N eu I nfer: Knowledge Inference on N -ary Facts (2020)

Abstract: Knowledge inference on knowledge graph has attracted extensive attention, which aims to find out connotative valid facts in knowledge graph and is very helpful for improving the performance of many downstream applications. However, researchers have mainly poured attention to knowledge inference on binary facts. The studies on n-ary facts are relatively scarcer, although they are also ubiquitous in the real world. Therefore, this paper addresses knowledge inference on n-ary facts. We represent each n-ary fact as a primary triple coupled with a set of its auxiliary descriptive attribute-value pair(s). We further propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. Besides handling the common task to infer an unknown element in a whole fact, NeuInfer can cope with a new type of task, flexible knowledge inference. It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s). Experimental results demonstrate the remarkable superiority of NeuInfer.

## introduction with the introduction of connotative valid facts, knowledge inference on knowledge graph improves the performance of many downstream applications, such as vertical search and question answering @xcite . existing studies @xcite mainly focus on knowledge inference on binary facts with two entities connected with a certain binary relation, represented as triples, (head entity, relation, tail entity). they attempt to infer the unknown head/tail entity or the unknown relation of a given binary fact. however, n-ary facts involving more than two entities are also ubiquitous. for example, in freebase, more than 1/3 entities participate in n-ary facts @xcite . the fact that john bardeen received n obel p rize in p hysics in 1956 together with w alter houser brattain and w illiam shockley foot_0 is a typical 5ary fact. so far, only a few studies @xcite @xcite have tried to address knowledge inference on n-ary facts. in existing studies for knowledge inference on nary facts, each n-ary fact is represented as a group of peer attributes and attribute values. in practice, for each n-ary fact, there is usually a primary triple (the main focus of the n-ary fact), and other attributes along with the corresponding attribute values are its auxiliary descriptions. take the above 5-ary fact for example, the primary triple is (john bardeen, award-received, n obel p rize in p hysics), and other attribute-value pairs including point-in-time : 1956 , together-with : w alter houser brattain and together-with : w illiam shockley are its auxiliary descriptions. actually, in @xcite and wikidata @xcite , a primary triple is identified for each n-ary fact. the above 5-ary fact is a relatively complete example. in the real-world scenario, many n-ary facts appear as only partial ones, each consisting of a primary triple and a subset of its auxiliary description(s), due to incomplete knowledge acquisition. for example, (john bardeen, awardreceived, n obel p rize in p hysics) with pointin-time : 1956 and it with {together-with : w alter houser brattain, together-with : w illiam shockley} are two typical partial facts corresponding to the above 5-ary fact. for differentiation, we call those relatively complete facts as whole ones. we noticed that existing studies on n-ary facts infer an unknown element in a welldefined whole fact and have not paid attention to knowledge inference on partial facts. later on, we refer the former as simple knowledge inference, while the latter as flexible knowledge inference. with these considerations in mind, in this paper, by discriminating the information in the same n-ary fact, we propose a neural network model, called neuinfer, to conduct both simple and flexible knowledge inference on n-ary facts. our specific contributions are summarized as: neuinfer can particularly handle the new type of task, flexible knowledge inference, which infers an unknown element in a partial fact consisting of a primary triple and any number of its auxiliary description(s).