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

Structure of Talk

Individual research directions

General techniques for creative reasoning

A case study

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

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

University of A CorunhaHybrid Society (HS)

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

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

Creative Systems GroupUniversity of Coimbra Computational Models of Creativity

– Analogy– Evolution– Conceptual Blending

Models of Surprise Hybrid Societies for Creativity

Assessment

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

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

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

General Techniques

Conceptual blending Metaphorical/analogical reasoning Inductive inference Hypothesis repair Evolutionary methods

Conceptual Blending

Blend: Rutherford Atom

Input: Atom (1) Input: Solar System(2)

E = electron N = nucleus r = rotates around N much bigger than E

S

P E

N

N=S

r

E = P

o

P = planet S = sun o = orbits around S much bigger than P

Electron = Atomic Planet Nucleus = Atomic Sun Gravity-like force keeps the electrons in orbit about the nucleus

BUT:

Electrons have a statistical rather

than absolute position in space

r=o

similar

similar

Metaphorical Reasoning

“A poison secreted by certain animals”

Venom (1)

Poison (1)

isa

isa

isa

Substance

Entity

Object

“Anything that harms or destroys”

Poison (2)

Destructiveness

isa

isa

isa

Quality

Abstraction

Attribute

“An artist who is master of a particular style”

Insult (1)

Disrespect

isa

isa

isa

Communication

Abstraction

Relation

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

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

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

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)

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

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

Gene Ontology (Example)

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

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

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