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

被引：5

作者：

Cauteruccio, Francesco ^{[1
]}

Lo Giudice, Paolo ^{[2
]}

Musarella, Lorenzo ^{[2
]}

Terracina, Giorgio ^{[1
]}

Ursino, Domenico ^{[3
]}

Virgili, Luca ^{[3
]}

机构：

[1] Univ Calabria, Dipartimento Matemat & Informat, I-87036 Arcavacata Di Rende, CS, Italy

[2] Univ Mediterranea Reggio Calabria, Dipartimento Ingn Informaz Infrastrutture & Energ, Via Univ,25 Gia Salita Melissari, I-89124 Reggio Di Calabria, CF, Italy

[3] Univ Politecn Marche, Dipartimento Ingn Informaz, Via Brecce Bianche 12, I-60131 Ancona, Italy

来源：

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING | 2020年 / 19卷 / 03期

关键词：

Unstructured sources; interschema property derivation; structuring unstructured data; big data; METADATA QUALITY; DIGITAL REPOSITORIES; SIMILARITY; CLASSIFICATION; CONSTRUCTION; INTEGRATION; SYSTEM; MODEL; DIKE;

D O I：

10.1142/S0219622020500182

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：849 / 889

页数：41

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