Meeting the Sustainable Development Goals (SDGs) established by the United Nations, presents a large-scale challenge for all countries. To monitor progress towards these goals, there is a need to develop key performance indicators using existing data and metadata. The computation of the indicators requires integrating and analyzing heterogeneous datasets, in particular web open data. This approach aims to highlight the positive impact of the web on the society. However, the diversity of web data sources and formats raises major issues in terms of structuring and integration. Despite the abundance of open data and metadata, its exploitation remains limited, leaving untapped potential for guiding urban policies towards sustainability. We have so far introduced a novel approach for SDG indicator computation, leveraging the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs). We have proposed a method that combines rule-based filtering with LLM-powered schema mapping to establish semantic correspondences between diverse data sources and SDG indicators, including disaggregated attributes. Our approach integrated these mappings into a KG, which enables indicator computation by querying graph’s topology. Finally, we have evaluated our method through a case study focusing on the SDG Indicator 11.7.1 about accessibility of public open spaces. Our experimental results are promising showing significant improvements compared to traditional schema matching techniques.