Abstract
We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. Specifically, given the encoded state of an input text, our decoder directly predicts paths in the knowledge graph, starting from the root and ending at the the target node following hypernym-hyponym relationships. In this way, and in contrast to other text-to-entity mapping systems, our model outputs hierarchically structured predictions that are fully interpretable in the context of the underlying ontology, in an end-to-end manner. We present a proof-of-concept experiment with encouraging results, comparable to those of state-of-the-art systems.- Anthology ID:
- N19-1196
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1968–1976
- Language:
- URL:
- https://aclanthology.org/N19-1196
- DOI:
- 10.18653/v1/N19-1196
- Cite (ACL):
- Victor Prokhorov, Mohammad Taher Pilehvar, and Nigel Collier. 2019. Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1968–1976, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (Prokhorov et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/N19-1196.pdf
- Code
- VictorProkhorov/Text2Path