Abstract
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in relationship triples. R-MeN considers each triple as a sequence of 3 input vectors that recurrently interact with a memory using a transformer self-attention mechanism. Thus R-MeN encodes new information from interactions between the memory and each input vector to return a corresponding vector. Consequently, R-MeN feeds these 3 returned vectors to a convolutional neural network-based decoder to produce a scalar score for the triple. Experimental results show that our proposed R-MeN obtains state-of-the-art results on SEARCH17 for the search personalization task, and on WN11 and FB13 for the triple classification task.- Anthology ID:
- 2020.acl-main.313
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3429–3435
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.313
- DOI:
- 10.18653/v1/2020.acl-main.313
- Cite (ACL):
- Dai Quoc Nguyen, Tu Nguyen, and Dinh Phung. 2020. A Relational Memory-based Embedding Model for Triple Classification and Search Personalization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3429–3435, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Relational Memory-based Embedding Model for Triple Classification and Search Personalization (Nguyen et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.acl-main.313.pdf
- Code
- daiquocnguyen/R-MeN