Knowledge Graph


To make knowledge graph technologies more accessible to climate and energy researchers.


This is a collaborative work between:


A large number of today’s climate data centers present their collected data in the form of raw tables (e.g. RDB, CSV, JSON): KNMI Climate Explorer, NOAA datasets. These datasets are helpful when people are researching a limited number of climatic factors in a single data source, e.g. analyzing how temperature changes over time in a certain city. As this might only need to interrogate a few number of columns within a data table.

Including data of different domains can be a very time-consuming job for climatic researchers. Recently, one of the popular solutions that is greatly explored is employing an ontology or a knowledge graph, that offers the expressivity and flexibility to easily extend to various interoperable domains. Knowledge graph technologies have been exploited in a wide range of research areas for data modeling and data management. They greatly assists in defining the semantic model of the underlying data combined with domain knowledge. In this research theme, we aim to make knowledge graph technologies more accessible to climate and energy researchers.

Our project, Link Climate, is created to provide Ireland’s and England’s NOAA climate daily summaries datasets with a complemented data navigation capability in the form of knowledge graph. It is composed of a sparql server with data periodically updated from NOAA Climate Data Online and a dereferencing engine powered by LodView. To explore the climate KG, step to entrance to SPARQL endpoint.

Our proposed knowledge graph model modelled on NOAA climate data.

We are currently inviting researchers to complete a usability test for our online portal to further improve the non-expert experience in manipulating climate knowledge graph data. We’d appreciate if you can complete this 10 minutes survey.


Please refer to the publications.