Kiril Gashteovski, Sebastian Wanner, Sven Hertling, Samuel Broscheit, Rainer Gemulla.

doi:10.24432/C53W2J

TL;DR

Open information extraction (OIE) systems extract relations and their arguments from natural language text in an unsupervised manner. The resulting extractions are a valuable resource for downstream tasks such as knowledge base construction, open question answering, or event schema induction. In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia. OPIEC complements the available OIE resources: It is the largest OIE corpus publicly available to date (over 340M triples) and contains valuable metadata such as provenance information, confidence scores, linguistic annotations, and semantic annotations including spatial and temporal information. We analyze the OPIEC corpus by comparing its content with knowledge bases such as DBpedia or YAGO, which are also based on Wikipedia. We found that most of the facts between entities present in OPIEC cannot be found in DBpedia and/or YAGO, that OIE facts often differ in the level of specificity compared to knowledge base facts, and that OIE open relations are generally highly polysemous. We believe that the OPIEC corpus is a valuable resource for future research on automated knowledge base construction.

Citation

@inproceedings{
gashteovski2019opiec,
title={{\{}OPIEC{\}}: An Open Information Extraction Corpus},
author={Kiril Gashteovski and Sebastian Wanner and Sven Hertling and Samuel Broscheit and Rainer Gemulla},
booktitle={Automated Knowledge Base Construction (AKBC)},
year={2019},
url={https://openreview.net/forum?id=HJxeGb5pTm},
doi={10.24432/C53W2J}
}