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

Zhenwei Tang*, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish (US)
Title of host publicationThe Semantic Web – ISWC 2023 - 22nd International Semantic Web Conference, Proceedings
EditorsTerry R. Payne, Valentina Presutti, Guilin Qi, María Poveda-Villalón, Giorgos Stoilos, Laura Hollink, Zoi Kaoudi, Gong Cheng, Juanzi Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages19
ISBN (Print)9783031472398
StatePublished - 2023
Event22nd International Semantic Web Conference, ISWC 2023 - Athens, Greece
Duration: Nov 6 2023Nov 10 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Semantic Web Conference, ISWC 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Fuzzy Logic
  • Knowledge Representation Learning
  • Multi-hop Logical Query Answering
  • Neuro-symbolic Reasoning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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