The rapid spread of misinformation on and through social media poses a significant challenge to public understanding of climate change and evidence-based policymaking. While natural language processing techniques have been used to analyse online discourse on climate change, no existing resources link social media claims to scientific literature. Thus, we introduce ClimateCheck, a human-annotated dataset that connects 435 unique, climate-related English claims in lay language to scientific abstracts. Each claim is connected to at least one and at most seventeen abstracts, resulting in 3,048 annotated claim-abstract pairs. The dataset aims to facilitate fact-checking and claim verification by leveraging scholarly document processing to improve access to scientific evidence in online discussions about climate change.

Misinformation in public discourse on global and significant issues like climate change is often facilitated through social media. However, current systems do not address fact-checking climate-related claims against trustworthy, evidence-based sources, such as scientific publications. We organised the ClimateCheck shared task at the 5th Scholarly Document Processing (SDP) Workshop, co-located with ACL 2025 in Vienna, Austria. The task featured two subtasks: 1. Abstracts retrieval given a claim, and 2. Claim verification based on the retrieved abstract. ClimateCheck had 27 registered users with active participation from 13 teams, ten of which submitted results for the first subtask and three for the second. The winning team achieved a Recall@10 score of 0.66 and a Binary Preference score of 0.49 for subtask I, and an F1 score of 0.73 for subtask II. Their method combined sparse retrieval using BM25, an ensemble of fine-tuned cross-encoder models using BGE-rerankers, and large language models for classification.

Following the introduction of the European Sustainability Reporting Standard (ESRS), companies will have to adapt to a new policy and provide mandatory sustainability reports. However, implementing such reports entails a challenge, such as the comprehension of a large number of textual information from various sources. This task can be accelerated by employing Large Language Models (LLMs) and ontologies to effectively model the domain knowledge. In this study, we extended an existing ontology to model ESRS Topical Standard for disclosure. The developed ontology would enable automated reasoning over the data and assist in constructing Knowledge Graphs (KGs). Moreover, the proposed ontology extension would also help to identify gaps in companies’ sustainability reports with regard to the ESRS requirements.Additionally, we extracted knowledge from corporate sustainability reports via LLMs guided with a proposed ontology and developed their KG representation.

This paper describes the results of the Knowledge Graph Question Answering (KGQA) shared task that was co-located with the TextGraphs 2024 workshop. In this task, given a textual question and a list of entities with the corresponding KG subgraphs, the participating system should choose the entity that correctly answers the question. Our competition attracted thirty teams, four of which outperformed our strong ChatGPT-based zero-shot baseline. In this paper, we overview the participating systems and analyze their performance according to a large-scale automatic evaluation. To the best of our knowledge, this is the first competition aimed at the KGQA problem using the interaction between large language models (LLMs) and knowledge graphs.