Abstract
Open Data portals have become a common service provided by many Public Administrations around the world, where they openly publish many data sets concerning citizens and territories, in order to increase the amount of information made available for people, firms and public administrators. As an effect, Open Data corpora has become so huge that it is impossible to deal with them by hand; as a consequence, it is necessary to develop tools for effectively querying corpora of open data, in order to find the desired data sets.
In our previous work [1], we presented a novel technique to query open data corpora. In this paper, we present an evolution of that technique, obtained by refining some steps and by introducing some novelties. We still rely on the blindly querying approach: the user does not have to know in advance the actual structure of possibly thousands of data sets, but formulates the query trying to characterize the items of interests; in fact, a novelty of our approach is that our technique looks for single items within data sets, not for data sets. Then, the technique tries to rewrite the query by exploiting the catalog of the corpus in order to find the most similar and relevant terms.
The main enhancement introduced in the technique and presented in this paper is the way the technique looks for similar terms in the catalog, that now is based on a semantic approach: the WordNet dictionary is exploited to get synonyms of terms in the query. Furthermore, a new set of experiments has been performed, in order to prove the effectiveness of the enhanced technique.
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Notes
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We used Open Refine (http://openrefine.org) a powerful tool to work with messy data.
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Pelucchi, M., Psaila, G., Toccu, M. (2018). Enhanced Querying of Open Data Portals. In: Majchrzak, T., Traverso, P., Krempels, KH., Monfort, V. (eds) Web Information Systems and Technologies. WEBIST 2017. Lecture Notes in Business Information Processing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-93527-0_9
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