سال انتشار:
2020
عنوان انگلیسی مقاله:
ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning
ترجمه فارسی عنوان مقاله:
ADRL: یک چارچوب یادگیری تقویتی عمیق مبتنی بر توجه برای استدلال نمودار دانش
منبع:
Sciencedirect - Elsevier - Knowledge-Based Systems, 197 (2020) 105910. doi:10.1016/j.knosys.2020.105910
نویسنده:
Qi Wanga,∗, Yongsheng Hao b, Jie Cao c
چکیده انگلیسی:
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 selfattention
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.
Keywords: Knowledge graph | Knowledge reasoning | Reinforcement learning | Deep learning | Attention
قیمت: رایگان
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