Graph integration of structured, semistructured and unstructured data for data journalism

被引：12

作者：

Anadiotis, Angelos Christos ^{[1
,2
]}

Balalau, Oana ^{[3
]}

Conceicao, Catarina ^{[4
,5
]}

Galhardas, Helena ^{[4
,5
]}

Haddad, Mhd Yamen ^{[3
]}

Manolescu, Ioana ^{[3
]}

Merabti, Tayeb ^{[3
]}

You, Jingmao ^{[3
]}

机构：

[1] Inst Polytech Paris, Ecole Polytech, Paris, France

[2] Ecole Polytech Fed Lausanne, Lausanne, Switzerland

[3] Inst Polytech Paris, INRIA, Paris, France

[4] Univ Lisbon, INESC ID, Lisbon, Portugal

[5] Univ Lisbon, IST, Lisbon, Portugal

来源：

INFORMATION SYSTEMS | 2022年 / 104卷

关键词：

Data journalism; Heterogeneous data integration; Information extraction; NAMED ENTITY RECOGNITION;

D O I：

10.1016/j.is.2021.101846

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments. (C) 2021 Elsevier Ltd. All rights reserved.

引用

页数：16

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