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With the overwhelming popularity of Knowledge Graphs (KGs), researchers havepoured attention to link prediction to fill in missing facts for a long time.However, they mainly focus on link prediction on binary relational data, wherefacts are usually represented as triples in the form of (head entity, relation,tail entity). In practice, n-ary relational facts are also ubiquitous. Whenencountering such facts, existing studies usually decompose them into triplesby introducing a multitude of auxiliary virtual entities and additionaltriples. These conversions result in the complexity of carrying out linkprediction on n-ary relational data. It has even proven that they may causeloss of structure information. To overcome these problems, in this paper, werepresent each n-ary relational fact as a set of its role and role-value pairs.We then propose a method called NaLP to conduct link prediction on n-aryrelational data, which explicitly models the relatedness of all the role androle-value pairs in an n-ary relational fact. We further extend NaLP byintroducing type constraints of roles and role-values without any externaltype-specific supervision, and proposing a more reasonable negative samplingmechanism. Experimental results validate the effectiveness and merits of theproposed methods.

Link Prediction on N-ary Relational Data Based on Relatedness Evaluation