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.

A Lightweight Approach to Extract Interschema Properties from Structured, Semi-Structured and Unstructured Sources in a Big Data Scenario / Cauteruccio, Francesco; LO GIUDICE, Paolo; Musarella, Lorenzo; Terracina, Giorgio; Ursino, Domenico; Virgili, Luca. - In: INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING. - ISSN 0219-6220. - 19:3(2020), pp. 849-889. [10.1142/s0219622020500182]

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

Paolo Lo Giudice;Lorenzo Musarella;Domenico Ursino;
2020-01-01

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.
2020
Unstructured sources
interschema property derivation
structuring unstructured data
big data
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/133754
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