@article{chen2022linkingpark,
author = {Chen, Shuang and Karaoglu, Alperen and Negreanu, Carina and Ma, Tingting and Yao, Jin-Ge and Williams, Jack and Jiang, Feng and Gordon, Andy and Lin, Chin-Yew},
title = {LinkingPark: An automatic semantic table interpretation system},
year = {2022},
month = {June},
abstract = {In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.},
url = {http://approjects.co.za/?big=en-us/research/publication/linkingpark-an-automatic-semantic-table-interpretation-system/},
journal = {Journal of Web Semantics},
}