Embedding Multimodal Relational Data for Knowledge Base Completion

Pouya Pezeshkpour, Liyan Chen, Sameer Singh


Abstract
Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Further, using these learned embedings and different neural decoders, we introduce a novel multimodal imputation model to generate missing multimodal values, like text and images, from information in the knowledge base. We enrich existing relational datasets to create two novel benchmarks that contain additional information such as textual descriptions and images of the original entities. We demonstrate that our models utilize this additional information effectively to provide more accurate link prediction, achieving state-of-the-art results with a considerable gap of 5-7% over existing methods. Further, we evaluate the quality of our generated multimodal values via a user study.
Anthology ID:
D18-1359
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3208–3218
Language:
URL:
https://aclanthology.org/D18-1359
DOI:
10.18653/v1/D18-1359
Bibkey:
Cite (ACL):
Pouya Pezeshkpour, Liyan Chen, and Sameer Singh. 2018. Embedding Multimodal Relational Data for Knowledge Base Completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3208–3218, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Embedding Multimodal Relational Data for Knowledge Base Completion (Pezeshkpour et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1359.pdf
Attachment:
 D18-1359.Attachment.zip
Video:
 https://aclanthology.org/D18-1359.mp4
Code
 pouyapez/mkbe +  additional community code
Data
MovieLensYAGO