经验：DBpedia和Freebase上的类型对齐
Experience: Type alignment on DBpedia and Freebase
摘要：
关联开放数据在发布数据的数量和种类上都有所增长。由于这种多样性，许多不同类型的实例（例如Person）可以在已发布的数据集中找到。类型对齐是在两个这样的数据集之间自动匹配类型（可能以多对多的方式）的问题。类型对齐是实例匹配中一个重要的预处理步骤。实例匹配涉及标识引用同一基础实体的实例对。通过先验地执行类型对齐，只有符合对齐类型的实例才被一起处理，从而导致显著的节省。本文描述了两个大型跨域RDF知识图谱DBpedia和Freebase的类型对齐经验，这两个知识图谱包含数百甚至数千个独特类型。具体来说，我们提出了一个基于MapReduce的类型对齐算法，并表明至少有三种合理的方式来评估类型对齐实例匹配的更大的上下文内。我们评论这些结果的一致性，并注意到一些一般的观察研究人员评估类似的算法跨域图。
英文摘要：
Linked Open Data exhibits growth in both volume and variety of published data. Due to this variety, instances of many different types (e.g. Person) can be found in published datasets. Type alignment is the problem of automatically matching types (in a possibly many-many fashion) between two such datasets. Type alignment is an important preprocessing step in instance matching. Instance matching concerns identifying pairs of instances referring to the same underlying entity. By performing type alignment a priori, only instances conforming to aligned types are processed together, leading to significant savings. This article describes a type alignment experience with two large-scale cross-domain RDF knowledge graphs, DBpedia and Freebase, that contain hundreds, or even thousands, of unique types. Specifically, we present a MapReduce-based type alignment algorithm and show that there are at least three reasonable ways of evaluating type alignment within the larger context of instance matching. We comment on the consistency of those results, and note some general observations for researchers evaluating similar algorithms on cross-domain graphs.
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