دانلود مقاله انگلیسی رایگان:ADRL: یک چارچوب یادگیری تقویتی عمیق مبتنی بر توجه برای استدلال نمودار دانش - 2020
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  • ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning
    ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 9
    حجم فایل: 703 کیلوبایت

    قیمت: رایگان


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