RLPath: a knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning

Chen, Ling; Jun, Cui; Tang, Xing; Qian, Yuntao; Li, Yansheng

doi:10.1007/s10489-021-02672-0

Cited by 29 publications

(2 citation statements)

References 25 publications

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“…Next, MINERVA [23] and MARLPaR [24] further improve the DeepPath model. In addition, some studies such as AttnPath [25] and RLPath [26] extend RL-based reasoning method by incorporating attention mechanisms into the path reasoning process. However, the above methods are all set in the static KG and cannot capture the time information of TKG.…”

Section: Traditional Knowledge Graph Reasoningmentioning

confidence: 99%

Modeling Paths and History for Temporal Knowledge Graph Reasoning

Chen,

Huang

2024

Preprint

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Knowledge Graph (KG) reasoning is a crucial task that discovers potential and unknown knowledge based on the existing knowledge. Temporal Knowledge Graph (TKG) reasoning is more challenging than KG reasoning because the additional temporal information needs to be handled. Previous TKG reasoning methods restrict the search space to avoid huge computational consumption, resulting in a decrease in accuracy. In order to improve the accuracy and efficiency of TKG reasoning, a model CMPH (Combination Model of Paths and History) is proposed, which consists of a path memory network and a history memory network. The former finds the paths in advance by a TKG path search algorithm and learns to memorize the recurrent pattern for reasoning, which prevents path search at inference stage. The latter adopts efficient encoder-decoder architecture to learn the features of historical events in TKG, which can avoid tackling a large number of structural dependencies and increase the reasoning accuracy. To take the advantages of these two types of memory networks, a gate component is designed to integrate them for better performance. Extensive experiments on four real-world datasets demonstrate that the proposed model obtains substantial performance and efficiency improvement for the TKG reasoning tasks. Especially, it achieves up to 8.6% and 11.8% improvements in MRR and hit@1 respectively, and up to 21 times speedup at inference stage comparing to the state-of-the-art baseline.

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Section: Traditional Knowledge Graph Reasoningmentioning

confidence: 99%

Modeling Paths and History for Temporal Knowledge Graph Reasoning

Chen,

Huang

2024

Preprint

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“…Multi-hop KBQA tasks often adopt methods based on memory networks [24,25], semantic parsing [26][27][28], or reinforcement learning [29][30][31][32]. For example, Xu et al [25] improved traditional key-value memory networks to answer complex questions by designing a new query updating strategy to mask previously addressed memory information from the query representations, and they introduced a novel STOP strategy to read a flexible number of triples from memory slots.…”

Section: Knowledge Base Questionmentioning

confidence: 99%

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base

Zhang

Jin

et al. 2022

Computational Intelligence and Neuroscience

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Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way humans solve multi-hop questions, which do not consider the latent relation information contained in connected edges, and lack of measuring the correlation between specific relations and the input question. To address these challenges, we propose an edge-aware graph neural network for multi-hop path reasoning task. First, a query node is directly added to the candidate subgraph retrieved from the knowledge base, which constructs what we term a query graph. This graph construction strategy makes it possible to enhance the information flow between the question and the nodes for the subsequent message passing steps. Second, question-related information contained in the relations is added to the entity node representations during graph updating; meanwhile, the relation representations are updated. Finally, the attention mechanism is used to weight the contribution from neighbor nodes so that only the information of neighbor nodes related to the query can be injected into new node representations. Experimental results on MetaQA and PathQuestion-Large (PQL) benchmarks demonstrate that the proposed model achieves higher Hit@1 and F1 scores than the baseline methods by a large margin. Moreover, ablation studies show that both the graph construction and the graph update algorithm contribute to performance improvement.

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