Ontology-based information extraction and integration from heterogeneous data sources

Abstract

In this paper we present the design, implementation and evaluation of SOBA, a system for ontology-based information extraction from heterogeneous data resources, including plain text, tables and image captions. SOBA is capable of processing structured information, text and image captions to extract information and integrate it into a coherent knowledge base. To establish coherence, SOBA interlinks the information extracted from different sources and detects duplicate information. The knowledge base produced by SOBA can then be used to query for information contained in the different sources in an integrated and seamless manner. Overall, this allows for advanced retrieval functionality by which questions can be answered precisely. A further distinguishing feature of the SOBA system is that it straightforwardly integrates deep and shallow natural language processing to increase robustness and accuracy. We discuss the implementation and application of the SOBA system within the SmartWeb multimodal dialog system. In addition, we present a thorough evaluation of the different components of the system. However, an end-to-end evaluation of the whole SmartWeb system is out of the scope of this paper and has been presented elsewhere by the SmartWeb consortium. r

A conceptualization of a nuclear or radiological emergency

“…The resulting representation abstracts away from varying orthographies and alternative or descriptive ways of referring to the same entity, such as 'nuclear power', 'atomic power' and 'power generated from nuclear fission' and also provides machineprocessable data regarding relations and events within which these entities are involved. In ontology-based information extraction (OBIE) an IE system both retrieves from and provides data to an ontology: extraction starts with a seed ontology and, in the course of the process, the extracted information is used to populate the ontology improving, in return, the performance of the IE system which exploits the populated ontology (McDowell and Cafarella, 2006;Buitelaar et al, 2008;Fragkou et al, 2008;Konstantopoulos et al, 2009).…”

Proceedings of the Third Workshop on Semantic Web and Information Extraction

“…With respect to the extraction methodology, our work is similar to Saggion et al (2007) and Buitelaar et al (2008), in that a combination of gazetteers and extraction rules is derived from the underlying ontology, in order to adapt the workflow to the domain of interest. A schema in terms of a reporting standard has recently been proposed by the MIASCI-consortium (Lemmon et al, 2014, Minimum Information About a Spinal Cord Injury Experiment).…”

Enhanced semantic representation for improved ontology-based information retrieval

“…For example, an ontology in the domain of engineering design needs to be based on design knowledge and cognitive studies, and include concepts related to product functionality, performance, material, manufacturing process, and environments [22]. The knowledge capability of the system can be enhanced by customising and integrating several predefined ontologies [23]. However, such knowledge engineering work is complex, expensive and time-consuming, as it requires a lot of human effort.…”