of ontologies merging in order to create a common repository of knowledge and integrate various sources of information. In this paper, we propose a formal approach for merging ontologies using typed graph grammars. This method relies on the algebraic approach to graph transformations, SPO (Simple PushOut) which allows a formal representation and ensures the consistence of the results. Furthermore, a new ontologies merging algorithm called GROM (Graph Rewriting for Ontology Merging) is presented.

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Empirical Merging of Ontologies — A Proposal of Universal Uncertainty Representation Framework

Cited by 7 publications

References 14 publications

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

A survey on fuzzy ontologies for the Semantic Web

Algebraic Graph Transformations for Merging Ontologies

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