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TGR: Neural-symbolic ontological reasoner for domain-specific knowledge graphs

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Abstract

Ontological reasoning has great prospects in applications based on domain-specific knowledge graphs (KG). However, it is difficult for existing logic reasoners to quickly perform inference over large-scale assertional boxes (ABoxes) in domain-specific KGs with complex ontologies. To address this challenge, a novel method named the “neural-symbolic ontological reasoner” is proposed. By incorporating neural-symbolic learning into ABox reasoning, a reasoner named the TimGangReasoner (TGR) is built. The TGR synthesizes graph data using an ontology, trains an ABox reasoning network (ABRN) model, and then approximately compiles the logic reasoning process of the ontology (represented by OWL+SWRL) into neural networks (NNs). The ABRN model encodes instances into vectors and then executes parallel vector computations to accelerate ABox reasoning. Experiments conducted on three open-source complex ontologies show that the TGR can achieve high-quality approximate deductive reasoning on ABoxes. The reasoning time consumption of the TGR increases linearly with the increase in the number of assertions, providing better scalability for large-scale ABoxes. Therefore, the TGR is able to reason quickly and accurately on domain-specific KGs that have complex underlying ontologies and contain large-scale ABoxes.

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Notes

  1. http://www.cs.man.ac.uk/~ezolin/dl/

  2. The open-world assumption (OWA) is the assumption that what is not stated is unknown. Both the ontologies represented by OWL and the KGs represented by RDF are based on the OWA. The closed-world assumption (CWA) is the opposite of the OWA; it assumes that what is not currently known is false.

  3. https://github.com/owlcs/owlapi

  4. https://github.com/eclipse/deeplearning4j

  5. http://www.hermit-reasoner.com

  6. https://github.com/stardog-union/pellet

  7. https://gitee.com/liubin0314/TimGang_Reasoner

  8. http://protege.cim3.net/file/pub/ontologies/family.swrl.owl/family.swrl.owl

  9. http://stl.mie.utoronto.ca/ontologies/simple_event_model/sem_r.swrl

  10. https://github.com/sbatsakis/TemporalRepresentations

  11. https://github.com/Bassem-Makni/NMT4RDFS

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Acknowledgements

Thanks for the support from Research Project under Grants GK20191A010279, GK20191A010296, in part by the National Science Foundation (NSF) of China under Grants 71571186, 61273322.

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  1. Science and Technology on Information Systems and Engineering Laboratory, National University of Defense Technology, Kaifu, 410073, Changsha, Hunan, China

    Xixi Zhu, Bin Liu, Li Yao, Zhaoyun Ding & Cheng Zhu

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Zhu, X., Liu, B., Yao, L. et al. TGR: Neural-symbolic ontological reasoner for domain-specific knowledge graphs. Appl Intell 53, 23946–23965 (2023). https://doi.org/10.1007/s10489-023-04834-8

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