Abstract

There are millions of high-quality tables available in Wikipedia. These tables cover many domains and contain useful information. To make use of these tables for data discovery or data integration, we need precise descriptions of the concepts and relationships in the data, known as semantic descriptions. However, creating semantic descriptions is a complex process requiring considerable manual effort and can be error prone. In this paper, we present a novel probabilistic approach for automatically building semantic descriptions of Wikipedia tables. Our approach leverages hyperlinks in a Wikipedia table and existing knowledge in Wikidata to construct a graph of possible relationships in the table and its context, and then it uses collective inference to distinguish genuine and spurious relationships to form the final semantic description. In contrast to existing methods, our solution can handle tables that …

Date

September 22, 2025

Authors

Binh Vu, Craig A Knoblock, Pedro Szekely, Minh Pham, Jay Pujara

Conference

The Semantic Web–ISWC 2021: 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24–28, 2021, Proceedings 20

Pages

304-320

Publisher

Springer International Publishing