Abstract
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.
Highlights
For developing semantic web-based applications (Hitzler et al, 2011; Fensel et al, 2005) ontologies are widely accepted as a means for providing a shared understanding of common domains
From the ontology matching tool, we found the total number of mappings, that is M, which is equal to the summation of the number of class map Cm, number of properties map Pm and number of individual map Imbetween source ontology and target ontology
We presented a better approach for ontology integration
Summary
For developing semantic web-based applications (Hitzler et al, 2011; Fensel et al, 2005) ontologies are widely accepted as a means for providing a shared understanding of common domains. Ontologies are designed and developed by different people or groups with different context and usage This creates heterogeneity in knowledge represented by ontologies about the same domain. Defining and quantifying these measures is a crucial problem of ontology matching where many research groups have contributed to the domain of semantic web in the past This ontology matching and alignment can be very useful in merging knowledge presented in the ontology. Challenges to perform heterogeneous knowledge integration and merging motivate in developing a technique for ontology clustering based on ontology matching results. Domain-specific knowledge is present in multiple ontologies and it is required to merge various heterogeneous ontologies into one in order to obtain complete knowledge For this purpose, use of ontology clustering, merging and matching is essential. Ontologies in each bin are subsequently merged into single global ontology
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