Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2024

Abstract

Efficient news exploration is crucial in real-world applications, particularly within the financial sector, where numerous control and risk assessment tasks rely on the analysis of public news reports. The current processes in this domain predominantly rely on manual efforts, often involving keyword-based searches and the compilation of extensive keyword lists. In this paper, we introduce NCEXPLORER, a framework designed with OLAP-like operations to enhance the news exploration experience. NCEXPLORER empowers users to use roll-up operations for a broader content overview and drill-down operations for detailed insights. These operations are achieved through integration with external knowledge graphs (KGs), encompassing both fact-based and ontology-based structures. This integration significantly augments exploration capabilities, offering a more comprehensive and efficient approach to unveiling the underlying structures and nuances embedded in news content. Extensive empirical studies through master-qualified evaluators on Amazon Mechanical Turk demonstrate NCEXPLORER'S superiority over existing state-of-the-art news search methodologies across an array of topic domains, using real-world news datasets.

Keywords

News exploration, Knowledge graphs, Online analytical processing

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the 40th IEEE International Conference on Data Engineering (ICDE 2024) : Utrecht, Netherlands, May 13-17

First Page

1

Last Page

7

Identifier

10.1109/ICDE60146.2024.00400

Publisher

IEEE

City or Country

Utrecht, Netherlands

Additional URL

https://doi.org/10.1109/ICDE60146.2024.00400

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