A Semantic Filter Based on Relations for Knowledge Graph Completion (2021)

Abstract: Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in recent years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.

## introduction knowledge graphs (kgs) are collections of largescale triples, such as freebase @xcite , @xcite and dbpedia @xcite . kgs play a crucial role in applications such as question answering services, search engines, and medical care. although there are billions of triples in kgs, they are still incomplete. these incomplete knowledge bases will bring limitations to practical applications. therefore, knowledge graph completion, known as link prediction, which automatically predicts missing links between entities based on given links, has recently attracted growing attention. inspired by word embedding @xcite , researchers try to solve the link prediction through knowledge graph embedding. knowledge graph embedding models map entities and relations into low-dimensional vectors (or matrices, tensors), measure the rationality of triples through specific functions between entities and relations, and rank the triples with function scores. since transe @xcite proposes to use relation vectors to represent the geometric distance between entities, many variants emerge. for example, transh @xcite first explores the different representations of entities under different relations. transr @xcite attempts to map entities to the relational space through a particular matrix. transd @xcite tries to incorporate the different representations of the entities under the entity and relation into the calculation. these variants attempt to perform complex transformations based on relations or triples to achieve different representations of entities in different semantic spaces. recently, scholars are more inclined to solve link prediction by designing models with more powerful representation, such as complex @xcite , tucker @xcite , rotate @xcite , a method based on vector space rotation, and hake @xcite . contrary to the actual semantic description, models in recent research apply identical representation for the same entity in different triples. since the invention of transe @xcite , early scholars, who realized that we should compare different attributes of entities in different triples, tried to improve the model in this direction. however, most recent studies only focus on investigating the more robust representation of entities, such as autoeter @xcite and ro-tate @xcite . surprisingly, the attempt to find various representations of entities in different semantic spaces is gradually discarded. in practice, entities are collections of attributes, and each entity can contain various semantic attributes. figure 1 shows the comparison of boxes with the same shape and different colors. when comparing different attributes such as colors or shapes, entities should have different expressions rather than exact representations. the paper believes that each relation describes the links between the head and tail entities in particular attributes. measuring the plausibility of a given triplet means comparing the matching degree of the attributes associated with the predicate between the entities. therefore, this paper proposes a semantic filter module to select different attributes of entities in different triples. ## related work in this section, we describe related works and the critical differences between them. we divide knowledge graph embedding models into three leading families @xcite , including tensor decomposition models, geometric models, and deep learning models. tensor decomposition models. these models implicitly consider triples as tensor decomposition. distmult @xcite constrains all relation embeddings to be diagonal matrices, which reduces the space of parameters to access a more accessible model to train. rescal @xcite represents each relationship with a full rank matrix. complex @xcite extends the kg embeddings to the complex space to better model asymmetric and inverse relations. analogy @xcite employs the general bilinear scoring function but adds two main constraints inspired by analogical structures. based on the tucker decomposition, tucker @xcite factorizes a tensor into a set of vectors and a smaller shared core matrix. ## background in this section, we introduce kg embedding and kg completion tasks. next, we briefly introduce several models involved in this paper. ## sfbr model this section introduces a novel module-a semantic filter based on relations for knowledge graph completion. we first introduce the basic framework of sfbr in section 4.1 and the specific filter design in section 4.2. finally, we introduce several cases on several models in section 4.3. ## experiment this section is organized as follows. first, we introduce the experimental settings in section 5.1. then, we show the effectiveness of sfbr on three benchmark datasets in section 5.2. finally, we visualize and analyze the embeddings generated by sfbr in section 5.3. ## conclusion this paper designs a relation-based semantic filter-sfbr-for the geometric and tensor decomposition models based on knowledge graph completion. sfbr is based on the observation that judging the rationality of a particular triple is to compare specific attributes between the entities, ignoring other unrelated dimensions. therefore, this paper provides a relation-based semantic filter to extract the attributes that need to be compared and suppress the irrelevant attributes of entities. experiments show that sfbr can effectively improve the performance of the traditional models, especially the geometric models. the visualization shows that sfbr can effectively extract the relevant dimensions and distinguish the comparisons among different attributes. compared with the base models, sfbr only has a slight growth in resource occupation.