Abstract
Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.
Highlights
The evolution of semantic web technologies and the growth of big data volumes maintained by various database models have resulted in many disparate and independent data sources [1]
Ontologies play an essential role in addressing semantic heterogeneity to achieve semantic interoperability among the various web applications and services [2]
We present a background knowledge (BK) multimatcher model to combine and aggregate the different mapping We present a BK multimatcher model to combine and aggregate the different mapalignments created by several automatic matchers, notably, LogMap, LogMapLt, and AML, ping alignments created by several automatic matchers, notably, LogMap, LogMapLt, and to enhance the final alignment
Summary
The evolution of semantic web technologies and the growth of big data volumes maintained by various database models have resulted in many disparate and independent data sources [1]. It is crucial to determine how traditional information systems can be transferred into more integrated systems. In this context, ontologies play an essential role in addressing semantic heterogeneity to achieve semantic interoperability among the various web applications and services [2]. Integrating and sharing data are still challenging because ontologies are semantically heterogeneous. The BK based matching or indirect matching approach or context based matching is the opposite of direct matching. It detects mappings between ontologies for alignment by taking advantage of external resources [14].
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