A new approach to data search and discovery
While the traditional publication of statistical data relies on the specification of the metadata needed to identify, retrieve and navigate the data only, the approach we propose transforms data search and discovery to be more intuitive and semantically relevant, namely:
- semantic search provides users with flexible and faster access to the data through the ability to use natural language to query “things”, e.g. concepts, data, categories;
- discovery of facts and relationships based on navigation through hidden patterns allows for large scale analysis and identification of related things by users.
The pseudo knowledge graph approach proposed here is the start of a foundation that will empower Eurostat to transform data discovery and access to be more intuitive and semantically relevant than before.
To develop the pseudo knowledge graph, the (structured) information provided by metadata is complemented with the (semi-structured) information available as online resources, from which textual sources are extracted, including the Eurostat website.
Eurostat has conducted a proof of concept by means of a rapidly developed prototype staged in a test environment based on a set of use cases, datasets or and content sources.