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.
Highlights
Knowledge graph entity disambiguation is to match entity mentions in facts to corresponding entities in a given knowledge graph
Inspired by the above ideas, we propose an entity disambiguation model based on entity-relationship embedding and entity subgraph structure embedding (EDEGE)
Inspired by the above methods, this paper comprehensively considers the semantic features of the entity-relation and the subgraph structure of the knowledge graph and uses these features as the input of the graph neural network to disambiguate the entity in the knowledge graph
Summary
Knowledge graph entity disambiguation is to match entity mentions in facts to corresponding entities in a given knowledge graph. E weakness of these models is that the global structure feature between entities is not encoded in the embedding presentation Another line of work utilizes neural networks to do Computational Intelligence and Neuroscience entity disambiguation in an end-to-end way [8, 9] (joint entity and relation extraction model based on rich semantics), which utilizes entity-relation embedding and a differentiable joint inference method for entity disambiguation. Neural network-based entity disambiguation methods cannot capture the global structure feature of the knowledge graph and have poor explanation. Irdly, EDEGE concatenated the entity-relation embedding and entity’s subgraph embedding, which are used as the input of relational graph neural network to disambiguate the ambiguous entities in an end-to-end way. E entity relationship contains the global structure and semantic relations between entities and relations and uses a graph neural network to encode on the graph global entity embedding vector to improve the precision of entity disambiguation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.