Link Prediction on N-ary Relational Data
WWW '19: The Web Conference on The World Wide Web Conference WWW 2019(2019)
摘要
With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to complete KGs for a long time. However, they mainly focus on promoting the performance on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction concerning more than two arities. It has even proven that they may cause loss of structural information. To overcome these problems, in this paper, without decomposition, we represent each n-ary relational fact as a set of its role-value pairs. We further propose a method to conduct Link Prediction on N-ary relational data, thus called NaLP, which explicitly models the relatedness of all the role-value pairs in the same n-ary relational fact. Experimental results validate the effectiveness and merits of the proposed NaLP method.
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关键词
Link prediction, knowledge graph, n-ary relational fact, neural network
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