论文标题
联合实体对准和悬空实体检测的准确无监督方法
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
论文作者
论文摘要
知识图集成通常遭受了无法找到一致性交叉知识图（kgs）的广泛存在的悬挂实体。在大多数实际情况下，悬挂的实体集都无法使用，并且手动挖掘由具有相同含义的实体组成的实体对，这是劳动力消费。在本文中，我们提出了一种新型的与联合实体比对（EA）和悬挂实体检测（DED）的精确无监督方法，称为UED。 UED地雷矿山的字面语义信息以生成伪实体对和EA的全球指导对准信息，然后利用EA结果来帮助DED。我们构建了一个医学跨语性知识图数据集，并为EA和DED任务提供数据。广泛的实验表明，在EA任务中，UED取得了EA结果，可与最先进的EA基线相媲美，并通过结合监督的EA数据来胜过当前最新的EA方法。对于DED任务，UED在没有监督的情况下获得了高质量的结果。
Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is labor-consuming. In this paper, we propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED. The UED mines the literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA and then utilizes the EA results to assist the DED. We construct a medical cross-lingual knowledge graph dataset, MedED, providing data for both the EA and DED tasks. Extensive experiments demonstrate that in the EA task, UED achieves EA results comparable to those of state-of-the-art supervised EA baselines and outperforms the current state-of-the-art EA methods by combining supervised EA data. For the DED task, UED obtains high-quality results without supervision.
