Objective
The general aim of ILP is to develop theories, techniques and applications of inductive learning from observations and background knowledge in a first order logical framework. This project aims at continuing the ILP 1 project in which several significant research results have been obtained. To close the gap between conceptual work and applications, the consortium has identified four key areas where ILP technology has great potential. These areas are (1) natural language processing, (2) data mining and discovery, (3) design and configuration, and (4) data-base design. End-users active in these areas have been united in a users club and collaborate in the project by providing relevant test data. Based on an analysis of these four application domains, 14 scientific problems in need of substantial progress have been identified and organized around 4 themes:
- Background knowledge / Techniques are needed that:
-can handle large numbers of background predicates (Relevance),
-can update theories structured in many levels (Revision),
-can carry out predicate invention in deep-structured theories (Invention)
- Complex Hypotheses / Techniques are needed that:
-can learn deep structured theories and optimise the choice of a set of clauses for a single predicate (Multi-Clause),
-can handle long chains of relevant literals, connected by shared variables (Deep),
-can better handle recursive hypotheses (Recursion),
-can search efficiently in the presence of structural concepts expressed in complex clauses (Structure).
- Built-in semantics / Techniques are needed that:
-better handle numbers (Numbers),
-can express probabilistic constraints and definitions (Probabilities),
-can learn and use constraints (Constraints),
-work more efficiently through the use of built-in predicates and algorithms (Built-in).
- Sampling issues / Techniques are needed that:
-can learn from large data sets (Large Data),
-can learn from small data sets (Small Data), and
-offer some reliability guarantees (Reliability).
The main methodology applied will be 1) to study the scientific problem starting from given application domains and data (provided by the end-user club), 2) to generalize away from the application, 3) to develop theory, techniques and implementations to cope with a specific problem, 4) to evaluate the developed framework on the application domains and data, and 5) to use the obtained feedback to re-iterate if necessary.
Inductive logic programming (ILP) is a research area lying at the intersection of inductive machine learning and logic programming.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences databases
- natural sciences computer and information sciences data science natural language processing
- natural sciences computer and information sciences data science data mining
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
3000 LEUVEN
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.