Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs.
SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.
In this paper, we propose BI-REC, a conversational recommendation system for BI applications to help users accomplish their data analysis tasks.
Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs.
Our results show that: (1) our novel lookup-based method outperforms state-of-the-art lookup-based methods, (2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and (3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation.