Purpose

In 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/approach

The 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.

Findings

The 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/value

The 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.

中文翻译：

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融合实体描述和网络结构的知识图谱补全模型

目的

近年来，知识图谱补全越来越受到研究关注，并取得了显着的进步。然而，大多数现有模型在获取实体和关系表示时仅使用知识图三元组的结构。相比之下，实体描述和知识图网络结构的整合被忽略了。本文旨在研究如何利用实体描述和网络结构来增强知识图谱的完成，并在不同数据集之间具有较高的泛化能力。

设计/方法论/途径

作者提出了一种实体描述增强知识图补全模型（EDA-KGC），该模型结合了实体描述和网络结构。它由表示初始化、深度交互和推理三个模块组成。表示初始化模块利用实体描述来获取实体的预训练表示。深度交互模块获取实体和关系之间深度交互的特征。推理组件利用深度交互特征向量和实体表示矩阵进行矩阵运算，从而获得目标实体的概率分布。作者在 FB15K、WN18、FB15K-237 和 WN18RR 数据集上进行了大量实验，以验证所提出模型的效果。

发现

实验表明，所提出的模型优于传统的基于结构的知识图补全模型和实体描述增强的知识图补全模型。实验还表明，该模型在稀疏数据、动态实体和有限训练周期等不同场景下具有更大的可行性。研究表明，实体描述与网络结构的融合可以显着提高知识图谱完成任务的效果。

原创性/价值

该研究对于补全知识图谱中缺失的信息，提高知识图谱在信息检索、问答等领域的应用效果具有重要参考意义。