A Knowledge Graph Entity Disambiguation Method Based on Entity‐Relationship Embedding and Graph Structure Embedding

Abstract

The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attributes to obtain the mention embedding vector, compare it with the candidate entity embedding vector for similarity, and perform entity matching through the similarity. The disadvantage of this type of method is that it ignores the structural characteristics of the knowledge graph where the entity is located, that is, the connection between the entity and the entity, and therefore cannot obtain the global semantic features of the entity. To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector of entity relationship and the embedding vector of subgraph structure feature. EDEGE first trains the semantic vector of the entity relationship, then trains the graph structure vector of the subgraph where the entity is located, and balances the weights of these two vectors through the entity similarity function. Finally, the balanced vector is input into the graph neural network, and the matching between the entities is output to achieve entity disambiguation. Extensive experimental results proved the effectiveness of the proposed method. Among them, on the ACE2004 data set, the Precision, Recall, and F1 values of EDEGE are 9.2%, 7%, and 11.2% higher than baseline methods.

Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

“…Specifically, neural network-based approaches have gained popularity by incorporating the subgraph structure features of knowledge bases. These features are utilized as inputs to graph neural networks, enabling the disambiguation of entities within the knowledge base (Ma et al, 2021). Various methods have been explored, including convolutional and recurrent neural networks, as well as LSTM networks, to disambiguate entities based on extracted associations among them (Geng et al, 2021;Phan et al, 2017).…”

Improving Entity Disambiguation by Reasoning over a Knowledge Base

“…Graph neural networks (GNN) have been used to represent KB facts to inform ED predictions (Sevgili et al, 2019;Ma et al, 2021). These approaches can potentially access the information in all KB facts, but are reliant on the quality of the graph embeddings, which may struggle to represent many basic semantics (Jain et al, 2021) particularly for unpopular entities (Mohamed et al, 2020).…”

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base

“…Recent studies on multi-hop question answering attempt to build graphs based on entities and conduct reasoning over the constructed graph using graph neural networks [35][36][37][38], which are introduced to modify propagation limitation in long-distance relation. GNN-based question answering consists of many popular research directions, including reading comprehension, multiple-choice question answering, open domain question answering, and KBQA.…”