Abstract
A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6%, 6.0%, and 16.4% in three common metrics BLEU, METEOR, and TER, respectively.- Anthology ID:
- P18-1151
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1627–1637
- Language:
- URL:
- https://aclanthology.org/P18-1151
- DOI:
- 10.18653/v1/P18-1151
- Cite (ACL):
- Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, and Wei Wang. 2018. GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1627–1637, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data (Trisedya et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-1151.pdf
- Data
- WebNLG