基于文本辅助正则化的知识图谱补全
Knowledge Graph Completion with Text-aided Regularization
摘要：
知识图谱完成是通过估计可能的实体或专有名词来扩展知识图谱/库的任务，这些实体或专有名词可以使用一组预定义的关系或描述两个事物的互连的动词/谓词来连接。一般来说，我们将此问题描述为向当前顶点和边的网络添加新边。传统的方法主要集中在利用图本身固有的图形信息并训练相应的嵌入来描述这些信息;然而，我们认为与实体相关的语料库也应该包含能够积极影响嵌入的信息，以更好地进行预测。在我们的项目中，我们尝试了多种方法来使用提取的或原始的文本信息，以帮助现有的KG嵌入框架达到更好的预测结果，在损失函数中的正则化部分添加相似性函数。结果表明，我们已经取得了体面的改进，基线KG嵌入方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
