Towards ensuring Satisfiability of Merged Ontology

Fahad, Muhammad; Moalla, Néjib; Bouras, Abdelaziz

doi:10.1016/j.procs.2011.04.242

Cited by 11 publications

(13 citation statements)

References 13 publications

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“…Fahad et al (2011) have exhibited a system of naturally recognizing semantic irregularities in the early stages of ontology merging. In this way, the ontology is free from 'common class/occurrence between disjoint classes mistake,' 'excess of disjoint relations' 'repetition of subclass/sub property relations,' 'circulatory error in class/property progressive system,' and different kinds of 'semantic irregularity' errors.…”

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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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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“…They performed corresponding combinatorial optimization problems for the identification of alignments. Both tools, LogMap and CODI, resemble with the pre-integration phase of our tool DKP-AOM (Fahad et al 2011(Fahad et al , 2012 because of the consistency and satisfiability analysis during the mapping of concepts between ontologies. But, we also took the initiative of merging the consistent candidate mappings to produce a new merged ontology, which is considered the main contribution of this paper.…”

Section: Comparison With Ontology Matchers From Oaei 2012mentioning

confidence: 99%

“…Regardless of few ontology mapping systems (Jiménez-Ruiz and Cuenca-Grau 2011; Huber et al 2011), current ontology merging systems do not follow appropriate mechanisms for achieving the accuracy, consistency and conciseness of the merged ontology (Fahad et al 2011). Semi-automatic systems (such as Prompt Noy and Musen 2003, Chimaera McGuinness et al 2000, MoA Dou et al 2002 depend on end-users for the validation of mappings and need human intervention in the decision making process of merging concepts.…”

Section: Introductionmentioning

confidence: 99%

“…These systems benefit from the merging of light-weight ontologies and end-users can only merge their class hierarchies. For the former limitation, in our previous research, we contributed and embedded a Quality Mechanism (based on Consistency, Completeness and Conciseness) inside an automatic merging system DKP-AOM that captures all the information from the input ontologies and provides a full merge solution (Fahad et al 2011(Fahad et al , 2012. The embedded quality criteria remove the load of validation on an end-user and ontology merging becomes facilitated with automatic validation and verification of the resultant output.…”

Section: Introductionmentioning

confidence: 99%

“…The embedded quality criteria remove the load of validation on an end-user and ontology merging becomes facilitated with automatic validation and verification of the resultant output. The proposed system highlights the contradictory elements between the ontologies, however, this aspect is out of the scope of this paper and can be found in Fahad et al (2011Fahad et al ( , 2012. For the latter limitation, in this paper we elaborate our mapping criteria for the detection of all possible mapping pairs by analysing their class expressions (i.e., axiomatic definitions) of concepts and then merge their individual axiomatic expressions to form a combined expression automatically to improve the effectiveness of our merging system DKP-AOM.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Merging of axiomatic definitions of concepts in the complex OWL ontologies

Fahad

2016

Artif Intell Rev

Self Cite

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In the last decade, ontology matching and mapping research has shown a measurable progress. This topic draws substantial attention within the research community, though it is not fully researched so far and new complex and effective solutions are needed. Current works are limited in finding alignments or mappings between concepts of heterogeneous ontologies. But, once ontology mappings are found, then how they (or their class expressions) are to be integrated automatically is left open for the ontology merging research. This paper elaborates the mapping of class expressions of concepts and contributes an algorithm for their merging in an automatic ontology merging process without any human intervention. However, the challenge of mapping axiomatic definitions is the most difficult task for merging concept definitions of the source ontologies, but it reveals significant increase in precision and recall values. In addition, with the study of these algorithms, we conclude that ontology merging facilitates when one wants to get ontology with the better quality as the combined rich axioms are added in the merged ontology. We also discuss the results of our first successful participation in the Conference, OA4QA and Anatomy track of OAEI 2015.

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Ensuring safe and consistent coengineering of cyber‐physical production systems: A case study

Tröls

Mashkoor

Demuth

et al. 2020

J Software Evolu Process

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In today's engineering projects, companies continuously have to adapt their systems to changing customers or dynamic market requirements. This requires a flexible, iterative development process in which different parts of the system under construction are built and updated concurrently. However, concurrent engineering becomes quite challenging in domains where different engineering artifacts from different disciplines come into play, such as safety‐critical cyber‐physical systems, where the involved engineering artifacts are quite heterogeneous in nature. In such systems, it is of utmost importance that different artifacts remain consistent in order to guarantee a correctly functioning end product. In this article, we discuss our experiences (with a leading company working in the areas of production automation and product processing) in maintaining the consistency between electrical models and the corresponding software controller, when both are subject to continuous changes. The article discusses how we let engineers describe the relationships between electrical models and the corresponding software controller code in the form of links and consistency rules. Additionally, we demonstrate that how our approach, through a process of continuous consistency checking, notifies engineers about the erroneous impact of their changes in various engineering artifacts.

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Towards ensuring Satisfiability of Merged Ontology

Cited by 11 publications

References 13 publications

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

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

Merging of axiomatic definitions of concepts in the complex OWL ontologies

Ensuring safe and consistent coengineering of cyber‐physical production systems: A case study

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