CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion

• URL: http://arxiv.org/abs/2202.13785v1
• Date: Fri, 25 Feb 2022 03:30:22 GMT
• Title: CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
• Authors: Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu
• Abstract summary: Previous knowledge graph embedding techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction. We propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts.
• Score: 43.172893405453266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results.

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