  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
keywordbased 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.
