TY - GEN
T1 - Neural Multi-hop Logical Query Answering with Concept-Level Answers
AU - Tang, Zhenwei
AU - Pei, Shichao
AU - Peng, Xi
AU - Zhuang, Fuzhen
AU - Zhang, Xiangliang
AU - Hoehndorf, Robert
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Fuzzy Logic
KW - Knowledge Representation Learning
KW - Multi-hop Logical Query Answering
KW - Neuro-symbolic Reasoning
UR - http://www.scopus.com/inward/record.url?scp=85177174380&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47240-4_28
DO - 10.1007/978-3-031-47240-4_28
M3 - Conference contribution
AN - SCOPUS:85177174380
SN - 9783031472398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 522
EP - 540
BT - The Semantic Web – ISWC 2023 - 22nd International Semantic Web Conference, Proceedings
A2 - Payne, Terry R.
A2 - Presutti, Valentina
A2 - Qi, Guilin
A2 - Poveda-Villalón, María
A2 - Stoilos, Giorgos
A2 - Hollink, Laura
A2 - Kaoudi, Zoi
A2 - Cheng, Gong
A2 - Li, Juanzi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Semantic Web Conference, ISWC 2023
Y2 - 6 November 2023 through 10 November 2023
ER -