Ontology-Based Information Extraction From PDF Documents With Xonto

Abstract

Information extraction is of paramount importance in several real world applications in the areas of business, competitive and military intelligence because it enables to acquire information contained in unstructured documents and store them in structured forms. Unstructured documents have different internal encodings, one of the most diffused encoding is the visualization-oriented Adobe portable document format (PDF). Although several sophisticated and indeed complex approaches were proposed, they are still limited in many aspects. In particular, existing information extraction systems cannot be applied to PDF documents because of their completely unstructured nature that pose many issues in defining IE approaches. In this paper the novel ontology-based system named XONTO, that allows the semantic extraction of information from PDF documents, is presented. The XONTO system is founded on the idea of self-describing ontologies in which objects and classes can be equipped by a set of rules named descriptors. These rules represent patterns that allow to automatically recognize and extract ontology objects contained in PDF documents also when information is arranged in tabular form. This way a self-describing ontology expresses the semantic of the information to extract and the rules that, in turn, populate itself. In the paper XONTO system behaviors and structure are sketched by means of a running example.

Towards a Language for Representing and Managing the Semantics of Big Data

“…The syntax is based on the intuitive logic programming. In particular, ontological constructs are partially derived from OntoDLP (Calimeri et al, 2003) (Ricca and Leone, 2007), whereas acquisition formalism are based on the XOnto language (Oro and Ruffolo, 2008) (Oro et Al, 2009). OntoDLP introduces many interesting features, including complex types, e.g.…”

“…Database and linguistic descriptors, which enable to extract and integrate data available in heterogeneous data sources, are presented in the following. Descriptors are founded on the basic idea described in (Oro and Ruffolo, 2008) and (Oro et Al, 2009) which describe a system for Information Extraction (IE) from PDF documents. It represents an approach founded on the idea that objects and classes of ontologies can be equipped by a set of rules that describe how recognize and extract objects contained into an external source.…”

A Knowledge Representation Formalism for Semantic Business Process Management

“…The information extraction task is performed by exploiting the semantic information extraction approach described in Section 5. Extracted information are stored as ontology instances Oro et al (2009a).…”

“…A prototype of the SD-KRF has been implemented by combining the JBPM engine JPDL (2011) and the XONTO system Oro et al (2009a). It is designed to follow a clinical processes life-cycle model based on 3 phases: processes and ontologies design and implementations, execution and monitoring, analysis.…”