论文标题
实体类型使用嵌入在知识图中的预测
Entity Type Prediction in Knowledge Graphs using Embeddings
论文作者
论文摘要
开放知识图（例如DBPEDIA，WIKIDATA，YAGO）已被认为是数据挖掘和信息检索领域中不同应用的骨干。因此，知识图（kg）的完整性和正确性至关重要。这些KG大多数主要是通过从Wikipedia快照中提取的自动信息提取或用户提供的信息积累或使用启发式方法来创建的。但是，已经观察到这些KGS的类型信息通常是嘈杂，不完整和不正确的。为了解决这个问题，在这项工作中提出了一种多标签分类方法，用于使用kg嵌入的实体分类。我们将方法与当前最新类型的预测方法进行了比较，并报告了KGS实验。
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.
