论文标题
语义实体丰富通过利用多语言描述进行链接预测
Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction
论文作者
论文摘要
大多数知识图（kgs）包含各种自然语言实体的文本描述。这些实体的描述提供了有价值的信息，这些信息可能不会在KG的结构化部分中明确表示。基于这个事实，已经提出了一些链接预测方法，这些链接预测方法利用实体的文本描述中提出的信息来学习（单语）kgs的表示形式。但是，这些方法仅以一种语言使用实体描述，而忽略了以下语言给出的描述可以提供互补信息，从而提供其他语义。在该立场论文中，将讨论有效利用多语言实体描述的问题，以便在kgs中进行链接预测的目的以及该问题的潜在解决方案。
Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.
