Knowledge Graph Completion is a task of expanding the knowledge graph/base through estimating possible entities, or proper nouns, that can be connected using a set of predefined relations, or verb/predicates describing interconnections of two things. Generally, we describe this problem as adding new edges to a current network of vertices and edges. Traditional approaches mainly focus on using the existing graphical information that is intrinsic of the graph and train the corresponding embeddings to describe the information; however, we think that the corpus that are related to the entities should also contain information that can positively influence the embeddings to better make predictions. In our project, we try numerous ways of using extracted or raw textual information to help existing KG embedding frameworks reach better prediction results, in the means of adding a similarity function to the regularization part in the loss function. Results have shown that we have made decent improvements over baseline KG embedding methods.
翻译:完成知识图是一项任务,即通过估计可能的实体或适当的名词来扩大知识图/基础,这些实体可以通过一套预先确定的关系或动词/预言来连接,或者通过描述两种事物的相互联系。一般来说,我们把这个问题描述为在目前的脊椎和边缘网络中增加新的边缘。传统方法主要侧重于使用图中固有的现有图形信息,并培训相应的嵌入来描述信息;然而,我们认为,与实体相关的材料还应包含能够积极影响嵌入的信息,从而更好地作出预测。在我们的项目中,我们尝试了多种方法,利用提取或原始文本信息来帮助现有的 KG 嵌入框架取得更好的预测结果,从而在损失函数的正规化部分增加类似功能。结果显示,我们在基线 KG 嵌入方法方面已经做了体面的改进。