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Implementing Large-Scale ABox Materialization Using Subgraph Reasoning

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Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13368))

Abstract

The ontology knowledge base can be divided into two parts: TBox and ABox, where the former models schema-level knowledge within the domain, and the latter is a statement of assertions or facts about a set of instances. ABox materialization is the process of discovering implicit assertions in ABox by reasoning based on existing knowledge, which is important in knowledge base applications. Ontology reasoning is a common method for ABox materialization. However, it is considered to be a computationally intensive operation and does not scale well for large-scale ABox. To solve this problem, this paper proposes an approximate reasoning hypothesis: materialization on the overall ABox is approximately equivalent to the collection of subgraph reasoning on ABox. Based on this hypothesis, a subgraph reasoning method for large-scale ABox materialization is proposed. Subgraph reasoning first divides ABox into instance-centered multi-hops subgraphs, then performs ontology reasoning on each subgraph, and finally takes the collection of all subgraph reasoning results as the result of ABox materialization. We conduct experiments on multiple open-source ontologies, and analyze the rationality of the approximate reasoning hypothesis. The experimental results show that subgraph reasoning can effectively improve the reasoning efficiency and achieve superior scalability for large-scale ABox materialization reasoning.

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Notes

  1. 1.

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

  2. 2.

    http://krr-nas.cs.ox.ac.uk/ontologies/lib/LUBM/.

  3. 3.

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

  4. 4.

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

  5. 5.

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

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Authors and Affiliations

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

    **xi Zhu, Bin Lin, Zhaoyun Ding, Li Yao & Cheng Zhu

Authors
  1. **xi Zhu
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  2. Bin Lin
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  3. Zhaoyun Ding
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  4. Li Yao
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  5. Cheng Zhu
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Corresponding author

Correspondence to Cheng Zhu .

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Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Zhu, X., Lin, B., Ding, Z., Yao, L., Zhu, C. (2022). Implementing Large-Scale ABox Materialization Using Subgraph Reasoning. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_48

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  • DOI: https://doi.org/10.1007/978-3-031-10983-6_48

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