🚀 Exciting news! Our new survey paper about #KnowledgeGraphs - the future of large-scale information encoding for #AI services- is out. We reviewed the literature and tackled the next challenges. #DataScience #MachineLearning #AIprogress In #OpenAccess : link.springer.com/article/10…

Great news! Our team has secured three new UKRI metascience projects worth approximately £500,000 in total! We’ll help shape how science is evaluated and funded, through trustworthy AI and better ways to recognise promising ideas. blog.stem.open.ac.uk/three-n… #Metascience #AI

First results from our UKRI metascience project: one of the first studies of LLM grant scoring, using anonymised data within UKRI's secure environment. Insights for AI-assisted grant review. More to follow! arxiv.org/abs/2609.25327 #Metascience #PeerReview

🎉 Our SEER paper is now published in Knowledge-Based Systems! SEER combines AI agents to extract who said what in the news and build knowledge graphs, supporting analysis of voices and viewpoints at scale. doi.org/10.1016/j.knosys.202… #AI #KnowledgeGraphs

🎉 Our CSO Classifier 4.0 paper is now published in SoftwareX! The open-source tool maps research papers to Computer Science Ontology topics, with an updated semantic model, topic weights and faster processing. doi.org/10.1016/j.softx.2026… #OpenScience #CSOClassifier

AI can help researchers identify plausible directions for what to study next. Our paper presents ResearchLink, which generates and ranks hypotheses from scientific knowledge graphs for human investigation. doi.org/10.1016/j.knosys.202… #AIForScience #KnowledgeGraphs

AI systems need data platforms that can evolve with them. Our Journal of Big Data paper presents an open, knowledge-graph-based framework for modelling adaptable platforms. Paper: doi.org/10.1186/s40537-025-0… #KnowledgeGraphs #BigData

Knowledge graphs need scalable validation as automated generation improves. Our paper shows how LLMs, automated checks and targeted expert review can share the work. Paper: doi.org/10.1016/j.ipm.2025.1… #KnowledgeGraphs #HumanInTheLoop

Science needs maps that evolve as quickly as research itself. In our paper, we show that LLMs, including compact open models, can identify the topic relationships needed to build scholarly ontologies. Read the paper: doi.org/10.1016/j.ipm.2025.1… #AIinScience #Ontologies

Could AI assess research without becoming another black box? Our preprint identifies interpretable signals that LLMs associate with scientific quality, from methods to specialised language. Read the paper: arxiv.org/abs/2602.19115 #AIinScience #XAI

Research teams benefit from combining different expertise. Our Scientometrics paper links expertise diversity to five-year citation impact across 944,508 Computer Science papers. Read the paper: doi.org/10.1007/s11192-026-0… #ScienceOfScience #TeamScience

A useful literature review shows what is known, unresolved and how studies change the picture. Our new PeerJ Computer Science paper is out: Sci-Sentence helps AI recognise these roles at scale. Read the paper: doi.org/10.7717/peerj-cs.392… #AIinScience #NLP

AI could help build connected maps of science across disciplines. Our new ACM TIST paper is out: multidisciplinary fine-tuning transfers strongly across fields, supporting better research search and discovery. Read the paper: doi.org/10.1145/3828662 #AIinScience #Ontologies

News analysis should capture viewpoints and voices, not only topics. Our new paper is out: we combine fine-tuned LLMs with Wikidata context for interpretable analysis of political debate. Read the paper: doi.org/10.1140/epjds/s13688… #NewsAnalytics #KnowledgeGraphs

Online discussions can influence markets, but sentiment alone misses what people are actually recommending. We introduce Social Trading Action Detection to identify buy, sell or no-action signals. doi.org/10.1016/j.ipm.2026.1… #FinancialNLP #LLMs

Can small open LLMs help build biomedical ontologies? We tested five models on MeSH relations. Targeted fine-tuning produced strong results, even for a 3B-parameter model. Paper, benchmark and code: arxiv.org/abs/2607.17902 #BiomedicalAI #Ontologies

🔬 AI can analyse news at scale, but what can a story actually be about? Our new Semantic Web Journal paper provides the epistemological foundation: a typology realised as reusable ontologies (50 classes, 1,622 axioms). doi.org/10.1177/221049682513… #SemanticWeb #AI

🔥 AI is transforming scientific discovery, but we need structured knowledge to reason over it. CS-KG 2.0: 25M entities, 67M relationships from 15M CS papers. Open via SPARQL. Published in Scientific Data (Nature). doi.org/10.1038/s41597-025-0… #KnowledgeGraphs #OpenScience

🔬 Stronger science needs teams that combine complementary expertise. Our Scientometrics study of 944K CS papers shows diversity of expertise is a major driver of citation impact (r=0.955). doi.org/10.1007/s11192-026-0… #ScienceOfScience