Dr Clemens Kupke


Computer and Information Sciences


Learning weighted automata over principal ideal domains
van Heerdt Gerco, Kupke Clemens, Rot Jurriaan, Silva Alexandra
Foundations of Software Science and Computation Structures 23rd International Conference, Foundations of Software Science and Computation Structures 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. Lecture Notes in Computer Science Vol 12077, pp. 602-621 (2020)
Expressive logics for coinductive predicates
Kupke Clemens, Rot Jurriaan
Computer Science Logic (2020)
Compositional game theory with mixed strategies : probabilistic open games using a distributive law
Ghani Neil, Kupke Clemens, Lambert Alasdair, Nordvall Forsberg Fredrik
Applied category theory conference 2019, pp. 1-12 (2019)
Coalgebra learning via duality
Barlocco Simone, Kupke Clemens, Rot Jurriaan
Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol 11425 LNCS, pp. 62-79 (2019)
Completeness for game logic
Enqvist Sebastian, Hansen Helle Hvid, Kupke Clemens, Venema Yde, Marti Johannes
Proceedings of the 34th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2019 2019 34th Annual ACM/IEEE Symposium on Logic in computer Science (LICS) (2019)
A compositional treatment of iterated open games
Ghani Neil, Kupke Clemens, Lambert Alasdair, Nordvall Forsberg Fredrik
Theoretical Computer Science Vol 741, pp. 48-57 (2018)

more publications


KTP - Symphonic Software
Atkey, Bob (Principal Investigator) Kupke, Clemens (Co-investigator)
06-Jan-2017 - 05-Jan-2019
Coalgebraic Foundations of Semi-Structured Data (EPSRC First Grant)
Kupke, Clemens (Principal Investigator)
"Databases are irreplaceable in our modern information society. Classically, information has been stored in rigidly structured databases employing the relational data model and the query language SQL. This has led to highly optimised relational database management systems such as Oracle or Mircrosoft SQL that have large-scale industrial deployment. Underpinning much of the success of these systems has been their close connection with their mathematical foundations within relational algebra and mathematical logic. However, the internet has dramatically changed our understanding of data and databases: i) data on the Web is inherently hierarchical and arranged in a network/graph; and ii) the decentralised nature of the Web means that data comes from various heterogenous sources, is often incomplete or unreliable and has no uniform structure. Still, Web data retains some structure and, consequently, semi-structured data seeks to understand what structure persists and how it can be utilised. Examples of widespread, industrially-used data models are XML, JSON and RDF.

Mathematically, semi-structured data is usually represented as unranked labelled trees where nodes are accessed via the parent, child and sibling relations or labelled graphs where nodes are accessed via the edge relation. Data elements are stored at the nodes. Due to the special features of Web data mentioned above, query languages for semi-structured data face significantly greater challenges compared to those for data stored in a relational database: i) queries must navigate the path structure within a tree or graph and query the data elements found along such paths to explore important properties of the data; and ii) one must add a common vocabulary to the data - often via an ontology - that provides a logical layer for integrating semantically related data from heterogeneous sources. But as semi-structured data models have become increasingly sophisticated and expressive - e.g. in order to model the uncertainty attached to the data - the development of matching query and ontology languages have struggled to keep pace. Indeed, while there is a large body of work on specific semi-structured data models and their corresponding query languages there is currently no comprehensive theory that both accounts for existing semi-structured data formats and their query languages, and is able to guide their extension to the next generation of semi-structured data formats. For example, while the widely used query language XPath has been recently extended from XML to graph data, there is currently no agreed mechanism to further extend XPath to graph data with uncertainty.

Our central insight is that coalgebra provides the right level of abstraction to underpin a comprehensive theory of query and ontology languages for semi-structured data. This is because i) coalgebra generalises - via coalgebraic modal logic - the usual modal logics used for traversing trees and graphs; and ii) coalgebra generalises - via coalgebraic logic programming - standard rule-based ontology languages which are based upon logic programming."
01-Jan-2016 - 31-Jan-2018
Ghani, Neil (Principal Investigator) Kupke, Clemens (Co-investigator) McBride, Conor (Co-investigator)
01-Jan-2014 - 31-Jan-2017

more projects


Computer and Information Sciences
Livingstone Tower

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