Keynote Talk: Opening the Black Box: Deriving Rules from Data

May 23, 2017 9:35 AM
Elena Baralis ( ) is full professor in the Computer Engineering Department of the Politecnico di Torino. Her current research interests are in the field of database systems and data mining, more specifically on mining algorithms for big databases and sensor/stream data analysis.

A huge amount of data is currently being made available for exploration and analysis in many application domains. Patterns and models are extracted from data to describe their characteristics and predict variable values. Unfortunately, many high quality models are characterized by being hardly interpretable. Rules mined from data may provide easily interpretable knowledge, both for exploration and classification (or prediction) purposes. In this talk I will introduce different types of rules (e.g., several variations on association rules, classification rules) and will discuss their capability of describing phenomena and highlighting interesting correlations in data.



Call for Posters, Demos and Business Cases and Technologies

April 25, 2017 4:18 PM

Call for Posters

For additional short poster papers related to theoretical advances, novel technologies, and innovative applications concerning knowledge representation and reasoning with rules.

  • Deadline May 26th
  • Notification June 2nd

Accepted poster papers will be published as CEUR Proceedings and indexed by SCOPUS.

Call for Papers and Demos for the 11th International Rule Challenge

The best paper/presentation in the Challenge will receive the

RuleML Challenge Award (USD 500) 

  • Abstract May 1st
  • Papers May 8th
  • Notification May 15th

Accepted poster papers will be published as CEUR Proceedings and indexed by SCOPUS.

Call for Business Cases and Technologies for the Industry Track: State of Practice, Challenges and Opportunities

  • Papers May 8th
  • Notification May 15th

The best papers will be recommended with the permission of the authors for publication in the CEUR proceedings of the conference.

Keynote Talk: Meta-Interpretive Learning: Achievements and Challenges

April 19, 2017 9:39 AM
Stephen Muggleton ( ) from Imperial College London is a professor of Machine Learning and Director of Syngenta University Innovation Centre on Systems Biology.

Meta-Interpretive Learning (MIL) is a recent Inductive Logic Programming technique aimed at supporting learning of recursive definitions.

A powerful and novel aspect of MIL is that when learning a predicate definition it automatically introduces sub-definitions, allowing decomposition into a hierarchy of reuseable parts. MIL is based on an adapted version of a Prolog meta-interpreter. Normally such a meta-interpreter derives a proof by repeatedly fetching first-order Prolog clauses whose heads unify with a given goal. By contrast, a meta-interpretive learner additionally fetches higher-order meta-rules whose heads unify with the goal, and saves the resulting meta-substitutions to form a program.

This talk will overview theoretical and implementational advances in this new area including the ability to learn Turing computabale functions within a constrained subset of logic programs, the use of probabilistic representations within Bayesian meta-interpretive and techniques for minimising the number of meta-rules employed.

The talk will also summarise applications of MIL including the learning of regular and context-free grammars, learning from visual representions with repeated patterns, learning string transformations for spreadsheet applications, learning and optimising recursive robot strategies and learning tactics for proving correctness of programs. The talk will conclude by pointing to the many challenges which remain to be addressed within this new area.

Keynote Talk: The Secret Life of Rules in Software Engineering

April 11, 2017 9:16 AM
Jordi Cabot (IN3-UOC, Barcelona, will give an interesting talk explaining how UML + OCL can largely be used for rule modelling in software engineering.

Explicit definition and management of rules is largely ignored in most software development projects. While the most “popular” software modeling language (UML) enjoys some measure of success in real-world software projects, its companion, the Object Constraint Language (the OMG standard to complement UML models with textual constraints and derivation rules) is largely ignored. As a result, rules live hidden in the code, implemented in an ad-hoc manner.
This somehow worked when data was mostly stored in relational databases and DBAs could at least enforce some checks on that data. But now, data lives in the open (e.g., data as a service, big data), accessible in a variety of formats (NoSQL, APIs, CSVs,…). This evolution facilitates the consumption and production of data but puts at risk any piece of software accessing it, at least in case no proper knowledge of the structure, quality and content of that data is available. And with the emergence of open data, it’s not only the software who accesses the data but people as well.
In the talk, he argues that rules must become first-class citizens in any software development project and describe our initiatives in discovering, representing and enforcing rules on (open and/or semi-structured) data.