Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models

Victor Prokhorov, Mohammad Taher Pilehvar, Nigel Collier


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
URL:
https://www.aclweb.org/anthology/N19-1196
DOI:
10.18653/v1/N19-1196
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PDF:
https://www.aclweb.org/anthology/N19-1196.pdf