使用新对象属性丰富链接数据集
Enriching Linked Datasets with New Object Properties
摘要：
虽然有几个RDF知识库可通过LOD倡议，这样的链接数据集的本体模式不是很丰富。特别是，它们缺少对象属性。在任何两个给定类之间查找新对象属性（及其实例）的问题尚未在关联数据的上下文中详细研究。在本文中，我们提出了DART（检测任意关系丰富的T盒的关联数据）-一个无监督的解决方案，以丰富LOD云与两个给定的类之间的新对象属性。DART利用上下文相似性来从网络语料库中识别可能代表个体之间关系的文本模式。这些文本模式，然后聚类的释义检测手段，以捕捉两个给定的LOD类之间的对象属性。DART还将发现的关系完全自动映射到链接数据集中的属性。这有许多用途，如识别全新的关系，消除不相关的关系，并生成预期的属性公理。我们已经在几对类上对我们的方法进行了经验评估，发现该系统确实可以用于丰富具有新对象属性及其实例的链接数据集。我们将DART与NELL（Never-Ending Language Learning）的一个分支newOntExt系统进行了比较。我们的实验表明，DART给出了更好的结果比newOntExt的正确性，以及关系的数量。
英文摘要：
Although several RDF knowledge bases are available through the LOD initiative, the ontology schema of such linked datasets is not very rich. In particular, they lack object properties. The problem of finding new object properties (and their instances) between any two given classes has not been investigated in detail in the context of Linked Data. In this paper, we present DART (Detecting Arbitrary Relations for enriching T-Boxes of Linked Data) - an unsupervised solution to enrich the LOD cloud with new object properties between two given classes. DART exploits contextual similarity to identify text patterns from the web corpus that can potentially represent relations between individuals. These text patterns are then clustered by means of paraphrase detection to capture the object properties between the two given LOD classes. DART also performs fully automated mapping of the discovered relations to the properties in the linked dataset. This serves many purposes such as identification of completely new relations, elimination of irrelevant relations, and generation of prospective property axioms. We have empirically evaluated our approach on several pairs of classes and found that the system can indeed be used for enriching the linked datasets with new object properties and their instances. We compared DART with newOntExt system which is an offshoot of the NELL (Never-Ending Language Learning) effort. Our experiments reveal that DART gives better results than newOntExt with respect to both the correctness, as well as the number of relations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
