T ran S : Transition-based Knowledge Graph Embedding with Synthetic Relation Representation (2022)

Abstract: Knowledge graph embedding (KGE) aims to learn continuous vector representations of relations and entities in knowledge graph (KG). Recently, transition-based KGE methods have become popular and achieved promising performance. However, scoring patterns like TransE are not suitable for complex scenarios where the same entity pair has different relations. Although some models attempt to employ entity-relation interaction or projection to improve entity representation for one-to-many/many-to-one/many-to-many complex relations, they still continue the traditional scoring pattern, where only a single relation vector in the relation part is used to translate the head entity to the tail entity or their variants. And recent research shows that entity representation only needs to consider entities and their interactions to achieve better performance. Thus, in this paper, we propose a novel transition-based method, TranS, for KGE. The single relation vector of the relation part in the traditional scoring pattern is replaced by the synthetic relation representation with entity-relation interactions to solve these issues. And the entity part still retains its independence through entity-entity interactions. Experiments on a large KG dataset, ogbl-wikikg2, show that our model achieves state-of-the-art results.

## introduction knowledge graphs (kgs), such as freebase @xcite , wikidata @xcite , dbpedia @xcite and yago @xcite , play a very important role in many fields, including question answering @xcite , semantic parsing @xcite , information retrieval @xcite and so on. kg, as a multi-relational graph, is composed of entities as nodes and relations as different types of edges. it is usually represented as the form of triplets (h, r, t), i.e., (head entity, relation, tail entity), where relation indicates the relationship between the two entities. knowledge graph embedding (kge) is an important and fundamental research topic in kg. it aims to learn dense semantic representations of entities and relations for downstream tasks such as kg completion and link prediction. generally speaking, kge methods can be roughly divided into the following directions: translational distance @xcite @xcite @xcite @xcite , semantic matching @xcite @xcite @xcite @xcite and neural networks @xcite @xcite @xcite . because transition-based kge method like transe @xcite is simple and effective, this series of models are becoming more and more popular in both academia and industry. specifically, transe makes the difference between two entity vectors (h and t) approximate to the relation vector (r), i.e., th ≈ r. that is to say, the relation r is characterized by the translating vector r. however, transe is not suitable to deal with complex relations like one-to-many/many-to-one/manyto-many. for example, in figure 1 , after graduating from erasmus university rotterdam, pauline meurs became a professor at the same university. and the composer, producer, screenwriter, editor and director of the film, indramalati, can be the same person, jyoti prasad agarwala. although previous models @xcite @xcite @xcite such as transh/r/d have considered relevant issues, they still focus on the entity-relation projection or interaction in the entity part and continue the transe pattern, r t -r h ≈ r, where r t and r h is the deformation of t and h, r t -r h is the entity part, and r is the relation part. actually, recent research, interht @xcite , shows that the entity part only needs to consider the head and tail entities and their interaction information to achieve remarkable performance and outperform previous transx series models. unfortunately, it again ignores the problem of complex relation representation. therefore, from the perspective of interaction, how to solve the problem in figure 1 by introducing entity-relation interactions in the relation part under the condition that only entity-entity interactions are retained in the entity part needs to be further considered. to this end, we propose a novel transition-based knowledge graph embedding model, trans, which replaces traditional scoring pattern with synthetic relation pattern, i.e., r t -r h ≈ r + r + r. the final relation representation is the sum of multiple relation vectors. two of them (r, r) are also related to the head entity h and the tail entity t in addition to the relation r (orange solid lines denote r, and blue dotted lines denote r, r in figure 1 ). for one thing, in the entity part, instead of using entity-relation interaction and projection, it focuses only on entities and their interactions themselves to guarantee their independence and effectiveness. for another thing, different from other methods that utilize entity-relation interactions in the entity part, our method migrates their interactions to the relation part and forms synthetic relation representation, which can effectively solve the problem that a single relation vector cannot represent different relations when facing the same entity pair. experiments on a large knowledge graph dataset, ogbl-wikikg2, show that our proposed model achieves the best results with fewer parameters. ## related work recently, graph structures are used widely in natural language processing, recommendation and other areas @xcite . kg, as one of the graph structures, uses triples consisting of head nodes, tail nodes and relation edges to represent structured knowledge. to further compare different transition-based knowledge graph embeddings, we summarize related methods in