In these years, Italy is dealing with serious economic and social issues, which are aggravated by the recent global economic crisis. These issues include, among others: high fiscal burden, widespread tax evasion, increasing unemployment rate and the progressively aging of population. In this context there are two major needs that public administrations have to meet. On one hand, it is mandatory to fight tax evasion, on the other hand, there is the need to ensure social services in a more efficient, fair and effective way. Our industrial need is to support governance and policy making in achieving these goals through the integrated analysis of a large amount of information collected by both public administrations and other public officers and organizations (e.g. notaries, public utilities and so on). This information is typically scattered over several heterogeneous and decoupled data sources. Moreover, it might also be partially outdated, unreliable and redundant. The adoption of semantic technologies enables the construction of an integrated, trustworthy and accurate knowledge base, providing a picture of the fiscal and social situation of each single citizen and of the community of a local municipality, overcoming the limitations of legacy systems. Cornerstones of the solution are: 1) a set of domain ontologies aimed at improving data integration, and at producing useful inference from explicit information; 2) a scalable system to reconcile identities to the same real world entity across datasets, associating a unique and persistent name to each single entity; and 3) leveraging geo-spatial technologies to achieve a deeper understanding of the observed districts by means of spatial analysis and reasoning.
overcome these limits, we organized a pipeline relying on an ensemble of scalable and state of the art technologies to define a Semantic ETL suitable to create a Semantic Big Data Pool.
We rely on a customized and optimized version of Open Refine tool to perform data cleaning operation, including
syntactical validations and transformation of the original data coming from the public institutions. The formal and
syntactical validations, expressed according to a specific rule language, are the results of many years of experience on
the field. At this stage, issues related to semantic and structural heterogeneity affecting the original data are
normalized relying on a set of maintainable contextual ontology mappings towards the defined domain ontology. Each
record is analysed to extract information about the involved entities to reconcile their identities relying on the Okkam
Entity Name System [
The result of this first part of the process is a physically distributed and logically integrated large RDF graph that can be
manipulated and processed relying on emerging big data technology. Therefore, we rely on tools like Apache HBase,
Apache Incubated Spark, Apache Incubated Flink, Apache Hive, and Apache Pig to define (complex) big data shuffling
processes producing any view, analysis and mesh-up necessary to support tax assessment domain applications. In fact,
it is possible to select subsets of the giant RDF graph to store it in application specific data management systems. For
example, we sink data into a triple store such as OpenRDF Sesame and enable scalable rule-based reasoning tasks
using SPRINGLES. Another example is to build sub-graphs to support seamless real time navigation of the knowledge
relying on effective indexing tools such as as Apache SIREn, or to perform graph-based analysis sinking data in a graph
database (e.g. Neo Technology Neo4J). Finally, it is possible to integrate semantic technologies in the core of the
SpagoBI suite in order to enable novel Business Intelligence tools and techniques [
The integration of geo-spatial technologies adds an important analytic dimension to SICRaS. Taking advantage of this kind of information, we firstly intend to exploit the notion of territory, seen as a spatial region in our ontological model. This enables new ways of extracting, observing and analysing data about real world entities and the spatial relations among them. Secondly, we develop techniques to match entities relying on geo-spatial features to link our knowledge base to external sources (e.g. urban development plans) to find out new information valuable for tax assessment and for other fiscal and social purposes.
In SICRaS we define a scalable and efficient data processing pipeline, capable of overcoming the limits of current semantic technologies riding the wave of emerging big data processing tools. A wise union of semantic and big data technologies, tempered with deep domain knowledge and sophisticated geospatial tools, creates seamless opportunities to define tax assessment applications. Exploiting the wealth of data in an efficient and effective way, we aim to define the next generation of tools for policy makers and help the Italian institutions in overcoming the challenges of the 21st century.
[1] Isabella Distinto, Nicola Guarino, and Claudio Masolo. 2013. A well-founded ontological framework for modeling personal income tax. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (ICAIL '13). ACM, New York, NY, USA, 33-42.