Ontology-Based Content Trust Support of Expert Information Resources in Quantitative Spectroscopy Alexander Fazliev 1 , Alexey Privezentsev 1 , Dmitry Tsarkov 2 , and Jonathan Tennyson 3 1 V.E.Zuev Institute of Atmospheric Optics SB RAS, Zuev Square 1, 634021, Tomsk, Russia {faz,remake}@iao.ru 2 School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK tsarkov@cs.man.ac.uk 3 Department of Physics and Astronomy, University College London, London WC1E 6BU, UK jtennyson@ucl.ac.uk Abstract. An approach to assessing the content trust of information resources based on a publishing criterion has been developed and ap- plied to several tens of spectroscopic expert datasets. The results repre- sented as an OWL-ontology are shown to be accessible to programmable agents. The assessments enable the amount of measured and calculated trusted and distrusted data for spectroscopic quantities and ranges of their change in expert datasets to be determined. Building knowledge bases of this kind at virtual data centers intended for data intensive sci- ence will provide realization of an automatic selection of spectroscopic information resources exhibiting a high degree of trust. Keywords: Trust, OWL Ontology, Quantitative Spectroscopy. 1 Introduction Spectral line parameters are used in different subject domains: remote sensing, climate studies, astronomy, etc. Data of this type are in great demand, and the number of expert data providers is increasing progressively [10, 5, 7, 4, 18, 17, 26]. More stringent requirements are imposed on data quality, including data accu- racy, completeness, validity, trust and resource consistency. For a wide range of applied tasks, currently available expert data are shown to be inadequate to meet the requirements, because they contain outdated, distrusted and incom- plete information [14]. Quantitative spectroscopy is a data-intensive science dealing with information collected over the course of ninety years, but it is in the last six years when se- mantic technologies have been used in this domain. Processing of the long-term P. Klinov and D. Mouromtsev (Eds.): KESW 2013, CCIS 394, pp. 15–28, 2013. c  Springer-Verlag Berlin Heidelberg 2013