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milIE: Modular & Iterative Multilingual Open Information Extraction ...
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Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
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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.
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Keyword:
004 Informatik
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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
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On aligning OpenIE extractions with Knowledge Bases: A case study
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