E-BELA: Enhanced Embedding-Based Entity Linking Approach

Abstract

Entity linking is the process of connecting mentions of entities in natural language texts, such as references to people or places, to specific entities in knowledge graphs, such as DBpedia or Wikidata. This process is crucial in the natural language processing tasks since it facilitates disambiguating entities in unstructured data, enhancing understanding and semantic processing. However, entity linking faces challenges due to the complexity and ambiguity of natural languages, as well as the discrepancy between the form of textual entity mentions and entity representations. Considering that entity mentions are in natural language and entity representations in knowledge graphs have object nodes that describe them in the same way, in this work we propose E-BELA, an effective approach based on literal embeddings. We aim to put close vector representations of mentions and entities in a vector space, allowing linking of mentions and entities by using a similarity or distance metric. The results demonstrate that our approach outperforms previous ones, contributing to the field of natural language processing.