Abstract

Semantic labeling is the process of mapping attributes in data sources to classes in an ontology and is a necessary step in heterogeneous data integration. Variations in data formats, attribute names and even ranges of values of data make this a very challenging task. In this paper, we present a novel domain-independent approach to automatic semantic labeling that uses machine learning techniques. Previous approaches use machine learning to learn a model that extracts features related to the data of a domain, which requires the model to be re-trained for every new domain. Our solution uses similarity metrics as features to compare against labeled domain data and learns a matching function to infer the correct semantic labels for data. Since our approach depends on the learned similarity metrics but not the data itself, it is domain-independent and only needs to be trained once to work effectively …

Date

September 22, 2025

Authors

Minh Pham, Suresh Alse, Craig A Knoblock, Pedro Szekely

Conference

The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I 15

Pages

446-462

Publisher

Springer International Publishing