Machine learning for understanding the contextual semantics of tabular web sources

Author

Weerasinghe, Jagath ; Weerasinghe, Saranga ; Panditha, Akila ; Weerasinghe, Vathsala

fYear

2013

fDate

17-20 Dec. 2013

Firstpage

577

Lastpage

582

Abstract

Tables are frequently used in web sources to present relational data in a human friendly manner. Because they are intended for humans, using machines to extract such information is difficult. There are approaches such as wrappers that attempt to solve this problem, but they lack adaptability and require high maintenance. Identifying and extracting information from web tables is not a trivial task, and understanding the semantics of a web table proves to be even harder. In this paper, we introduce a machine learning based approach to understand the semantics in the data residing in tabular web sources. We suggest features that reflect the characteristics of the content in the tables and analyze their impact on the accuracy of the classification process.

Keywords

information resources; learning (artificial intelligence); semantic Web; Web tables; classification process; contextual semantics; machine learning based approach; tabular Web sources; Accuracy; Data mining; Feature extraction; HTML; Pricing; Semantics; Web pages; Artificial Intelligence; Machine Learning; Semantics; Web Tables;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial and Information Systems (ICIIS), 2013 8th IEEE International Conference on

Conference_Location

Peradeniya

Print_ISBN

978-1-4799-0908-7

Type

conf

DOI

10.1109/ICIInfS.2013.6732048

Filename

6732048