Nancy Pontika

KMi research informs parliamentary debate on AI, labour, and gender inequality

KMi research informs parliamentary debate on AI, labour, and gender inequality

KMi’s Prof Miriam Fernandez was recently invited to contribute to a roundtable discussion at the UK Parliament organised by Fawcett Society in collaboration with the Misogyny & AI Network. The discussion brought together MPs, peers, barristers, trade unions, researchers, civil servants, and women’s sector leaders to discuss artificial intelligence and its impact on women’s lives.

Representing the Centre for Protecting Women Online (CPWO) and KMi, Prof Fernandez presented the results of a Responsible Ai UK funded project in which the team explored how AI is shaping the labour market, with a particular focus on its implications for the gender pay gap and the future of women’s work. Key takeaways, and policy recommendations are outlined in a working paper. In particular, the report highlights the disproportionate risk for women in the most vulnerable positions and recommends targeted upskilling programs for women in occupations with high AI exposure, particularly in healthcare, administrative and professional service roles. 

This engagement builds on KMi’s strengths in Responsible AI, socio‑technical systems, and data‑driven social research, demonstrating how rigorous academic work can inform real‑world policy debates. By connecting research evidence to decision‑makers, the work supports The Open University’s mission to deliver research with clear public value, contributing to fairer labour markets, better AI governance, and a more equitable digital future.

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KMi is addressing technology‑facilitated gender‑based violence

KMi is addressing technology‑facilitated gender‑based violence

Research from KMi is contributing to international debates on technology‑facilitated gender‑based violence (TFGBV), as Prof Miriam Fernandez presents work from The Open University’s Centre for Protecting Women Online (CPWO) across major academic, policy, and public forums.

Earlier this year, Prof Fernandez was an invited speaker at the (Re)Claiming Our Space Conference in Nicosia, organised by the Mediterranean Institute of Gender Studies (MIGS). Her talk examined how digital technologies are increasingly weaponised against women and girls, with a particular focus on gendered disinformation and the role of AI‑driven platforms in amplifying harm.

These themes were also explored in a more technical context at the Women in Forensic Computing Workshop, held alongside DFRWS EU 2026 in Sweden. Bringing together researchers and practitioners in digital forensics, law, and AI, the workshop addressed emerging challenges such as deepfakes, AI‑enabled abuse, and the evidential complexity of documenting online harm.

CPWO’s research has also been featured in O’s “So What?” series, helping translate complex findings for wider audiences and highlighting the need for safety‑by‑design, stronger legal frameworks, and improved public understanding of digital harms.

This body of work reflects KMi’s core strengths in Responsible AI, socio‑technical systems, data‑driven social research, and ethical technology design. Strategically, it supports the University’s mission to deliver research with clear public value, benefiting students, informing policy, and contributing to a safer and more equitable digital world.

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From lab to standard: KMi research behind the new W3C Data Façades community group

From lab to standard: KMi research behind the new W3C Data Façades community group

KMi is proud to share a milestone for one of its research lines in knowledge graphs engineering: the launch of the Data Façades Community Group at the W3C, the international standards body for Web technologies. The group’s formation marks the first step towards standardisation. It is an exciting moment, and one that has been a long time in the making.

The idea

Any practitioner working with real-world data knows the frustration: data lives in CSV files, JSON APIs, XML feeds, spreadsheets, relational databases, and each format demands its own transformation pipeline. For knowledge graph engineers, this has long meant writing complex mappings or ad-hoc code, requiring familiarity with both source formats and dedicated mapping languages.

Façade-X was conceived as a simpler alternative: a minimalist meta-model that treats any data source, regardless of format, as a uniform structure of containers, slots, and values in RDF. Rather than learning a new transformation language, engineers can query any data format directly using the SPARQL they already know. The idea was first published in Facade-X: an opinionated approach to SPARQL anything by Enrico Daga (KMi) and Luigi Asprino (ISTC-CNR), together with Paul Mulholland (KMi) and Aldo Gangemi (ISTC-CNR). A follow-up article, Knowledge Graph Construction with a Façade, extended the approach to the full knowledge graph construction workflow.

The reference implementation, SPARQL Anything, demonstrated practical feasibility across JSON, CSV, XML, HTML, spreadsheets, and more. At KMi, the work has also involved Jason Carvalho, Marco Ratta, and Paul Warren, who have contributed to research, development, and evaluation.

The Community Group

The Data Façades Community Group aims to make Façade-X a vendor-neutral, interoperable technology that any developer or organisation can implement, decoupling it from the original SPARQL Anything codebase. Work happens openly on GitHub, with meeting minutes publicly archived. Current specification drafts, covering the formal metamodel and RDF vocabulary, are available at w3c-facade-x.github.io/facade-x-specs.

Equally important has been the early engagement of industry experts who helped shape the approach from outside academia: Justin Dowdy, Ivo Velitchkov, and Mathias Vanden Auweele have all brought practitioner perspectives that have been invaluable in grounding the work in real use cases.

Industry adoption

Encouragingly, companies are already implementing Façade-X in their products. Maplib (Data Treehouse) is a high-performance Rust-based knowledge graph library for Python that integrates the Façade-X approach for heterogeneous data access.Triply, a leading knowledge graph infrastructure company, has also begun engaging with the approach for their TriplyDB platform. Their participation in the Community Group brings production-scale implementation experience that will be essential for developing specifications that work in the real world.

New theoretical work

Alongside the standardisation effort, Luigi Asprino and Enrico Daga have published “Towards a theory of Façade-X data access: satisfiability of SPARQL basic graph patterns”, forthcoming in the Special Issue: Data Management for (Knowledge) Graphs of Transactions on Graph Data and Knowledge. The paper studies which SPARQL queries are satisfiable when evaluated over Façade-X data sources, a question with direct implications for query optimisation and the development of more efficient data integration systems.

Looking ahead

The Data Façades Community Group is a beginning, not an endpoint, the plan is to develop mature specifications and progress to a full W3C Working Group. With solid research foundations, growing industry adoption, and an active open community, the pieces are in place.

If you work with heterogeneous data and knowledge graphs, we invite you to follow the group’s progress, or join the conversation.

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Open University Researchers awarded £450,000 for “Unlearning AI” 

Open University Researchers awarded £450,000 for “Unlearning AI”