DLCC Gold Standard

Corresponding GitHub repository: DL-TC-Generator on GitHub <strong>Abstract</strong> Knowledge graph embedding is a representation learning technique which projects entities and relations in a knowledge graph to continuous vector spaces.<br> Embeddings have gained a lot of uptake and have been heavily used in link prediction and other downstream prediction tasks.<br> Most approaches are evaluated on a single task or a single group of tasks to determine their overall performance. The evaluation is then assessed in terms of how well the embedding approach performs on the task at hand, but it is hardly evaluated (and often not even deeply understood) what information the embedding approaches are <em>actually</em> learning to represent. To fill this gap, we present the DLCC (Description Logic Class Constructors) benchmark, a resource to analyze embedding approaches in terms of which kinds of classes they can represent. Two gold standards are presented, one based on the real world knowledge graph DBpedia, and one synthetic gold standard.对应GitHub仓库：GitHub平台的DL-TC-Generator <strong>摘要</strong> 知识图谱嵌入（Knowledge graph embedding）是一类将知识图谱中的实体与关系投影至连续向量空间的表示学习技术。该类嵌入表示已获得广泛应用，并被大量用于链接预测及其他下游预测任务。现有多数方法仅通过单一任务或单组任务来评估其整体性能，此类评估仅围绕嵌入方法在当前任务中的表现展开，却极少（甚至往往未得到深入探究）分析这些嵌入方法<em>实际</em>学习到了何种表示信息。为填补这一研究空白，我们推出了DLCC（描述逻辑类构造器，Description Logic Class Constructors）基准数据集，该资源可用于分析嵌入方法所能表示的各类别。本次工作提供了两份金标准数据集：一份基于真实世界知识图谱DBpedia，另一份为合成金标准数据集。