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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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.
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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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