Consistent Ontologies Evolution Using Graph Grammars

Mahfoudh, Mariem; Forestier, Germain; Thiry, Laurent; Hassenforder, Michel

doi:10.1007/978-3-642-39787-5_6

Cited by 5 publications

(11 citation statements)

References 19 publications

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“…In our previous work [18], we distinguished the ontology changes considering the classification proposed by Klein et al [11]. Indeed, this classification of basic/elementary and composite/complex changes is based on the user's vision and does not take into consideration the system's vision.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous work [18], we have introduced the formalization of the ontology changes with Typed Graph Grammars and have focused on the atomic changes. A deeper study is presented in this paper which presents an exhaustive list of the atomic ontology changes and describes how consistently formalize the composite and complex changes.…”

Section: Introductionmentioning

confidence: 99%

“…They are used in several fields of computer science such as software systems modeling, pattern recognition and formal language theory [14]. Recently, they started to be used in the ontology field, in particular for the modular ontologies formalization [15], Resource Description Framework graphs representation [16], collaborative ontologies evolution [17] and consistent ontologies evolution [18].…”

Section: Introductionmentioning

confidence: 99%

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Algebraic graph transformations for formalizing ontology changes and evolving ontologies

Mahfoudh

Forestier

Thiry

et al. 2015

Knowledge-Based Systems

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a b s t r a c tAn ontology represents a consensus on the representation of the concepts and axioms of a given domain. This consensus is often reached through an iterative process, each iteration consisting in modifying the current version of the consensus. Furthermore, frequent and continuous changes are also occurring when the represented domain evolves or when new requirements have to be considered. Consequently, ontologies have to be adaptable to handle evolution, revision and refinement. However, this process is highly challenging as it is often difficult to understand all affected ontology parts when changes are performed. Thus, inconsistencies can occur in the ontology as the changes can introduce contradictory axioms. To address this issue, this paper presents a formal approach for evolving ontologies using Typed Graph Grammars. This method relies on the algebraic approach Simple PushOut (SPO) of graph transformations. It formalizes the ontology changes and proposes an a priori approach of inconsistencies resolution. The modified ontology does not need an explicit checking as an incorrect ontology version cannot actually be generated. To validate our proposal, an implementation is presented using the Attributed Graph Grammar (AGG) toolbox.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Algebraic graph transformations for formalizing ontology changes and evolving ontologies

Mahfoudh

Forestier

Thiry

et al. 2015

Knowledge-Based Systems

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“…They are used in several fields of computer science such as software systems modelling, pattern recognition and formal language theory [4]. Recently, they started to be used in the ontology field, in particular for the formalization of the operations on ontologies like the alignment, merge and evolution [5,6,7,8,9,10]. In our previous work [10], we used T GG to formalize and implement ontology changes.…”

Section: Introductionmentioning

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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“…GROM is another apparatus that is actualized here and created a worldwide ontology from given two ontologies and their mapping in a planned manner. A paper by (Mahfoudh et al, 2013) introduced the utilization of the graph grammars to approve and apply the ontology changes. The Algebraic Graph Grammar (AGG) apparatus is utilized to show system made out of various diagram-reworking rules and to systematize the forward and backward procedure of change of the ontologies to diagrams.…”

Section: Ontology Mergingmentioning

confidence: 99%

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Makwana

Ganatra

2018

Journal of Computer Science

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Knowledge representation is a crucial area of work in the intelligent system, especially in query answering system development. Ontology is used to represent shared knowledge of a particular domain for query answering system. Domain-specific ontology can be designed and developed by many groups and researchers, because of which there is heterogeneity in the knowledgebase. Ontology integration or merging is necessary in order to solve this problem of mixed knowledge. Finding similarity between two ontologies is crucial to achieve integration or merging of ontology. In this study, we present a method to generate a cluster of ontologies using global similarity measure of two ontologies. Ontology matching tools are used to find matched classes between two ontologies. Output of ontology matching tool is mapping between two ontologies and is used for generating clusters of ontology. We use Jaccard Similarity Index as a global similarity measure for clustering. Based on this measure, the popular k-means clustering algorithm is used to perform clustering of ontologies. Bins of ontologies are generated from each cluster. From each bin, all ontologies are finally merged into a single ontology, which helps us in reducing search effort in querying knowledge in query processing. The outcome of this research paper to provide better solution for merging ontology. Here, we use agriculture domain ontology corpus from the standard dataset for experimentation.

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Consistent Ontologies Evolution Using Graph Grammars

Cited by 5 publications

References 19 publications

Algebraic graph transformations for formalizing ontology changes and evolving ontologies

Algebraic graph transformations for formalizing ontology changes and evolving ontologies

Algebraic Graph Transformations for Merging Ontologies

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

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