Extracting Novel Facts from Tables for Knowledge Graph Completion

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Abstract

We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2019
Subtitle of host publication18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings
EditorsChiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon
PublisherSpringer
Pages364-381
Number of pages18
Volume1
ISBN (Electronic)9783030307936
ISBN (Print)9783030307929
DOIs
Publication statusPublished - 2019
Event18th International Semantic Web Conference, ISWC 2019 - Auckland, New Zealand
Duration: 26 Oct 201930 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11778 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Semantic Web Conference, ISWC 2019
CountryNew Zealand
CityAuckland
Period26/10/1930/10/19

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