A Lightweight Approach to Extract Interschema Properties from Structured, Semi-Structured and Unstructured Sources in a Big Data Scenario

Francesco Cauteruccio (), Paolo Lo Giudice (), Lorenzo Musarella (), Giorgio Terracina (), Domenico Ursino and Luca Virgili ()
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Francesco Cauteruccio: Dipartimento di Matematica e Informatica, Università della Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende, CS, Italy
Paolo Lo Giudice: #x2020;Dipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università Mediterranea di Reggio Calabria, Via dell’Università, 25 (già Salita Melissari), 89124 Reggio Calabria CF, Italy
Lorenzo Musarella: #x2020;Dipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università Mediterranea di Reggio Calabria, Via dell’Università, 25 (già Salita Melissari), 89124 Reggio Calabria CF, Italy
Giorgio Terracina: Dipartimento di Matematica e Informatica, Università della Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende, CS, Italy
Domenico Ursino: #x2021;Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Luca Virgili: #x2021;Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 03, 849-889

Abstract: The knowledge of interschema properties (e.g., synonymies, homonymies, hyponymies and subschema similarities) plays a key role for allowing decision-making in sources characterized by disparate formats. In the past, wide amount and variety of approaches to derive interschema properties from structured and semi-structured data have been proposed. However, currently, it is esteemed that more than 80% of data sources are unstructured. Furthermore, the number of sources generally involved in an interaction is much higher than in the past. As a consequence, the necessity arises of new approaches to address the interschema property derivation issue in this new scenario. In this paper, we aim at providing a contribution in this setting by proposing an approach capable of uniformly extracting interschema properties from a huge number of structured, semi-structured and unstructured sources.

Keywords: Unstructured sources; interschema property derivation; structuring unstructured data; big data (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219622020500182

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