An Open-World Extension to Knowledge Graph Completion Models
| Autor: | Shah, Haseeb, Villmow, Johannes, Ulges, Adrian, Schwanecke, Ulrich, Shafait, Faisal |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | AAAI-19 Vol 33 (2019) 3044-3051 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1609/aaai.v33i01.33013044 |
| Popis: | We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual description. Our model combines a regular link prediction model learned from a knowledge graph with word embeddings learned from a textual corpus. After training both independently, we learn a transformation to map the embeddings of an entity's name and description to the graph-based embedding space. In experiments on several datasets including FB20k, DBPedia50k and our new dataset FB15k-237-OWE, we demonstrate competitive results. Particularly, our approach exploits the full knowledge graph structure even when textual descriptions are scarce, does not require a joint training on graph and text, and can be applied to any embedding-based link prediction model, such as TransE, ComplEx and DistMult. Comment: 8 pages, accepted to AAAI-2019 |
| Databáze: | arXiv |
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