Modeling Fiscal Data with the Data Cube Vocabulary Jindˇrich Mynarz Department of Information and Knowledge Engineering, University of Economics W. Churchill Sq. 4 130 67 Prague, Czech Republic jindrich.mynarz@vse.cz Vojtˇech Svátek Department of Information and Knowledge Engineering, University of Economics W. Churchill Sq. 4 130 67 Prague, Czech Republic svatek@vse.cz Sotirios Karampatakis School of Mathematics Aristotle University of Thessaloniki Thessaloniki, Greece and Open Knowledge Foundation Greece Thessaloniki, Greece sokaramp@auth.gr Jakub Klímek Department of Information and Knowledge Engineering, University of Economics W. Churchill Sq. 4 130 67 Prague, Czech Republic and CTU, Prague Thákurova 9, Praha 6 Czech Republic jakub.klimek@ﬁt.cvut.cz Charalampos Bratsas School of Mathematics Aristotle University of Thessaloniki Thessaloniki, Greece and Open Knowledge Foundation Greece Thessaloniki, Greece cbratsas@math.auth.gr ABSTRACT We present a ﬁscal data model based on the Data Cube Vocabulary, which we developed for the OpenBudgets.eu project. The model deﬁnes component properties out of which data structure deﬁnitions for concrete datasets can be composed. Based on initial usage experiments, simple validation constraints have been formulated. CCS Concepts •Information systems →Resource Description Frame- work (RDF); •Theory of computation →Data model- ing; Keywords Data Cube Vocabulary, SKOS, ﬁscal data, RDF 1. INTRODUCTION Fiscal data is increasingly published as open data by var- ious government institutions, from the EU to the munic- ipality level. From the structural point of view they can be characterized as statistical data, consisting of numerical values (observations) indexed by discrete values of various dimensions. The dimensions typically refer to time (such as Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proﬁt or commercial advantage and that copies bear this notice and the full cita- tion on the ﬁrst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior speciﬁc permission and/or a fee. Request permissions from permissions@acm.org. c⃝2016 ACM. ISBN 978-1-4503-2138-9. DOI: 10.1145/1235 ﬁscal period), budget authority (municipality, regional gov- ernment, ministry, etc.), thematic category (such as build- ing or social service), spending type (e.g., salaries, subsidies, etc.), and a few others. The data can be modeled as mul- tidimensional cubes and subsequently analyzed using data analytics tools: interactive visualization, data mining, etc. The Horizon 2020 project OpenBudgets.eu (2015-2017) aims to support diﬀerent scenarios of exploiting ﬁscal data by journalists, corruption-ﬁghting NGOs or local civic ac- tivists. While ﬁscal data alone only supports a limited scope of analytical tasks, they could beneﬁt from being augmented with further data describing demographic, economic (e.g., GDP) or political (e.g., the ruling political party) features related to the values of dimensions in the data. This opens a way to eﬃcient use of RDF technology, with the Data Cube Vocabulary (DCV) and Simple Knowledge Organi- zation System (SKOS) RDF vocabularies playing a central role. In the paper we share our experience from a practical use case in applying DCV to ﬁscal data. We ﬁrst brieﬂy describe both the underlying vocabularies and the newly developed OpenBudgets.org ﬁscal data model, conceived as a set of reusable DCV component properties that can be specialized for concrete datasets in the form of data structure deﬁni- tions (DSDs). Compared to the existing ﬁscal data models, as reviewed in Kl´ımek et al. [3], the data model is more generic and covers both spending and budget data. Next we report on initial usage experi