Translating XBRL Into Description Logic. An Approach Using Protege, Sesame & OWL

In the context of the eTen project, WINS, a web-based business intelligence service to public and private financial institutions has been designed and implemented. One of the goals of the project was to provide new financial knowledge on companies from information gathered through interoperable information services. The services were implemented under the new emerging standard XBRL used for financial reporting. We sketch how relevant financial information was extracted from annual financial reportings. We also show at the same time the limitations we encountererd with the XBRL schema, due to the lack of reasoning support over XML-based data and information extracted from documents. To overcome these difficulties, we describe the “ontologization” of XBRL, which we assume to be a necessary requisite for large intelligent web-based financial information and decision support systems. 1 General Background In the context of the eTen project, WINS, a web-based business intelligence service to public and private financial institutions has been designed and implemented. One of the goals of the project was to provide new financial knowledge on companies from information gathered through interoperable information services [1]. The services were implemented under the new emerging standard called XBRL (eXtensible Business Reporting Language) used for financial reporting. In the following, we sketch how relevant financial information was extracted from annual financial reportings. We also show at the same time the limitations we encountererd with the XBRL schema, due to the lack of reasoning support over XML-based data and information extracted from documents that is finally mapped onto XBRL instances. In the first part of our submission, we just summarize our way of information extraction guided by XBRL, and afterwards describing our work dedicated to the ontologization of XBRL, which we assume to be a necessary requisite for large intelligent web-based financial information and decision support systems. The work described here will be further carried out within an Integrated Project of the 6th Framework, called MUSING (MUlti-Industry, Semantic-based next generation business Intelligence). 1 ETEN 2003/1, Grant agreement nr. C51083. WINS stands for Web-based Intelligence for common-interest fiscal Networked Services’. 2 For more information, see XBRL International: http://www.xbrl. org. 2 Incremental Information Extraction Guided by XBRL The next three subsections present the basic setting, viz., XBRL-guided information extraction of structured and unstructured documents. 2.1 Knowledge-Driven Information Extraction from Structured and Unstructured Documents The actual input for the information extraction (IE) task in WINS consists of balance sheets in PDF format, containing structured forms (tables) and free text (included, for example, in the annexes of balance sheets). Relevant information extracted from these sources are merged and mapped onto the XBRL format. A terminological clarification should be given at this place. IE often refers to the task of filling useror application-defined templates with the result of information detected by natural language analysis tools in textual documents. For certain applications, knowledge bases are available, supporting the IE task. Such knowledge might consist of taxonomies, thesauri, or ontologies. In this case, knowledge-driven IE tries to populate knowledge bases with instances detected in the textual documents. This was the situation in WINS, where the XBRL taxonomy was guiding the IE task through the analysis of both tabular data and free text. In the end, an XBRL structure should be instantiated with the information extracted from the annual reports of companies. 2.2 The Mapping Process from Text to XBRL The mapping process has been implemented within a Web service made available to the WINS partners. The Web service operates on PDF/text files from WINS data providers and returns files, containing the data in XBRL format. In a first step, text and tables from the PDF documents were extracted. It was also necessary to apporimatively recontructs the original layout, which is getting lost in the PDF-to-text conversion. Once this has been done, the WINS information extraction module inspects the generated HTML documents, trying to find correspondences in the text of the tables for labels of concepts contained in the overall XBRL taxonomy. But not only the detection of realizations of XBRL concepts in the document is important. The extraction tools must also detect relevant dates in the tables as well as currencies used, so that the figures contained in the tables , e.g., balance and profit & loss (P&L) tables, are getting their correct interpretation. Since the XBRL taxonomy is also considering information about a company as such (name, address, number of employees, etc), the extraction tools need to detect this kind of information. In order to obtain such information, we implemented a simple named entity recognition algorithm for detecting names of companies, locations, and relevant persons. 456 BUSINESS INFORMATION SYSTEMS BIS 2006