Correcting Knowledge Base Assertions

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Ernesto Jimenez-Ruiz, Erik B. Myklebus, Xi Chen, Ian Horrocks, Jiaoyan Chen
Journal/Conference Name The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
Paper Category
Paper Abstract The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB.
Date of publication 2020
Code Programming Language Python

Copyright Researcher 2022