Approximate Reasoning for Large-Scale ABox in OWL DL Based on Neural-Symbolic Learning

Xixi Zhu, Bin Liu, Cheng Zhu (), Zhaoyun Ding and Li Yao
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Xixi Zhu: Science and Technology on Information Systems and Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Bin Liu: Science and Technology on Information Systems and Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Cheng Zhu: Science and Technology on Information Systems and Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Zhaoyun Ding: Science and Technology on Information Systems and Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Li Yao: Science and Technology on Information Systems and Engineering Laboratory, National University of Defense Technology, Changsha 410073, China

Mathematics, 2023, vol. 11, issue 3, 1-24

Abstract: The ontology knowledge base (KB) can be divided into two parts: TBox and ABox, where the former models schema-level knowledge within the domain, and the latter is a set of statements of assertions or facts about instances. ABox reasoning is a process of discovering implicit knowledge in ABox based on the existing KB, which is of great value in KB applications. ABox reasoning is influenced by both the complexity of TBox and scale of ABox. The traditional logic-based ontology reasoning methods are usually designed to be provably sound and complete but suffer from long algorithm runtimes and do not scale well for ontology KB represented by OWL DL (Description Logic). In some application scenarios, the soundness and completeness of reasoning results are not the key constraints, and it is acceptable to sacrifice them in exchange for the improvement of reasoning efficiency to some extent. Based on this view, an approximate reasoning method for large-scale ABox in OWL DL KBs was proposed, which is named the ChunfyReasoner (CFR). The CFR introduces neural-symbolic learning into ABox reasoning and integrates the advantages of symbolic systems and neural networks (NNs). By training the NN model, the CFR approximately compiles the logic deduction process of ontology reasoning, which can greatly improve the reasoning speed while ensuring higher reasoning quality. In this paper, we state the basic idea, framework, and construction process of the CFR in detail, and we conduct experiments on two open-source ontologies built on OWL DL. The experimental results verify the effectiveness of our method and show that the CFR can support the applications of large-scale ABox reasoning of OWL DL KBs.

Keywords: neural-symbolic learning; approximate reasoning; large-scale ABox reasoning; neural network; ontology reasoning; OWL DL (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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