On Relational Learning for Information Extraction

The extraction and integration of data from multiples sources are required in current companies which manage their business process by heterogeneous collaborating applications. However, integrating web applications is an arduous task because they are intended for human consumption and they do not provide APIs to access to their data automatically.Web Information extractors are used for this purpose but, they mostly provide ad-hoc highly domain dependent solutions. In this paper we aim at devising Information Extractors with a FOIL based core algorithm. It is a widely used first order rule learning algorithm since their rules are substantially more expressive and allow to learn complex concepts that cannot be represented in the attribute-value format. Furthermore, we focus on integrating other scoring functions to check if we can improve the rule search guide speeding up the learning process in order to make FOIL tractable in real-world domains such as Web sources.