Maximizing mutual information across knowledge graphs for robust entity alignment
Authors:
- Ao GaoAGAo Gao
- Mingda LiMLMingda Li
- Wei LiuWLWei Liu
- Zhengya SunZSZhengya Sun
- Neng WanNWNeng Wan
- 16 July 2025
- journal article
- Published by Elsevier in Expert Systems with Applications
- Vol. 296
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (U22A2068)
- National Natural Science Foundation of China (U23A20320)
- Ministry of Science and Technology of the People's Republic of China
- National Key Research and Development Program of China (2022YFB 2703300)
This publication has 13 references indexed in Scilit:
- Learning Dynamic and Static Representations for Extrapolation-Based Temporal Knowledge Graph ReasoningIEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024
- One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph CompletionIEEE Transactions on Knowledge and Data Engineering, 2024
- Learning dual disentangled representation with self-supervision for temporal knowledge graph reasoningInformation Processing & Management, 2024
- TypeEA: Type-Associated Embedding for Knowledge Graph Entity AlignmentAPSIPA Transactions on Signal and Information Processing, 2023
- Variety-aware GAN and online learning augmented self-training model for knowledge graph entity alignmentInformation Processing & Management, 2023
- Billion-Scale Similarity Search with GPUsIEEE Transactions on Big Data, 2019
- WikidataCommunications of the ACM, 2014
- Matching large ontologies: A divide-and-conquer approachData & Knowledge Engineering, 2008
- Estimating mutual informationPhysical Review E, 2004
- Asymptotic evaluation of certain markov process expectations for large time. IVCommunications on Pure and Applied Mathematics, 1983