Open budget data are among the most frequently published datasets of the open data ecosystem, intended to improve public administrations and government transparency. Unfortunately, the prospects of analysis across different open budget data remain limited due to schematic and linguistic differences. Budget and spending datasets are published together with descriptive classifications. Various public administrations typically publish the classifications and concepts in their regional languages. These classifications can be exploited to perform a more in-depth analysis, such as comparing similar items across different, cross-lingual datasets. However, in order to enable such analysis, a mapping across the multilingual classifications of datasets is required. In this paper, we present the framework for Interlinking of Heterogeneous Multilingual Open Fiscal DaTA (IOTA). IOTA makes use of machine translation followed by string similarities to map concepts across different datasets. To the best of our knowledge, IOTA is the first framework to offer scalable implementation of string similarity using distributed computing. The results demonstrate the applicability of the proposed multilingual matching, the scalability of the proposed framework, and an in-depth comparison of string similarity measures.
IOTA: Interlinking of Heterogeneous Multilingual Open Fiscal DaTA
IOTA: Interlinking of Heterogeneous Multilingual Open Fiscal DaTA.