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ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning
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DOI：10.1016/j.knosys.2020.105910.png)
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Abstract
En 中文
Knowledge graph reasoning is one of the key technologies for knowledge graph construction, which plays an important part in application scenarios such as vertical search and intelligent question answering. It is intended to infer the desired entity from the entities and relations that already exist in the knowledge graph. Most current methods for reasoning, such as embedding-based methods, globally embed all entities and relations, and then use the similarity of vectors to infer relations between entities or whether given triples are true. However, in real application scenarios, we require a clear and interpretable target entity as the output answer. In this paper, we propose a novel attention-based deep reinforcement learning framework (ADRL) for learning multi-hop relational paths, which improves the efficiency, generalization capacity, and interpretability of conventional approaches through the structured perception of deep learning and relational reasoning of reinforcement learning. We define the entire process of reasoning as a Markov decision process. First, we employ CNN to map the knowledge graph to a low-dimensional space, and a message-passing mechanism to sense neighbor entities at each level, and then employ LSTM to memorize and generate a sequence of historical trajectories to form a policy and value functions. We design a relational module that includes a self-attention mechanism that can infer and share the weights of neighborhood entity vectors and relation vectors. Finally, we employ the actor-critic algorithm to optimize the entire framework. Experiments confirm the effectiveness and efficiency of our method on several benchmark data sets. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Knowledge graph
Knowledge reasoning
Reinforcement learning
Deep learning
Attention
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