Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of automatically interpreting the tables in papers and of disambiguating the entities that they contain. To solve these problems, we propose a pipeline that employs both statistical-based classifiers and logic-based reasoning. First, our pipeline applies weakly supervised classifiers to recognize the type of tables and columns, with the help of a data labeling system and an ontology specifically designed for our purpose. Then, logic-based reasoning is used to link equivalent entities (via sameAs links) in different tables. An empirical evaluation of our approach using a corpus of papers in the Computer Science domain has returned satisfactory performance. This suggests that ours is a promising step to create a large-scale KB of scientific knowledge.

doi.org/10.1007/978-3-030-62419-4_20
Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence
19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Kruit, B.B, He, H., H, & Urbani, J. (2020). Tab2Know: Building a Knowledge Base from Tables in Scientific Papers. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-62419-4_20