research-article

KBPearl: a knowledge base population system supported by joint entity and relation linking

Authors:

Lei ChenAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 13, Issue 7

Pages 1035 - 1049

https://doi.org/10.14778/3384345.3384352

Published: 01 March 2020 Publication History

Abstract

Nowadays, most openly available knowledge bases (KBs) are incomplete, since they are not synchronized with the emerging facts happening in the real world. Therefore, knowledge base population (KBP) from external data sources, which extracts knowledge from unstructured text to populate KBs, becomes a vital task. Recent research proposes two types of solutions that partially address this problem, but the performance of these solutions is limited. The first solution, dynamic KB construction from unstructured text, requires specifications of which predicates are of interest to the KB, which needs preliminary setups and is not suitable for an in-time population scenario. The second solution, Open Information Extraction (Open IE) from unstructured text, has limitations in producing facts that can be directly linked to the target KB without redundancy and ambiguity. In this paper, we present an end-to-end system, KBPearl, for KBP, which takes an incomplete KB and a large corpus of text as input, to (1) organize the noisy extraction from Open IE into canonicalized facts; and (2) populate the KB by joint entity and relation linking, utilizing the context knowledge of the facts and the side information inferred from the source text. We demonstrate the effectiveness and efficiency of KBPearl against the state-of-the-art techniques, through extensive experiments on real-world datasets.

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 13, Issue 7

March 2020

194 pages

ISSN:2150-8097

Editors:
Magdalena Balazinska
University of Washington
,
Xiaofang Zhou
University of Queensland, Australia

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Published: 01 March 2020

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Su CQi JYan HZou KLin ZSerra ESpezzano F(2024)Enhancing SPARQL Generation by Triplet-order-sensitive Pre-trainingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679916(4061-4065)Online publication date: 21-Oct-2024
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