A sentence similarity-based approach for enhancing entity linking

Entity linking involves associating mentions of entities in natural language texts, such as references to people or locations, with specific entity representations in knowledge graphs like DBpedia or Wikidata. This process is essential in natural language processing tasks, as it aids in disambiguating entities in unstructured data, thereby improving comprehension and semantic processing. However, entity linking faces challenges due to the complexity and ambiguity of natural languages, as well as discrepancies between the forms of textual entity mentions and entity representations. Building upon our previous work, this study extends the E-BELA --Enhanced Embedding-Based Entity Linking Approach, which is based on literal embeddings. We extend our previous work by evaluating E-BELA using a new dataset, conducting a comprehensive analysis of failure cases and limitations, and providing further discussion of our results. E-BELA associates mentions and entity representations using a similarity or distance metric between vector representations of them in a shared vector space. The results suggest that our approach achieves comparable performance to other state-of-the-art methods, while employing a much simpler model, contributing to the field of natural language processing.