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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 milIE: Modular & Iterative Multilingual Open Information Extraction ... Kotnis, Bhushan; Gashteovski, Kiril; Rubio, Daniel Oñoro. - : arXiv, 2021 BASE Show details 2 Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction Gashteovski, Kiril; Gemulla, Rainer; Wang, Yanjie. - : Association for Computational Linguistics, 2020 BASE Show details 3 On aligning OpenIE extractions with Knowledge Bases: A case study Gashteovski, Kiril; Gemulla, Rainer; Kotnis, Bhushan. - : Association for Computational Linguistics (ACL), 2020 BASE Show details 4 OPIEC: An open information extraction corpus Gashteovski, Kiril [Verfasser]; Wanner, Sebastian [Verfasser]; Hertling, Sven [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2019 DNB Subject Category Language Show details 5 OPIEC: An open information extraction corpus Gashteovski, Kiril; Wanner, Sebastian; Hertling, Sven; Broscheit, Samuel; Gemulla, Rainer. - : OpenReview.net, 2019 Abstract: 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. Keyword: 004 Informatik URL: https://madoc.bib.uni-mannheim.de/48226/ https://madoc.bib.uni-mannheim.de/48226 https://madoc.bib.uni-mannheim.de/48226/7/OPIEC%20An%20Open%20Information%20Extraction%20Corpus.pdf BASE Hide details 6 MinIE: minimizing facts in open information extraction Corro, Luciano del; Gashteovski, Kiril; Gemulla, Rainer. - : Association for Computational Linguistics, 2017 BASE Show details

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