Solving Ontology Meta-Matching Problem Through an Evolutionary Algorithm With Approximate Evaluation Indicators and Adaptive Selection Pressure

Qing Lv,Chengcai Jiang,He Li

doi:10.1109/access.2020.3047875

Qing Lv, Chengcai Jiang + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3047875

Copy DOI

Abstract

Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication. As an effective method, ontology matching can address the problem above, whose core technique is the similarity measure. A single similarity measure calculates the similarity value about a feature between two concepts, but none of the similarity measures can ensure their effectiveness in all context due to the diverse heterogeneous features between two ontologies. Therefore, multiple similarity measures are usually aggregated to improve the result's confidence. The problem that how to determine the optimal aggregating weights for the different similarity measures to obtain a high-quality alignment is called the meta-matching problem of ontology, which is modeled as a nonlinear problem with many local optimal solutions. Evolutionary Algorithm (EA) can represent an efficient methodology to address the ontology meta-matching problem, but EA-based ontology matching techniques suffer from the premature convergence and the requirement of a reference alignment to evaluate the solutions. To overcome the defects mentioned above, in this work, an improved EA-based matching approach is proposed, where two approximate evaluation indicators, i.e. pseudo-recall and pseudo-precision, are presented to evaluate the solution's quality, and an adaptive selection pressure is utilized to overcome the premature convergence. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)'s benchmark, and the experimental results will prove the effectiveness of our proposed method.

Highlights

Ontology provides a fundamental framework to make data and knowledge shared and exchanged effectively in many applications, such as Internet of Things (IoT) and Cloud Computing (CC) [1]
The well-known benchmark provided by the Ontology Alignment Evaluation Initiative OAEI2 are used
Each dataset in the OAEI benchmark consists of two ontologies to be matched and a reference alignment which used to assess matching quality

Summary

Introduction

Ontology provides a fundamental framework to make data and knowledge shared and exchanged effectively in many applications, such as Internet of Things (IoT) and Cloud Computing (CC) [1]. To address the heterogeneity problem and set up semantic relevance, a technique named ontology matching is used to identify correspondences between two ontologies. It can be described as follows: given two. The associate editor coordinating the review of this manuscript and approving it for publication was Sudipta Roy. ontologies primarily, each one describing a set of discrete entities (usually classes, properties, instances, etc.), finding the equivalent entity mappings in two ontologies [3]. As the key technology of the ontology matching, similarity measures are used to calculate the similarity value between two concepts. One single similarity measure can not get a satisfactory result, aggregating various similarity measures to enhance the result’s confidence is a popular strategy. How to assign a reasonable weight to various similarity measures and to find thresholds to filter suspect matching results is referred to as the ontology meta-matching problem

Objectives

Results

Conclusion