Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

Abstract

Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.

Matching sensor ontologies through siamese neural networks without using reference alignment

“…Generally, precision (P), recall (R), and f-measure (F ) are utilized to test the matching results' quality (Xue, 2020;Xue et al, 2021b;Xue et al, 2021a): R = correct _found_correspondences all_possible_correspondences (4)…”

“…Generally, precision ( P ), recall ( R ), and f-measure ( F ) are utilized to test the matching results’ quality ( Xue, 2020 ; Xue et al, 2021b ; Xue et al, 2021a ): where P and R respectively indicate the accuracy and completeness of the results. P equals 1 denoting all found correspondences are correct, while R equals 1 representing that all correct correspondences are found; F is the harmonic mean of P and R to balance them.…”

Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism

“…A growing number of sensor ontologies have appeared due to the sensor ontology possesses powerful sensor network model expression ability, i.e., SensorOntology 2009 ontology, SSN ontology and IoT-Lite ontology, and so on [11][12][13]. And to enhance the interaction between sensor networks to achieve data integration, the sensor ontology matching technique has been brought out these years [14].…”

A Provably Secure Authentication and Key Exchange Protocol in Vehicular Ad Hoc Networks

“…At the same time, the rapid increase in the number of vehicles on the road has also made the traffic situation more complicated, and there will be many traffic problems, such as traffic accidents and road congestion. erefore, researchers apply artificial intelligence [1][2][3][4], wireless networks, and sensor technology [5,6] to road vehicle management, so that vehicles can share information and release relevant road information to alleviate traffic problems. is is the vehicular ad hoc network, which consists of vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication.…”