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Luyi Bai

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4 papers

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EAAI Journal 2026 Journal Article

Entity and relation feature learning framework for sparse temporal knowledge graph reasoning

Luyi Bai
Xiangxi Meng
Lin Zhu

In the realm of temporal knowledge graphs, reasoning mechanisms are essential for uncovering time-dependent relationships and ensuring high interpretability. However, existing models often struggle with sparsely populated temporal knowledge graphs, which record only critical knowledge units. To address these challenges, this paper proposes an Entity and Relation feature learning framework for Reasoning in Sparse Temporal Knowledge Graphs, denoted as STKGR-ER. STKGR-ER utilizes a graph attention network to dynamically aggregate entity features across relations and timestamps, enhancing semantic accuracy. Gated recurrent units then learn latent logical rules and temporal patterns, reinforcing relation embeddings. By enriching entity features and learning from relational sequences, STKGR-ER effectively addresses information scarcity and reduces irrelevant path interference. Experiments conducted on twelve sparse datasets, ranging in size from 870 to 4833 entities and containing up to 21, 552 training quadruples, including subsets of ICEWS14 and ICEWS05-15, demonstrate that STKGR-ER significantly improves performances (ICEWS: Integrated Crisis Early Warning System). Notably, on the Hits@10 metric, STKGR-ER surpasses the best multi-hop path baselines by 11. 91%, 13. 37%, and 18. 09% on ICEWS14-10%, ICEWS14-20%, and ICEWS14-30%, respectively, and by 5. 87%, 12. 54%, and 13. 07% on ICEWS05-15-2%, ICEWS05-15-3%, and ICEWS05-15-5%, respectively, highlighting its strong reasoning capabilities in sparse temporal environments.

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EAAI Journal 2024 Journal Article

Embedding-based entity alignment between multi-source temporal knowledge graphs

Lin Zhu
Nan Li
Luyi Bai

The goal of entity alignment is to identify entities in two multi-source knowledge graphs (KGs) that represent the same real-world object. Recent researches on multi-source entity alignment mainly concentrate on static KGs. In fact, temporal KGs have become valuable resources for numerous artificial intelligence applications, and entity alignment between multi-source temporal KGs is becoming more and more important. Current entity alignment models cannot support temporal tasks and fail to deal with the attributes with low literal similarity that share the same semantics through attribute embedding. Therefore, we propose a RDF (Resource Description Framework)-based model for representing temporal KGs, and an embedding-based entity alignment method for multi-source temporal KGs. This method computes for the similarity of temporal information and generates aligned attribute pairs in the predicate alignment module. We design an interactive module to make matched attributes and the matched entities help to find each other based on aligned attribute pairs. This module can calculate the similarity of attributes with low literal similarity. After getting the structure similarity of the structure embedding module, the final entity alignment result of temporal KGs is produced by the calculation of a binary linear regression function. Experimental results demonstrate that our proposed model outperforms existing approaches significantly.

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EAAI Journal 2024 Journal Article

Quadruple mention text-enhanced temporal knowledge graph reasoning

Lin Zhu
Wenjun Zhao
Luyi Bai

Most temporal knowledge graphs (TKGs) are incomplete, and TKGs reasoning can complete the missing information. TKGs reasoning can utilize various external text information, and the quadruple mention text is important text information. Existing temporal knowledge graph reasoning models only utilize structural quadruple information to complete reasoning tasks, while ignoring the rich semantic information in quadruple mention texts. In this paper, we propose a Quadruple Mention text-enhanced TKGs reasoning model (QM-mod). It can utilize both graph structure information and quadruple mention texts to accomplish TKGs reasoning tasks. Specifically, we extract quadruple mention text information from the NOW (News on the web) corpus. Then, we conduct experiments on multiple tasks, including link prediction, quadruple classification, ablation experiments and embedding dimension analysis. Experimental results show that our model has performance advantages on most metrics. The average improvement is 1. 89% in the link prediction experiment and the average improvement is 8. 1% in the quadruple classification experiment.

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EAAI Journal 2023 Journal Article

RoAN: A relation-oriented attention network for temporal knowledge graph completion

Luyi Bai
Xiangnan Ma
Xiangxi Meng
Xin Ren
Yujing Ke

In the last few years, the availability of temporal knowledge graphs (TKGs), which associate time information for each event, increased the need for completing in these TKGs. In order to solve the completion problem, the existing efforts extend static knowledge graph completion models to handle time-dependent representations, or encode the structural information of events for passing temporal message. However, the above efforts mostly construct TKGs in the form of multi-edges mesh and focus on entity features. They do not explore the power of relations for TKG completion task. In this paper, we introduce a form of relational multi-chains to reconstruct TKGs and propose a relation-oriented attention mechanism for embedding the features of relations. According to our attention mechanism, we build a Relation-oriented Attention Network (RoAN) to model temporal embeddings of relations. It is worth noting that our approach is model-agnostic and can be potentially combined with most existing TKG completion models. Experimental results show that our approach can be coupled with previous TKGC methods and can increase their performance accordingly. In addition, the analysis reveals the principle of our relation-oriented attention mechanism.

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