论文标题
蛋糕：多视图知识图的可扩展常识感知框架
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
论文作者
论文摘要
知识图在不可避免的情况下仍然不完整时存储了大量事实三元组。先前的知识图完成（KGC）模型预测了仅依靠事实视图数据的实体之间缺少联系，而忽略了宝贵的常识性知识。先前的知识图嵌入（KGE）技术受到无效的负面抽样和事实视图链接预测的不确定性，从而限制了KGC的性能。为了应对上述挑战，我们提出了一种新颖而可扩展的常识性知识嵌入（CAKE）框架，以自动从具有实体概念的事实三元组中提取常识。产生的常识增强了有效的自学，以促进高质量的负抽样（NS）和联合常识和事实视图链接预测。 KGC任务的实验结果表明，组装我们的框架可以增强原始KGE模型的性能，并且提出的常识性NS模块优于其他NS技术。此外，我们提出的框架很容易适应各种KGE模型，并解释了预测的结果。
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.
