Technology Category
Data Mining & Knowledge Management: Semantic WebNatural Language Processing: Information ExtractionSearch and Optimization: Distributed Search
DataLens: Enhancing Dataset Discovery via Network Topologies
🤖 AI Summary
Traditional text retrieval methods—relying on keywords and metadata—are increasingly inadequate in the context of rapidly expanding textual resources, as they require prior domain knowledge and fail to uncover latent semantic relationships among documents.
Method: This paper proposes a text resource exploration framework that integrates faceted search with network topology visualization. It introduces a customizable network graph visualization and a chained联动 interactive view mechanism, enabling multi-dimensional, multi-perspective discovery and collaborative exploration of resource associations. A web-based prototype system is implemented to validate the approach.
Contribution/Results: Empirical evaluation demonstrates that the framework significantly improves data practitioners’ efficiency in comprehending cross-resource semantic relationships and deepens their exploratory insights. The results substantiate the effectiveness and practicality of a network-centric exploration paradigm for enhancing text resource discovery performance.
Technology Category
Application Category
📝 Abstract
The rapid growth of publicly available textual resources, such as lexicons and domain-specific corpora, presents challenges in efficiently identifying relevant resources. While repositories are emerging, they often lack advanced search and exploration features. Most search methods rely on keyword queries and metadata filtering, which require prior knowledge and fail to reveal connections between resources. To address this, we present DataLens, a web-based platform that combines faceted search with advanced visualization techniques to enhance resource discovery. DataLens offers network-based visualizations, where the network structure can be adapted to suit the specific analysis task. It also supports a chained views approach, enabling users to explore data from multiple perspectives. A formative user study involving six data practitioners revealed that users highly value visualization tools-especially network-based exploration-and offered insights to help refine our approach to better support dataset search.
Problem
Research questions and friction points this paper is trying to address.
Challenges in efficiently identifying relevant textual resources
Lack of advanced search and exploration features in repositories
Keyword queries fail to reveal connections between resources
Innovation
Methods, ideas, or system contributions that make the work stand out.
Faceted search with advanced visualization techniques
Network-based visualizations adaptable to analysis tasks
Chained views for multi-perspective data exploration
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