Knowledge Graph Embedding Compression
Published in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics • Jan 1, 2020
Authors:
Mrinmaya Sachan
Abstract
Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of ...
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