Matching Knowledge Graphs with Compact Niching Evolutionary Algorithm

To address the Knowledge Graph (KG) heterogeneity issue, we need to determine a set of entity correspondences, which requires aggregating several complementary similarity measures to improve the confidence of the results. How to determine the suitable aggregating weights for the similarity measures to improve the KG alignment's quality is called the KG meta-matching problem, whose challenge of scalability remains significant in the Semantic Web (SW) domain. To face this challenge, this work proposes a Compact Niching Evolutionary Algorithm (CNEA) based matching technique. We first propose an approximate evaluation metric on the alignment's quality, and on this basis, a multi-modal optimization model is constructed to formally define the KG meta-matching problem. Then, a niching strategy is combined with EA's evolutionary paradigm to address it, which is able to effectively locate and maintain multiple global optimal solutions. Moreover, a serial matching framework and the compact evolutionary mechanism are presented to improve CNEA's efficiency. In particular, the former reduces the algorithm's search space of the instance matching phase with schema level alignments, and the latter uses the probability representation on the population to reduce the algorithm's memory consumption and run time. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)'s KG track to test our proposal's performance, and experimental results show that CNEA-based KG matching technique is both effective and efficient.