Ontology Matching with Knowledge Rules

Abstract

Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves substantially better F-score than the previous state-of-the-art on three ontology matching domains.

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

“…In the second group, the approaches do not rely on correspondence patterns and do not require instance-level knowledge: KAOM (Knowledge-Aware Ontology Matching) [13] is a system which uses Markov Logic Networks as a probabilistic framework for ontology matching. The Markov Logic formulae use the entities of the two ontologies (source and target) as constants, the relations between entities and the input knowledge rules as evidence.…”

“…Different approaches for generating such complex alignments have been proposed in the literature. While the proposal of [23,24] relies on correspondence patterns, the one in [13] uses knowledge-rules in Markov-Logic Networks. Those in [20,21,35] rely on statistical methods and correspondence patterns and the one in [18] deals with genetic programming.…”

“…Here we extend the work presented in [33] and in [31] by enriching the alignment sets with new pairs of ontologies and by considering the task for which the alignment is needed. We also extend the work in [31] and by adding an evaluation of three systems [23,24,13]. We extend the evaluation of the work in [33] by adding a new system described in [13] and by evaluating all the three systems on the ten pairs of ontologies for each alignment set.The paper is organised as follows.…”

“…We also extend the work in [31] and by adding an evaluation of three systems [23,24,13]. We extend the evaluation of the work in [33] by adding a new system described in [13] and by evaluating all the three systems on the ten pairs of ontologies for each alignment set.The paper is organised as follows. After giving the background on ontology matching ( §2) and discussing related work ( §3), we describe the methodology to create the alignments ( §4), the alignments themselves and their use for the evaluation of approaches ( §5).…”

“…We also extend the work in [31] and by adding an evaluation of three systems [23,24,13]. We extend the evaluation of the work in [33] by adding a new system described in [13] and by evaluating all the three systems on the ten pairs of ontologies for each alignment set.…”