CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
This work addresses the limitation of ignoring commonsense knowledge in knowledge graph completion, offering a scalable solution for improving accuracy in AI systems that rely on structured data.
The paper tackles the problem of incomplete knowledge graphs by proposing a commonsense-aware framework that enhances knowledge graph completion models, achieving superior performance in negative sampling and link prediction compared to existing techniques.
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.