选择与增强：增强型密集检索知识图谱增强
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation
AI总结：
本文提出一种多任务框架，通过检索器模型选择与知识图谱实体高度相关的文本描述并联合对齐或增强KG嵌入，在链接预测任务上较传统CNN方法显著提升了MRR和Hits@10指标。
摘要：
将文本信息注入知识图谱（KG）实体表示中，在改善NLP社区中面向KG任务的性能方面一直是一项值得探索的工作。用于增强KG嵌入的外部知识范围广泛，从语义丰富的词法依赖解析特征，到一组相关关键词，再到来自外部语料库（如Wikipedia）提供的完整文本描述等。尽管这种创新（文本增强的KG嵌入）取得了进展，但本工作提出它还可以进一步改进。由于文本固有的词汇歧义，使用单一文本描述不足以充分表示实体，因此我们提出了一个多任务框架，该框架联合选择与KG实体相关的一组文本描述，并将KG嵌入与文本描述进行对齐或增强。与以往插入知识库中声明的正式实体描述的工作不同，该框架利用检索器模型选择性地识别更丰富或高度相关的文本描述来增强实体。此外，该框架将增强过程中使用的描述数量视为一个参数，从而允许在确定合适数量之前对多个数量进行枚举。链接预测的实验结果表明，与使用传统CNN的文本增强知识图谱增强方法相比，平均倒数排名（MRR）和Hits@10得分分别提高了5.5%和3.5%。
英文摘要：
Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NLP community. External knowledge often adopted to enhance KG embeddings ranges from semantically rich lexical dependency parsed features to a set of relevant key words to entire text descriptions supplied from an external corpus such as wikipedia and many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made, the proposal in this work suggests that it can be improved even further. Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions. Different from prior work that plugs formal entity descriptions declared in knowledge bases, this framework leverages a retriever model to selectively identify richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the framework treats the number of descriptions to use in augmentation process as a parameter, which allows the flexibility of enumerating across several numbers before identifying an appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively, in comparison to text-enhanced knowledge graph augmentation methods using traditional CNNs.
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。
