A knowledge graph completion model integrating entity description and network structure

Abstract

PurposeIn recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.Design/methodology/approachThe authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.FindingsThe experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.Originality/valueThe research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

Guest editorial: Extraction and evaluation of knowledge entities in the age of artificial intelligence

“…To explore the use of entity descriptions and network structures in enhancing knowledge graph completion with a high generalization ability across datasets, Yu et al (2023) proposed an entity-description augmented knowledge graph completion model (EDA-KGC). The authors conducted extensive experiments on the FB15K, WN18, FB15K-237 and WN18RR datasets to validate the effectiveness of the model.…”

“…To explore the use of entity descriptions and network structures in enhancing knowledge graph completion with a high generalization ability across datasets, Yu et al . (2023) proposed an entity-description augmented knowledge graph completion model (EDA-KGC).…”

A Network Representation Learning Model Based on Multiple Remodeling of Node Attributes

“…The ConvE [29] algorithm is based on the model of a convolutional neural network, which represents entities and relationships as twodimensional matrices and uses a convolutional neural network to model node relationships with higher prediction accuracy and interpretability. Yu et al [30] propose a knowledge graph completeness model that integrates entity descriptions and network structures, and they study how to enhance the completeness of the knowledge graph by using the entity descriptions and the network structures. KANE [31] proposes a knowledge graph embedding model based on a knowledge graph attention network and its attributes to feature enhancement, which captures the higher-order structure and attribute features of knowledge graphs by considering both relation triplets and attribute triplets in a graph convolutional network framework.…”

Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space

“…Zhou et al [38] use Bi-LSTM to encode entity descriptions, and guide the representation of entity descriptions and entity structured representation interactively. Yu et al [39] think that the latent semantic information of entity descriptions obtained by CNN and LSTM is limited and propose a new method that incorporates the entity description and network structure. Cheng et al [23] encode entity descriptions through Transformer and attention, and utilize capsule network to capture global semantic features between entities and relations, and achieve good results in link prediction.…”