Abstract

This paper describes our broad goal of linking tabular data to semantic knowledge graphs, as well as our specific attempts at solving the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching. Our efforts were split into a Candidate Generation and a Candidate Selection phase. The former involves searching for relevant entities in knowledge bases, while the latter involves picking the top candidate using various techniques such as heuristics (the ‘TF-IDF’approach) and machine learning (the Neural Network Ranking model). We achieve an F1 score of 0.826 without any training data on the 400000+ cells to be annotated in Round 2 CEA challenge. On CTA and CPA variants, we score 1.099 and 0.790 respectively.

Date

March 6, 2026

Authors

Avijit Thawani, Minda Hu, Erdong Hu, Husain Zafar, Naren Teja Divvala, Amandeep Singh, Ehsan Qasemi, Pedro Szekely, Jay Pujara

Journal

SemTab, ISWC Challenge

Volume

2553