A web service for easy knowledge access of IPCC Reports for use in climate plans and their supporting cultural and educational projects

Proof of concept prototype

A project of the open research group semanticClimate — ‘liberating knowledge from climate-related reports’.

July 2023. 

semanticClimate is led by young Indian scientists and supported by The National Institute of Plant Genome Research (NIPGR) – Delhi, and partners: Open Science Lab, TIB.

Project summary

City and regional plans are important tools to help mitigate the impacts of climate change. 

The project is a prototype web service that allows users to search and collate IPCC Report information related to city climate plans. Using the service users can easily compile content from the IPCC reports as referenced readers.

Currently IPCC reports are not supported by search services that allow for granular indexing. The semanticClimate uses Linked Open Data (LOD) and Wikidata / Wikibase technologies to enable better visibility for the IPCC Report contents.

The IPCC Reports remain as the authoritative referenced source content, with the semanticClimate acting as a search and reuse layer.

The goal of the project is to help produce better informed and communicated city climate plans to produce more effective and democratically supported climate outcomes.

Use case: Supporting city climate plan authors

Climate plan authors need to find IPCC Report recommendations, sections, visualisations, and external references and use these in their own city climate plans as well as to distribute the content to their community for the purpose of making them democratically — understandable, accountable, and transparent.

Using the semanticClimate’s research instrument called the Knowledge Explorer an author could create a custom reader to share with stakeholders as an automatically typeset content package — including external citations — with linked references to the original source IPCC Reports.

semanticClimate works by breaking down the IPCC Reports into the smallest parts possible, for example - sentences, acronyms, section headers, images, citations — and semantically tags each part. This tagging then allows machine readability and use of machine learning tooling, which in effect unlocks the content for findability and recombinations as search results, knowledge graphs, and in addition as full-text content which can be reused as collated and automatically typeset readers, or new packages like a glossary list that hyperlinks references all original sections of the Reports where they are used.

City climate plans

Context for city climate plans (videos): 

Example city climate plans: 

Plan support programmes :

Work programme

semanticClimate research and development work creates open source software to enable what we call a ClimateExplorer and on top of this we run hackathons as LearnByExploring events.

semanticClimate code link: https://github.com/petermr/semanticClimate