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Working Group 4

Working Group 4. Creative Systems for Knowledge Management in Life Sciences. Purpose of this Talk. We are researching methods which we believe could provide non-standard solutions to complex problems We need concrete problems to identify possible interactions between the working groups.

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Working Group 4

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  1. Working Group 4 Creative Systems for Knowledge Management in Life Sciences

  2. Purpose of this Talk • We are researching methods which we believe could provide non-standard solutions to complex problems • We need concrete problems to identify possible interactions between the working groups

  3. Structure of Talk • Individual research directions • General techniques for creative reasoning • A case study

  4. Computational Bioinformatics Laboratory, Imperial College London • Progol system • Learning of concepts in bioinformatics • Theory behind, and implementation of ILP • Applications: • Predictive toxicology, secondary structure in proteins, learning metabolic pathways • HR system • Discovering in mathematics (and bioinformatics) • Theory behind, and implementation of ATF • Applications: • Adding to databases: Integer sequences, TPTP library • Finding invariants, inventing CSP constraints, tutorials • Scientific Discovery via integration of techniques

  5. Centre for Computational CreativityCity University, London • Formal frameworks for describing and reasoning about creative behaviour • Compare seach methods and outcomes • Define value etc and reason about properties of definitions • Pattern discovery and matching technogies for multidimensional datasets • Discover/locate geometrically identical structural regions, possibly with gaps in multi-D data • Example: 3D representations of atoms in space for pharmacophore bonding models

  6. University of A CorunhaHybrid Society (HS) • Solves the problem of Value in a dynamic context. • Allows the comparison of different computer paradigms and systems. • Allow the collaboration between humans and computer systems • Allows the use of adaptive techniques such us Evolutionary Computation and Artificial Neural Networks • Development framework to validate and to allow the learning of various computational models of tasks which require creativity and a social behaviour • HS is based on machines and humans living together in a virtual and “egalitarian” society

  7. Creative Systems GroupUniversity of Coimbra • Computational Models of Creativity • Analogy • Evolution • Conceptual Blending • Models of Surprise • Hybrid Societies for Creativity Assessment

  8. University of Edinburgh • Lakatos-style reasoning: • Experts interact to build a common theory • Counterexamples used to modify conjectures; clarify concepts; improve proofs • Ways of evaluating machine creativity

  9. Universidad Complutense de Madrid • Ongoing research work: • Knowledge intensive CBR • CBRArm: framework for CBR + ontologies • Generating narrative and metaphorical texts, NLG architectures, CBR for text generation • CBR for Knowledge Management • Java documentation, helpdesks • Information Filtering + User Modeling • Computer games

  10. Creative Reasoning • Reasoning in non-standard ways to produce: • “interesting”/valued/unexpected outputs • emergent complex behaviour • Reconceptualise existing knowledge structures to get new knowledge structures with added value • using in a different way than they were intended • lateral connections that weren’t there already • Heuristic reasoning • Including sound and unsound methods • Post hoc verification • value measurements for the domain are a pre-requisite

  11. General Techniques • Conceptual blending • Metaphorical/analogical reasoning • Inductive inference • Hypothesis repair • Evolutionary methods

  12. Conceptual Blending

  13. Metaphorical Reasoning

  14. Inductive Inference • Predictive Induction • Know the positives/negatives of a concept • Search for a concept which fits categorisation • Use examples as evidence for predictive accuracy • Cross validate results • Descriptive Induction • Search for rules which associate background predicates, using data as empirical evidence • (Sometimes) use deduction to prove rules found

  15. Hypothesis Repair • Using a counterexample to repair a faulty hypothesis by: • Generalising from counterexample to a property then stating the exception in the hypothesis • Generalising from the positives and then limiting the hypothesis to these

  16. Evolutionary Methods • Exploration of complex search spaces • in non-uniform ways • Based on biologically inspired evolutionary notions such as gene recombination, mutation, fitness functions • Dynamically adaptive systems

  17. Potential Applications • Levels of discovery • You know what you are looking for, • But you don’t know what it looks like • You don’t know what you are looking for • But you know you are looking for something • You didn’t know you were even looking for anything • Levels of search • At the object level (millions/billions of data points) • At the semantics level (tens of thousands of terms) • At the meta-level (scores of techniques)

  18. Possible (General) Application:Ontology Maintenance • Ontologies standardise concepts • And standardise relationships between them • Many areas see the need for ontologies • Including scientific domains such as life sciences • Very important that the ontology represents current scientific thinking • Need to continually maintain ontology • New nodes • New links • Need to continually interpret ontology • Large scale structures

  19. Case Study – Gene Ontology • ~14,000 terms from biology/genetics • Process, function, structure • Structured into hierarchies using isa/partof • Each term has genes associated • ~ 1.3 million genes (from, e.g., GenBank) • Aims to unify biology • Databases are in a bad state • Different interpretations/notations/standards

  20. Gene Ontology (Example)

  21. Methods for Ontology Maintenance • Mining rules between concepts using inductive techniques (adds edges) • Project to use HR for this in progress • Project to use Progol to learn terminology • Conceptual blending • Invent new concepts (nodes) • Metaphorical reasoning • Look at structure to reorganise links • Hypothesis repair • Explain genes which are seemingly misclassified

  22. Proactive and Reactive Applications • Proactive • Attempt to make discoveries in GO • Give value added when someone submits a new term to the ontology • Reactive • A new gene is added which (using sequence alignments) is associated with “wrong” concept • Creatively re-organise ontology to fix problem

  23. The Bottom Line • We have solutions but not problems • With respect to Life Sciences • Our application domains are disparate • But our methods are general • We’re already thinking about certain tasks/problems in life sciences • Predictive toxicology • Protein structure prediction • And we’re inventing our own problems • Maintaining the Gene Ontology • But we really need to discuss what it is that standard techniques do not yet give you • And see what creative systems/techniques can do

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