A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024)

Abstract: Knowledge graph completion (KGC) task is to infer the missing knowledge in the knowledge graph based on known factual triples. However, present KGC approaches still face the following two challenges. Those methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, which makes it difficult to capture logic semantic between relations and global topological context information. To tackle the above challenges, we propose a unified joint approach with Topological Context learning and Rule Augmentation (TCRA) for KGC. The TCRA framework consists of an entity topological context learning mechanism based on dual-branch hierarchical graph attention network, and a relation rule context learning mechanism based on Rule-Transformer and rule-to-relation aggregator. The former mechanism encodes the topological structure features of entities, aggregates the local neighborhood topological context information of entities on the three levels (entity, relation and triple), and build clusters of global head or tail entities related to the same relation. It can capture the local and global topological context information of entities related to the same relation. The latter mechanism introduces chain-like Horn rules as the context information of relations, and encodes the logical semantic of relations to enrich the relation representation. Experimental performances on three benchmark datasets FB15k-237, WN18RR and Kinship indicate the effectiveness and superiority of our proposed approach. The codes are publicly available.

## introduction currently, large-scale knowledge graphs (kgs) such as freebase @xcite , dbpedia @xcite and nell @xcite have been widely used in knowledgeintensive applications including semantic retrieval, question answering, and recommendation systems. specially, knowledge graphs provide strong technical support for applications such as public opinion monitoring, intelligent decision-making, and credit assessment. however, knowledge graphs constructed in a manual or automated way are usually incomplete and sparse. knowledge graph completion (kgc) task addressed in this paper is a vital technique to infer new knowledge and complete missing entities and relations within knowledge graphs. at present, kgc approaches are mainly divided into methods based on neural reasoning, symbolic reasoning, and neural-symbolic reasoning @xcite . the first family techniques @xcite @xcite @xcite first embed entities and relations into a low-dimensional dense vector space. and then they measure the plausibility of unobserved triples by calculating their scores in the continuous space. the methods in the second family @xcite @xcite @xcite deduce general logical rules from knowledge graphs, and apply the logical rules to infer missing facts, which can achieve explainable reasoning. the third family approaches @xcite @xcite fuse the former two kinds of methods, and have become a mainstream technology in the fields of natural language processing and knowledge graph construction. however, existing kgc methods still face the following problems: (a) the existing kgc approaches based on graph neural network (gnn) only perform simple linear updates on the relation representation, which can not capture the correlation information between the relations in kgs. (b) those methods only aggregate local neighborhood 1 13686 information. even if message passing is applied multiple times, it is difficult for nodes that are far apart in kgs to effectively transfer information. hence, they cannot capture global topological context information. to solve the above problems, we propose a unified joint representation learning approach with topological context learning and rule augmentation (tcra) for kgc task. the tcra model adopts an encoder-decoder architecture. the encoder consists of two modules: the entity topology context learning module based on a dualbranch hierarchical graph attention network and the relation rule context learning module based on rule-transformer and rule-to-relation aggregator, which are used to generate embeddings of entities and relations, respectively. architecturally, the entity context learning module leverages hierarchical graph attention network (h-gat) to aggregate local neighborhood structure information to generate local embeddings of entities. parallelly, it builds global head entity or tail entity clusters related to the same relation to capture global semantic associations between entities. in addition, the relation context learning module leverages chain-like horn rules as context information of relations, and designs the rule-transformer to encode the logical semantic information of the relations in rule body relation sequences to generate the representation of rule bodies. further, the rule-to-relation aggregator fuses the rule body representations related to the rule head relations, thereby updating the representations of the rule head relations. the contributions of this paper are summarized as follows: on the one hand, our tcra can effectively model the local topological structure information of entities, and capture the global structural characteristics of entities in the form of entity clusters for enriching the context representation of entities. on the other hand, logical rules are introduced to mine logical semantic association of relations, and those rules are explicitly modeled to constrain relation embeddings for enhancing the representation of relations. the methods based on symbolic reasoning deduce general logical rules from the knowledge graph, and then apply the logical rules to infer the missing facts, which have good interpretability. many rule mining approaches have been developed to extract rules from large-scale kgs, such as amie+ @xcite , rnnlogic @xcite , rlogic @xcite and ncrl @xcite . recent techniques used in knowledge graph reasoning combine neural reasoning and symbolic reasoning. one of the fused methods is the symbol-driven neural reasoning method, which utilizes logic to enhance embedding. specifically, the fusion mechanism can be classified into the following three types of ones. the first family of mechanisms including kale @xcite and ruge @xcite are to use logical rules as additional regularization for kge training. the second kind of methods such as itere @xcite and uniker @xcite are to employ logical rules to obtain new hidden triples and generate additional triples for kge training. the third kind of approaches such as rpje @xcite , rule @xcite are to leverage logical rules as additional information to enhance kgs representation learning. this paper focuses on the symbol-driven neural reasoning method to solve kgc task. ## conclusion and future work in this paper, a unied joint approach with topological context learning and rule augmentation (tcra) has been proposed to perform knowledge graph completion, which can infer missing knowledge in a more complementary way. the designed entity topological context learning mechanism based on dual-branch hierarchical graph attention network can capture local structural features of entities at three levels, and incorporate global entity cluster to capture the global structural characteristics of entities. meanwhile, the relation rule context learning mechanism based on rule-transformer and rule-to-relation aggregator is developed to facilitate adequate interaction between relations by leveraging chain-like horn rules, which can capture the logical semantic of relations to enrich the relation representation. ex-8 tensive experiments demonstrate that the proposed tcra model outperforms present methods. in the future, we will further design an iterative manner to implement rule-enhanced kge methods on this basis, so as to achieve full complementarity between logical rules and kge.