International audience

International audience

Monitoring Sustainable Development Goal (SDG) indicators requires integrating heterogeneous open datasets from sources such as relational databases, NoSQL stores, and APIs. While SDG indicators follow standardized definitions, open data sources are often fragmented, schema-less, and inconsistent, making both integration and computation challenging. In this demonstration, we present SDG-KG , a spatio-temporal Knowledge Graph (KG) framework designed to structure metadata, guide data retrieval, and formalize indicator computation workflows. Our approach leverages graph-based modeling to construct a Metadata Graph, apply conflict resolution techniques when multiple sources provide overlapping data, and dynamically generate query-driven execution plans. Through an interactive interface, users can explore United Nations specifications, inspect data provenance and the generated KG, and visualize the computed indicators.

The number of spatial datasets available online has increased exponentially in recent years. Therefore, the search for spatial datasets is becoming a fourishing research field. The use of knowledge graphs has become rampant in search engines and in information retrieval. In this article, we identify the main resources needed and those missing to allow a knowledge graph to support spatial dataset search. We then apply our approach to the water domain in France by building a dedicated knowledge graph and describe an evaluation method to measure its effectiveness.

Much of the information stored on the web contains geographical context, but current search engines treat such context in the same way as all other content. In this paper we describe the design, implementation and evaluation of a spatially aware search engine which is capable of handling queries in the form of the triplet of ⟨theme⟩⟨spatial relationship⟩⟨location⟩. The process of identifying geographic references in documents and assigning appropriate footprints to documents, to be stored together with document terms in an appropriate indexing structure allowing real‐time search, is described. Methods allowing users to query and explore results which have been relevance‐ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine. Normalised precision for 38 queries, containing four types of spatial relationships, is significantly higher (p<0.001) for searches exploiting spatial information than pure text search.