Abstrakt
Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics.
The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient.
However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth. In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery.
The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery. The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.
To the best of authors' knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery.
The prototype implementation of the framework is available on GitHub.