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 World Scientific Publishing Company.

A lightweight approach to extract interschema properties from structured, semi-structured and unstructured sources in a big data scenario / Cauteruccio, F.; Lo Giudice, P.; Musarella, L.; Terracina, G.; Ursino, D.; Virgili, L.. - 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

D. Ursino
;
L. Virgili
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 World Scientific Publishing Company.
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Descrizione: Electronic version of an article published as A lightweight approach to extract interschema properties from structured, semi-structured and unstructured sources in a big data scenario / Cauteruccio, F.; Lo Giudice, P.; Musarella, L.; Terracina, G.; Ursino, D.; Virgili, L.. - In: INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING. - ISSN 0219-6220. - 19:3(2020), pp. 849-889. 10.1142/S0219622020500182 ©2020 World Scientific Publishing Company, https://www.worldscientific.com/worldscinet/ijitdm. Only personal use of this material is permitted. Permission from publisher must be obtained for all other uses, in any current or future media.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/276193
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