An Iterative Automatic Final Alignment Method in the Ontology Matching System

Abstract

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

“…However, setting up and configuring such systems with several matchers, combination methods, and individual parameter settings are difficult, even for specialists. The ontology matching community has already addressed these challenges when combining several similarity measures in the same matcher [6,27,38] and has provided several solutions [39][40][41].…”

DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching

“…To improve the performance of ontology matching, we combine the Greedy Matching algorithm with the Stable Marriage algorithm to reduce the runtime of ontology matching. Inspired by [26], we only find the highest correspondences in the semantic similarity matrix as the candidate alignments. A correspondence between concept c s of ontology O s and c t of ontology O t is the highest correspondence if and only if it has the smallest semantic distance than any other correspondence of either c s or c t with some other concept.…”

“…In this paper, we introduce an automatic adjustment of the threshold method instead of manual adjustment. Since the highest correspondences found in the first iteration are most likely to be the correct matching pairs, we use their semantic similarity to calculate the threshold t [26]. The threshold value is defined as:…”

“…That is, the larger the ontology, the more time it takes for the final alignment. Meanwhile, the adjustment of the threshold value is usually determined manually within matching systems considering the experience of developers, therefore it is not necessarily adjusted in an optimal way for each pair of compared ontologies [26].…”