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Data, Knowledge and Action

The Data, Knowledge, and Action group carries out world-leading research in Databases, Semantic Technologies, and Knowledge Representation and Reasoning.

Topics of interest include query languages, optimization techniques, factorized and probabilistic databases, ontology languages, Datalog, knowledge representation formalisms, and computational reasoning techniques.

One focal point is the area of Big Data, and in particular “data wrangling” — the process of organising, preparing and accessing complex and heterogeneous data.

Also of interest are reasoning about action, planning, strategic reasoning, and sequential decision-making, which analyze cause-and-effect relationships to understand the consequences of complex actions, and empower AI agents with self-programming and autonomous deliberation capabilities.

As well as foundational research, the group also maintains strong links with industry, and has applied its research results in areas as diverse as oil and gas, engineering, business analytics, real estate, medicine and life sciences.

Links

Information Systems Group website

Related seminar series

All Activities

Activities

Information Modelling CellML, ISO 11179, metadata registries, semantic frameworks.

Read more about Information Modelling

Research Informatics This includes several examples of the application of computer science…

Read more about Research Informatics

All Projects

Projects

Bayesian Optimization Organizations working with big data often have to deal with big numbers of users, big sof…

Read more about Bayesian Optimization

Gauge Gauge is a tool for the evaluation of predicates and expressions written in the Alloy mod…

Read more about Gauge

All Publications

Publications

Faithful Rule Extraction for Differentiable Rule Learning Models

Read more about Faithful Rule Extraction for Differentiable Rule Learning Models

Contextual Semantic Embeddings for Ontology Subsumption Prediction

Read more about Contextual Semantic Embeddings for Ontology Subsumption Prediction

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Research