Abstract
The eXtensible Business Reporting Language (XBRL) has standardized the generation of and the access to financial statements like balance sheets, but language and XBRL-taxonomy diversity makes financial data integration across national borders and jurisdictions problematic. Integrating financial data in these circumstances requires that different multilingual jurisdictional taxonomies be aligned by finding correspondences between concepts. In this chapter, we outline a logic-based approach to this important alignment problem. The approach centers around the construction of an Accounting Ontology which, acting as a common denominator, is first used to enrich the semantics of ontologized XBRL taxonomies before reasoning is applied for alignment. Initial alignment experiments conducted on the French and Spanish balance sheets yielded 73.9 % recall and 36.6 % precision, but 100 % precision, if redundant mappings are ignored.
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Notes
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See http://www.sec.gov/.
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See http://www.ebr.org/.
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The YAM++ of year 2012 does not require training.
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References
Aleksovski, Z., ten Kate, W., & van Harmelen, F. (2006). Exploiting the structure of background knowledge used in ontology matching. In Proceedings of International Workshop on Ontology Matching (OM’06).
Allen, P. (2012). Case study: Taxonomy packages - A simple specification to solve a universal problem. Interactive Business Reporting, 2, 32.
Bao, J., Rong, G., Li, X., & Ding, L. (2010). Representing financial reports on the semantic web: A faithful translation from XBRL to OWL. In Proceedings of International Symposium on Rules (RuleML’10) (pp. 144–152).
Chou, C.-C., & Chi, Y.-L. (2010). Developing ontology-based epa for representing accounting principles in a reusable knowledge component. Expert Systems with Applications, 37(3), 2316–2323.
Declerck, T., & Krieger, H.-U. (2006). Translating XBRL into description logic. an approach using protege, sesame & OWL. In Proceedings of International Conference on Business Information Systems (BIS’06) (pp. 455–467).
Declerck, T., Krieger, H.-U., Thomas, S. M., Buitelaar, P., O’Riain, S., Wunner, T., et al. (2010). Ontology-based multilingual access to financial reports for sharing business knowledge across europe. In J. Roóz & J. Ivanyos (Eds.), Internal Financial Control Assessment Applying Multilingual Ontology Framework. Kiadja a Memolux Kft., Készült a HVG Press Kft. nyomdájában.
Frankel, D. S. (2009, June). XBRL and semantic interoperability. Model Driven Architecture Journal, 3 (5 pp.). http://www.bptrends.com/bpt/wp-content/publicationfiles/SIX%2006-09-COL-MDA%20Journal%202009-06%20XBRL%20v01-00-%20Frankel.pdf.
Gailly, F., & Poels, G. (2007). Towards ontology-driven information systems: Redesign and formalization of the REA ontology. In Proceedings of the 10th International Conference on Business Information Systems (BIS’07) (pp. 245–259).
García, R., & Gil, R. (2009). Publishing XBRL as linked open data. In Proceedings of World Wide Web Workshop: Linked Data on the Web (LDOW’09) (Vol. 538).
Garnsey, M. R., & Fisher, I. E. (2008). Appearance of new terms in accounting language: A preliminary examination of accounting pronouncements and financial statements. Journal of Emerging Technologies in Accounting, 5, 17–36.
Gerber, M. C., & Gerber, A. J. (2011). Towards the development of consistent and unambiguous financial accounting standards using ontology technologies. In Proceedings of the International Conference on Accounting.
Hoffman, C., & Watson, L. (2009). XBRL for dummies. Hoboken: Wiley Publishing.
Jean-Mary, Y. R., Shironoshita, E. P., & Kabuka, M. R. (2009). Ontology matching with semantic verification. Journal of Web Semantic, 7(3), 235–251.
Jiménez-Ruiz, E., Cuenca Grau, B., Zhou, Y., & Horrocks, I. (2012). Large-scale interactive ontology matching: Algorithms and implementation. In Proceedings of European Conference on Artificial Intelligence (ECAI’12) (pp. 444–449).
Krahel, J. P. (2012). On the Formalization of Accounting Standards (Ph.D. thesis, State University of New Jersey).
Li, B., & Min, L. (2009). An ontology-augmented xbrl extended model for financial information analysis. In Proceedings of IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS’09) (pp. 99–130).
Motik, B., Shearer, R., & Horrocks, I. (2007). Optimized reasoning in description logics using hypertableaux. In Proceedings of the 21st Conference on Automated Deduction (CADE’21). Lecture Notes in Artificial Intelligence (pp. 67–83).
Nagy, M., Vargas-vera, M., & Motta, E. (2006). Dssim-ontology mapping with uncertainty. In Proceedings of International Workshop on Ontology Matching (OM’06) (pp. 115–123).
Ngo, D., & Bellahsene, Z. (2012). Yam++: A multi-strategy based approach for ontology matching task. In Proceedings of International Conference on Knowledge Engineering and Knowledge Management (EKAW’12) (pp. 421–425).
Noy, N. F. (2004). Semantic integration: A survey of ontology-based approaches. SIGMOD Record, 33, 65–70.
O’Riain, S. (2012). Semantic Paths in Business Filings Analysis (Ph.D. thesis, National University of Ireland, Galway).
O’Riain, S., Curry, E., & Harth, A. (2011). XBRL and open data for global financial ecosystems: A linked data approach. International Journal of Accounting Information Systems, 13(2), 141–162.
Shvaiko, P., & Euzenat, J. (2013). Ontology matching: State of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering, 25(1), 158–176.
Solomon, W. D., Roberts, A., Rogers, J. E., Wroe, C. J., & Rector, A. L. (2000). Having our cake and eating it too: How the galen intermediate representation reconciles internal complexity with users’ requirements for appropriateness and simplicity. In Proceedings of the AMIA Symposium, American Medical Informatics Association (pp. 819–823).
Spohr, D., Hollink, L., & Cimiano, P. (2011). A machine learning approach to multilingual and cross-lingual ontology matching. In Proceedings of International Semantic Web Conference (ISWC’11) (pp. 665–680).
Verdin, T., Maguet, G., & Thomas, S. (2012). Promoting XBRL for cross-border data exchange by business registers in europe. Interactive Business Reporting, 2, 18–21.
Acknowledgments
The work presented in this chapter has been funded in part by the EU FP7 Activity ICT-4-2.2 under Grant Agreement No. 248458, Multilingual Ontologies for Networked Knowledge (MONNET) project, and by the DFG Research Unit FOR 1513, project B1. We would especially like to thank the xEBR Working GroupFootnote 19 for their help.
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Thomas, S.M., Wu, X., Ma, Y., O’Riain, S. (2014). Semantically Assisted XBRL-Taxonomy Alignment Across Languages. In: Buitelaar, P., Cimiano, P. (eds) Towards the Multilingual Semantic Web. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43585-4_17
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