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Neural Multi-hop Logical Query Answering with Concept-Level Answers

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14265))

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Abstract

Neural multi-hop logical query answering (LQA) is a fundamental task to explore relational data such as knowledge graphs, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers. Although previous LQA methods can give specific instance-level answers, they are not able to provide descriptive concept-level answers, where each concept is a description of a set of instances. Concept-level answers are more comprehensible to users and are of great usefulness in the field of applied ontology. In this work, we formulate the problem of LQA with concept-level answers (LQAC), solving which needs to address challenges in incorporating, representing, and operating on concepts. We propose an original solution for LQAC. Firstly, we incorporate description logic-based ontological axioms to provide the source of concepts. Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with instances. Moreover, we design operators involving concepts on top of fuzzy set representation of concepts and queries for optimization and inference. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our method for LQAC. In particular, we show that our method is promising in discovering complex logical biomedical facts.

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Notes

  1. 1.

    https://github.com/snap-stanford/KGReasoning.

  2. 2.

    https://yago-knowledge.org/downloads/yago-4.

  3. 3.

    http://downloads.dbpedia.org/wiki-archive/downloads-2016-10.html.

  4. 4.

    https://bio2vec.cbrc.kaust.edu.sa/data/elembeddings/el-embeddings-data.zip.

  5. 5.

    http://geneontology.org/.

  6. 6.

    https://github.com/lilv98/LQAC.

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Acknowledgements

This work has been supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/5041-01-01, the National Key Research and Development Program of China under Grant No. 2021ZD0113602, and the National Natural Science Foundation of China under Grant Nos. 62176014, the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Zhenwei Tang

  2. University of Notre Dame, Notre Dame, IN, USA

    Shichao Pei & Xiangliang Zhang

  3. Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Zhenwei Tang, Xi Peng & Robert Hoehndorf

  4. Institute of Artificial Intelligence, Beihang University, Beijing, China

    Fuzhen Zhuang

  5. Zhongguancun Laboratory, Beijing, China

    Fuzhen Zhuang

Authors
  1. Zhenwei Tang
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  2. Shichao Pei
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  3. Xi Peng
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  4. Fuzhen Zhuang
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  5. Xiangliang Zhang
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  6. Robert Hoehndorf
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Corresponding author

Correspondence to Zhenwei Tang .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, Beijing, China

    Juanzi Li

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Tang, Z., Pei, S., Peng, X., Zhuang, F., Zhang, X., Hoehndorf, R. (2023). Neural Multi-hop Logical Query Answering with Concept-Level Answers. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-47240-4_28

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  • Online ISBN: 978-3-031-47240-4

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