Semantic-based Access to Digital Document Databases

Discovering significant meta-information from document collections is a critical factor for knowledge distribution and preservation. This paper presents a system that implements intelligent document processing techniques, by combining strategies for the layout analysis of electronic documents with incremental first-order learning in order to automatically classify the documents and their layout components according to their semantics. Indeed, an in-deep analysis of specific layout components can allow the extraction of useful information to improve the semantic-based document storage and retrieval tasks. The viability of the proposed approach is confirmed by experiments run in the real-world application domain of scientific papers.