Abstract
Analysts spend a disproportionate amount of time with financial data curation before they are able to compare company performances in an analysis. The Extensible Business Reporting Language (XBRL) for annotating financial facts is suited for automatic processing to increase information quality in financial analytics. Still, XBRL does not solve the problem of data integration as required for a holistic view on companies. Semantic Web technologies promise benefits for financial data integration, yet, existing literature lacks concrete case studies. In this paper, we present the Financial Information Observation System (FIOS) that uses Linked Data and multidimensional modelling based on the RDF Data Cube Vocabulary for accessing and representing relevant financial data. FIOS fulfils the information seeking mantra of “overview first, zoom and filter, then details on demand”, integrates yearly and quarterly balance sheets, daily stock quotes as well as company and industry background information and helps analysts creating their own analyses with Excel-like functionality.
Chapter PDF
Similar content being viewed by others
Keywords
References
Bao, J., Rong, G., Li, X., Ding, L.: Representing Financial Reports on the Semantic Web: a Faithful Translation from XBRL to OWL. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 144–152. Springer, Heidelberg (2010)
Burdick, D., Hernández, M.A., Ho, H., Koutrika, G., Krishnamurthy, R., Popa, L., Stanoi, I., Vaithyanathan, S., Das, S.R.: Extracting, Linking and Integrating Data from Public Sources: A Financial Case Study. IEEE Data Eng. Bull. (2011)
Carretié, H., Torvisco, B., García, R.: Using Semantic Web Technologies to Facilitate XBRL-based Financial Data Comparability. In: International Workshop on Finance and Economics on the Semantic Web (2012)
Debreceny, R.: Feeding the information value chain: Deriving analytical ratios from XBRL filings to the SEC. Tech. rep. (2010)
García, R., Gil, R.: Triplificating and linking XBRL financial data. Information Storage and Retrieval (2010)
Hogan, A., Harth, A., Umbrich, J., Kinsella, S., Polleres, A., Decker, S.: Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine. Web Semantics: Science, Services and Agents on the World Wide Web 9, 365–401 (2011)
Kämpgen, B., Harth, A.: No Size Fits All – Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 290–304. Springer, Heidelberg (2013)
O’Riain, S., Curry, E., Harth, A.: XBRL and open data for global financial ecosystems: A linked data approach. Int. J. of Accounting Information Systems 13 (2012)
O’Riain, S., Coughlan, B., Buitelaar, P., Declerk, T., Krieger, U., Marie-Thomas, S.: Cross-Lingual Querying and Comparison of Linked Financial and Business Data. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 242–247. Springer, Heidelberg (2013)
Spies, M.: An ontology modelling perspective on business reporting. Information Systems 35 (2010)
Tseng, F.S.: Integrating heterogeneous data warehouses using XML technologies. Journal of Information Science 31 (2005)
Wenger, M., Thomas, M.A., Babb Jr., J.S.: An Ontological Approach to XBRL Financial Statement Reporting An Ontological Approach to XBRL Financial Statement Reporting. In: AMCIS 2011 Proceedings (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kämpgen, B., Weller, T., O’Riain, S., Weber, C., Harth, A. (2014). Accepting the XBRL Challenge with Linked Data for Financial Data Integration. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds) The Semantic Web: Trends and Challenges. ESWC 2014. Lecture Notes in Computer Science, vol 8465. Springer, Cham. https://doi.org/10.1007/978-3-319-07443-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-07443-6_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07442-9
Online ISBN: 978-3-319-07443-6
eBook Packages: Computer ScienceComputer Science (R0)