2026 Call for Submissions
We are calling leaders and innovators to share use cases and breakthroughs made possible using Knowledge Graphs, Graph Data Science and AI, Graph Databases, Semantic Technology and Ontologies.
The Connected Data platform provides a Community, Events, and Thought Leadership for those who use the Relationships, Meaning and Context in Data to achieve great things.
We've been Connecting Data, People & Ideas since 2016.
We focus on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology.
Over the years, we have organized numerous events big and small, with our flagship London conference attracting thousands of attendees.
We have had the honor of hosting luminaries such as Gary Marcus, Sir Nigel Shadbolt, and Gadi Singer, as well as emerging speakers who went on to achieve great things.
We are happy to have helped pave the way for the adoption and recognition these technologies are receiving today, innovating alongside our community. We are here to shape the next chapter.
The Call for Submissions for Connected Data London 2026, taking place at the Leonardo Royal Hotel Tower Bridge in the heart of London on November 11-12 is now open!
We are calling leaders and innovators to share use cases and breakthroughs made possible using Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology.
Submissions are across 2 areas:
Presentations.
Real world use cases and innovative approaches. Sessions presented live, with live streaming and post-event replays available. 25-minute presentations plus 10 minutes moderated Q&A. Abstract submission required. 3 presentation tracks:
- Nodes track. Focus on use cases, presentations with little technical details aimed primarily at a business audience.
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Edges track. Focus on innovation, presentations with some technical details aimed at both business and technical audiences.
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Educational track. Focus on applications, presentations with more technical details aimed primarily at a technical audience.
Masterclasses: Hands-on tutorials in which instructors teach attendees skills they can use in their daily work. 2-hour long sessions. Material can be split over more than one 2-hour long session. Detailed description submission required; template here.
Important Dates
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August 31, 2026 — Submission Deadline
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September 14, 2026 — Notification of Acceptance
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November 11, 2026 — Masterclass Day
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November 12, 2026 — Conference Day; Presentations & Keynotes
Topics of interest include, but are not limited to
AI, GenAI, Agentic AI and Knowledge Graphs
KGs and LLMs can form a mutually beneficial relationship. KGs can act as the truth layer for LLMs, while LLMs can assist in the creation of KGs. Sub-topics of special interest:
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Graph RAG. Lessons learned in practice, Graph RAG variants. Lessons learned in practice, Graph RAG variants. Emphasis on Knowledge Augmented Generation (KAG) leveraging ontology for RAG
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Graphs and Agents. Graphs powering agent workflows, agentic memory
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Reasoning and Neuro-symbolic AI. Combining rule-based reasoning with machine learning is a promising direction for the future of AI.
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Graph Learning and Graph Neural Networks. Use cases and innovation in GNNs, Graph Learning, and Graph Foundation models.
Knowledge Graph Development Lifecycle
Managing the full lifecycle of building and maintaining graphs in product, including updates, data integration, provenance, versioning, and related practices borrowed from software engineering. Sub-topics of special interest:
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Knowledge Graph skills. As the demand for Knowledge Graphs is growing, how can we ensure it is met? What are some good ways to train/upskill/certify people? Courses, resources, syllabi.
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Choosing persistence strategies for Knowledge Graphs. Triple stores, Labeled Property Graphs, Virtualisation, or in-memory.
Knowledge Management:
As we shift from managing information for people to structuring it for machines, these frameworks provide the necessary grounding for reliable intelligence. Sub-topics of special interest:
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Knowledge Organisation Schemes, Ontologies, Controlled Vocabularies, Thesauri, Taxonomies - many approaches exist which are being accelerated using AI
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Knowledge Discovery & Curation. Techniques for automated insight extraction and the governance of information lifecycles.
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Tagging & Search. Semantic indexing and metadata enrichment strategies that leverage natural language processing to connect disparate data silos with intent-driven retrieval.
Data Products
A data product is a reusable data asset designed for a specific purpose. It's a self-contained package that includes data, metadata, semantics, and templates. Data products can be internal to an organization or sold externally. Based on open semantic specifications, this approach treats data as something with value, cost, stewardship, and clear ownership.
Architecture for serving and accessing knowledge
APIs, GraphQL, MCP, conversational interfaces, query languages and more.
Graph Visualisation
Strategies for deciding what graph data to present/abstract for different audiences and visualization. 3D & VR graph visualisation.
Using graphs in-stream
Using graphs as a filter for streaming; streaming algorithms.
Causal Graphs
Causality enables the interpretation of relationships between variables. These relationships can be represented by directed acyclic graphs.
From Semantic SEO to GEO (Generative EO) and Agentic AI
SEO is evolving. What is the role of Knowledge Graphs going forward?
Retail
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Finance, Banking & Insurance
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Pharma, Healthcare and Science
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Retail
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Government
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Manufacturing and Logistics
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Media and Publishing
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Security/Cybersecurity; Intelligence and law enforcement
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Other industries & domains; e.g., Culture/Art, Non Profit etc.
Industry applications of Knowledge Graphs, Graph Analytics, Data Science and AI, Graph Databases and Semantic Technology in:
Innovations in Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology combined with:
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Analytics
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Natural Language Processing
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Data Governance, Data Quality, Data Observability, Data Mesh, Data Fabric, Metadata
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Data Engineering, DataOps, MLOps
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Data visualization, Human-computer interaction (HCI), User interfaces and user experience (UI/UX)
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Other topics
Foundations and Applications of Ontologies
The “O” word used to be reserved for an inner circle of experts. Today, organizations are increasingly making ontologies their AI guardrails and backbone for data, process and application harmonization. Sub-topics of special interest:
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Theory and practice of ontology and schema design. Choosing between / combining OWL, SHACL, upper ontologies, other data modeling approaches
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From data to knowledge engineering. How ontologies are defined, approached, understood, developed and used by non-experts in settings such as software development and analytics
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Ontology Tooling and Evaluation. Exploring the surge in visual editors, LLM-assisted ontology construction, quality benchmarks, and the debate around "vibe ontologies".
Context Graphs
Structures that capture not just entities and relationships, but decision traces, temporal lineage, and the precedents that govern autonomous behaviour. Whether framed as an evolution of knowledge graphs, a new primitive for agentic systems, or a rebranding of existing concepts, context graphs are reshaping how organizations think about AI memory, governance, and trust. Sub-topics of special interest:
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Defining and building context graphs. Architecture, standards, and the role of ontology.
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Context graphs and agentic AI. Decision lineage, provenance, and autonomous systems.
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Context graphs in practice. Real deployments, lessons learned, and the gap between theory and production.
Knowledge Graphs for Software Engineering
Software engineering is among the domains seeing the most immediate impact from AI, and knowledge graphs are a critical enabling layer. Sub-topics of special interest:
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Codebase knowledge graphs. AST-driven, RDF-based, and property graph approaches.
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Knowledge graphs for AI-assisted coding. Context, retrieval, and agent grounding.
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Knowledge graphs and Enterprise Architecture. Connecting software engineering to broader EA frameworks via ontology.
Data Catalogs and Semantic Layers
A well-governed inventory of data resources, along with a semantic layer that establishes shared relationships and context can accelerate data discovery, enable more effective use of data products, enhance analytics, and drive more informed decision-making.
Graph Database Evolution
Graph databases have been around for more than 15 years, and are on a growth trajectory. Their main challenges - fragmented landscape, performance limitations, and onboarding - are being dealt with with evolving standardisation, and new engines, tooling and educational approaches. Sub-topics of special interest:
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Onboarding and Tooling. Effective approaches and tools to get started and be productive with graph databases.
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Optimization vs standardization. New approaches to transactional and analytical workload management at scale, and support of evolving standards.
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Graph engines vs databases. When are graph persistence layers needed? How can graph analytics engines help, and where are they applicable?
Open source tooling
Ontology and taxonomy editors, processing libraries, query browsers, graph databases and more.
Temporal and Geospatial graphs
Strategies, libraries, and platforms for managing temporal and geospatial aspects of graphs.
Graph algorithms, optimization and distributed systems:
Techniques for fast execution of mission-critical algorithms. Algorithm parameters and tuning.
Personal Knowledge Management and Personal Knowledge Graphs:
The use of knowledge graph technology and practices in a personal context and capacity.
Our Commitment
We are committed to fostering a respectful environment, and we encourage learning and sharing in the broad community of people who attend and speak at our events.
You can check our pledge, our standards and our responsibilities in our Code of Conduct.
Join the over 350+ speakers who have shared their work with ~6000 attendees in 350+ sessions and help shape the future of connected data.
We meaningfully engage with and learn from our speakers. If your submission is accepted, you will get:
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Guidance to shape your talk
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A free pass for the event
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Discount codes for your network
Top minds participate in Connected Data London from the likes of Adobe, AstraZeneca, AWS, Bayer, BBC, Bloomberg, Citi, Microsoft, Netflix, Nvidia, Google, GSK, IBM, IKEA, NASA, Oracle, SAP, S&P, Thomson Reuters, Wolfram and Uber, as well as new entrants who go on to rock the world.
More information:
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Key audience and event metrics and demographics.
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History and previous events.
Evaluation Process
Proposals are evaluated by the program committee, which consists of leaders and innovators with strong industry and academic backgrounds.
All submissions are required to provide the following:
Concise description explaining:
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The problem you are addressing, or the opportunity you are creating
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Target audience, what will the people who attend your talk get from it and what type of background and experience they are expected to have
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Why this is important to our audience and the world at large
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How you have addressed the problem, or created the opportunity
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The impact of your work
Speaker profile providing:
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Background and experience
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Motivation for speaking at Connected Data London
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References to previous talks, if available
A short video of up to 3 minutes in which the submitter elaborates on their proposal
Submissions will be evaluated based on the following criteria:
Originality. Does the submission present a new approach, idea, platform, or finding? Incremental improvements on well-trodden ground score lower than novel thinking, whether in research, engineering, or practice.
Impact. Does the work demonstrate real-world adoption, measurable outcomes, or clear potential to move the needle? Commercial and societal impact and production deployments at-scale are weighted higher than proofs of concept.
Clarity. Is the submission well written, logically structured, and easy to follow? A strong submission makes the problem, approach, and outcome immediately apparent to a reviewer unfamiliar with the work. The description must also serve to introduce and promote your work to our wide and diverse audience.
Presentation. The short video is an opportunity to convey enthusiasm, context, and fit for the CDL audience and showcase presentation skills, and will be taken into account in the evaluation.
Submit Your Proposal
We manage our submissions process via the following form. We kindly ask you to read the instructions and fill it in properly. If you need help, you can reach out at info@connected-data.london