EmbNum：使用深度度量学习对数值进行语义标记
EmbNum: Semantic labeling for numerical values with deep metric learning
摘要：
数值的语义标注是指为未知的数值属性分配语义标签的任务。语义标签可以是本体中的数值属性、知识库中的实例或由领域专家手动注释的标记数据。在本文中，我们将语义标记作为一种检索设置，其中未知属性的标签由标记数据中最相关属性的标签分配。最大的挑战之一是未知属性很少与标记数据中的相似属性具有相同的值集。为了克服这个问题，考虑到价值分布的统计解释。然而，现有的研究假设了一种特定的分布形式。在事先不了解数据的情况下，特别不宜采用开放数据。为了解决这些问题，我们提出了一个神经数字嵌入模型（Embodiment）来学习有用的表示向量的数值属性，而无需事先假设的数据分布。然后，“语义相似性”的属性之间的测量这些表示向量的欧氏距离。我们对城市数据和开放数据的实证实验表明，Embodiment显着优于国家的最先进的方法的任务的数值属性语义标记的有效性和效率。
英文摘要：
Semantic labeling for numerical values is a task of assigning semantic labels to unknown numerical attributes. The semantic labels could be numerical properties in ontologies, instances in knowledge bases, or labeled data that are manually annotated by domain experts. In this paper, we refer to semantic labeling as a retrieval setting where the label of an unknown attribute is assigned by the label of the most relevant attribute in labeled data. One of the greatest challenges is that an unknown attribute rarely has the same set of values with the similar one in the labeled data. To overcome the issue, statistical interpretation of value distribution is taken into account. However, the existing studies assume a specific form of distribution. It is not appropriate in particular to apply open data where there is no knowledge of data in advance. To address these problems, we propose a neural numerical embedding model (EmbNum) to learn useful representation vectors for numerical attributes without prior assumptions on the distribution of data. Then, the "semantic similarities" between the attributes are measured on these representation vectors by the Euclidean distance. Our empirical experiments on City Data and Open Data show that EmbNum significantly outperforms state-of-the-art methods for the task of numerical attribute semantic labeling regarding effectiveness and efficiency.
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。
