Title: EmbNum: Semantic labeling for numerical values with deep metric learning,
Abstract: 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.