A Benchmark for Ontologies Merging Assessment

Abstract

In the last years, ontology modeling became popular and thousands of ontologies covering multiple fields of application are now available. However, as multiple ontologies might be available on the same or related domain, there is an urgent need for tools to compare, match, merge and assess ontologies. Ontology matching, which consists in aligning ontology, has been widely studied and benchmarks exist to evaluate the different matching methods. However, somewhat surprisingly, there are no significant benchmarks for merging ontologies, proving input ontologies and the resulting merged ontology. To fill this gap, we propose a benchmark for ontologies merging, which contains different ontologies types, for instance: taxonomies, lightweight ontologies, heavyweight ontologies and multilingual ontologies. We also show how the GROM tool (Graph Rewriting for Ontology Merging) can address the merging process and we evaluate it based on coverage, redundancy and coherence metrics. We performed experiments and show that the tool obtained good results in terms of redundancy and coherence.

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

“…At the same time, we can observe that average matching time is improve using our proposed approach 20% and 28% respectively for agriculture and conference domain. Table 5 is presenting ontology merging result using standard benchmarking parameter [25,47] like completeness, compactness, and redundancy. Apart from that we also measure average merging time of ontology pair for existing GROM tool vs. our algorithm applied to GROM tool for merging multiple ontologies.…”

“…Madhfoudh et al [47] introduced a measure for merging of ontologies that specify different ontologies types: scientific classifications, lightweight ontologies, heavyweight ontologies and multilingual ontologies. They propose a benchmark for ontologies merging, which contains different ontologies sorts.…”

“…That will help users to merge multiple ontologies on single click, using this average approach time of merging two ontology. We have used benchmarking technique present in [24,25,46,47].…”

“…Equations related to all these three metrics are explained in the paper [25,47].Coverage means the degree to which source and target ontology concepts are preserved in merge result ontology which is between 0 and 1. Coverage 1 means cover all concepts of input ontology, and 0 means no input ontology concepts covered.…”

Ontology Integration: Approaches and Challenging Issues

“…There have been some attempts to produce benchmarks for ontology integration. The first existing benchmark [27] is not published and thus cannot be used, whereas the second one [119] is composed of only very small ontologies. Both proposed benchmarks result from an automatic asymmetric merge of two input ontologies.…”