An approach to XBRL interoperability based on Ant Colony Optimization algorithm

Extensible Business Reporting Language (XBRL) is an XML-based language developed for enhancing interoperability among the entities involved in process of business reporting. Although this language is adopted by various regulators all around the world and has contributed greatly to semantic inter operability in this field, the variations between taxonomies and also between elements of instance documents, still cause many inconsistencies between elements. Although some existing approaches suppose the conversion of XBRL to ontologies and then resolve the inconsistencies by applying some mapping techniques, it does not seem practical because of low precision and incompleteness of these conversions. In this paper, a novel approach is proposed which utilizes Ant Colony Optimization (ACO) in order to detect best semantic mappings between inconsistent concepts of two XBRL documents. This approach analyzes the possible mappings with respect to various factors like concept names, all label texts, presentation and calculation hierarchies and so on. This makes the approach capable of finding mappings, which were not easily discoverable otherwise. The proposed approach is implemented and applied to actual XBRL reports. The results are measured with aid of well-known criteria (precision, recall and F-measure) and are compared with the well-known Hungarian algorithm and illustrate the better performance in accordance with these three criteria. (C) 2018 Elsevier B.V. All rights reserved.