Abstract
In the real world, many relational facts require context; for instance, a politician holds a given elected position only for a particular timespan. This context (the timespan) is typically ignored in knowledge graph link prediction tasks, or is leveraged by models designed specifically to make use of it (i.e. n-ary link prediction models). Here, we show that the task of n-ary link prediction is easily performed using language models, applied with a basic method for constructing cloze-style query sentences. We introduce a pre-training methodology based around an auxiliary entity-linked corpus that outperforms other popular pre-trained models like BERT, even with a smaller model. This methodology also enables n-ary link prediction without access to any n-ary training set, which can be invaluable in circumstances where expensive and time-consuming curation of n-ary knowledge graphs is not feasible. We achieve state-of-the-art performance on the primary n-ary link prediction dataset WD50K and on WikiPeople facts that include literals - typically ignored by knowledge graph embedding methods.- Anthology ID:
- 2022.deelio-1.9
- Volume:
- Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland and Online
- Venue:
- DeeLIO
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 87–99
- Language:
- URL:
- https://aclanthology.org/2022.deelio-1.9
- DOI:
- 10.18653/v1/2022.deelio-1.9
- Cite (ACL):
- Angus Brayne, Maciej Wiatrak, and Dane Corneil. 2022. On Masked Language Models for Contextual Link Prediction. In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 87–99, Dublin, Ireland and Online. Association for Computational Linguistics.
- Cite (Informal):
- On Masked Language Models for Contextual Link Prediction (Brayne et al., DeeLIO 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.deelio-1.9.pdf