A Novel Approach to Web Information Extraction

Business Intelligence requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers. The Web is the largest source of information nowadays. Unfortunately, the information it provides is available in semi-structured human-friendly formats, which makes it difficult to be processed by automated business processes. Classical propositional and ILP machine-learning techniques have been applied for this purpose. However, the former have not enough expressive power, whereas the latter are more expressive but intractable with large datasets. Propositionalisation was devised as a means to provide propositional techniques with more expressive power, enabling them to exploit structural information in a propositional way that allows them to be efficient. In this paper, we present a proposal to extract information from semi-structured web documents that uses this approach. It leverages a classical propositional machine learning technique and enhances it with the ability to learn from an unbounded context, which helps increase its precision and recall. Our experiments prove that our proposal outperforms other state-of-art techniques in the literature.