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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 – 3 of 3 1 Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction Gashteovski, Kiril; Gemulla, Rainer; Wang, Yanjie; Broscheit, Samuel. - : Association for Computational Linguistics, 2020 Abstract: Open Information Extraction systems extract(“subject text”, “relation text”, “object text”)triples from raw text. Some triples are textualversions of facts, i.e., non-canonicalized men-tions of entities and relations. In this paper, weinvestigate whether it is possible to infernewfacts directly from theopen knowledge graphwithout any canonicalization or any supervi-sion from curated knowledge. For this pur-pose, we propose the open link prediction task,i.e., predicting test facts by completing(“sub-ject text”, “relation text”, ?)questions. Anevaluation in such a setup raises the question ifa correct prediction is actually anewfact thatwas induced by reasoning over the open knowl-edge graph or if it can be trivially explained.For example, facts can appear in different para-phrased textual variants, which can lead to testleakage. To this end, we propose an evaluationprotocol and a methodology for creating theopen link prediction benchmark OLPBENCH.We performed experiments with a prototypicalknowledge graph embedding model for openlink prediction. While the task is very chal-lenging, our results suggests that it is possibleto predict genuinely new facts, which can notbe trivially explained. Keyword: 004 Informatik URL: https://madoc.bib.uni-mannheim.de/55724/1/Can%20We%20Predict%20New%20Facts%20with%20Open%20Knowledge%20Graph%20Embeddings%20A%20Benchmark%20for%20Open%20Link%20Prediction.pdf https://madoc.bib.uni-mannheim.de/55724/ https://madoc.bib.uni-mannheim.de/55724 BASE Hide details 2 On evaluating embedding models for knowledge base completion Gemulla, Rainer; Wang, Yanjie; Broscheit, Samuel. - : Association for Computational Linguistics, 2019 BASE Show details 3 Contention of Lust, Caution: Sexuality, Visuality and Female Subjectivity Wang, Yanjie In: Asian and Asian American Studies Faculty Works (2010) BASE Show details

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