Transcript Slide 1

Reasoning on the Web:
Theory, Challenges, and
Applications in Bioinformatics
Prof. Michael Schroeder
Biotec/Dept. of Computing
TU Dresden
[email protected]
comas.soi.city.ac.uk
Biotec
Contents
 Motivation
 Beyond the web: Rules, Reasoning, Semantics,
Ontologies
 Semantics of Deduction Rules
 Argumentation Semantics
 Fuzzy Reasoning
 Reaction rules
 Vivid Agents
 Prova
 Applications in Bioinformatics
By Michael Schroeder, Biotec, 2003
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The Web
 A great success story, but…
 it’s the web for humans, not machines
 Many areas, such as biology, have fully embraced
the web
 Human genome project is only tip of the iceberg
 More than 500 tools and databases online
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Example: Pubmed
 >12.000.000 literature
abstracts
 Great resource if one
knows what one is
looking for
 “Kox1” has 17 hits
 But “diabetes” will
produce >200.000
 Often need to automatically
process abstracts
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Title
Results of PubMed
 Lorenz P, Transcriptional repression mediated by the
KRAB domain of the human C2H2 zinc finger protein
Author
Kox1/ZNF10 does not require histone deacetylation.
Biol Chem. 2001 Apr;382(4):637-44.
 Fredericks WJ. An engineered PAX3-KRAB
transcriptional repressor inhibits
the malignant
Year
Journal of alveolar rhabdomyosarcoma cells
phenotype
harboring the endogenous PAX3-FKHR oncogene.
Mol Cell Biol. 2000 Jul;20(14):5019-31.
...
However, to a machine things look
different!
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Results of PubMed
 Lorez P, Trascriptioal
repressio mediated by the KRAB
domai of the huma C2H2 zic
figer protei Kox1/ZNF10 does
ot require histoe
deacetylatio.
Biol Chem. 2001 Apr;382(4):63744.
 Fredericks WJ. A egieered
PAX3-KRAB trascriptioal repressor
ihibits the maligat pheotype
of alveolar rhabdomyosarcoma
cells harborig the edogeous
PAX3-FKHR ocogee.
Mol Cell Biol. 2000
Jul;20(14):5019-31.
Solution:
tag data (XML)
...
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Results of PubMed
 <author>Lorez
P</author><title>Trascriptioal repressio
mediated by the KRAB domai of
the huma C2H2 zic figer protei
Kox1/ZNF10 does ot require
histoe deacetylatio. </title>
<journal>Biol Chem </journal><year>2001<year>
 <author>Lorez
P</author><title>Trascriptioal repressio
mediated by the KRAB domai of
the huma C2H2 zic figer protei
Kox1/ZNF10 does ot require
histoe deacetylatio. </title>
<journal>Biol Chem </journal><year>2001<year>
However,
to a machine things look different!
 ...
By Michael Schroeder, Biotec, 2003
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Results of PubMed
 <author>Lorez
P</author><title>Trascriptioal
repressio mediated by the KRAB domai
of the huma C2H2 zic figer protei
Kox1/ZNF10 does ot require histoe
deacetylatio. </title>
<joural>Biol Chem
</joural><year>2001<year>
 <author>Lorez
P</author><title>Trascriptioal
repressio mediated by the KRAB domai
of the huma C2H2 zic figer protei
Kox1/ZNF10 does ot require histoe
deacetylatio. </title>
<joural>Biol Chem
</joural><year>2001<year>
Solution:
use ontologies
(Semantic Web)
 ...
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GeneOntology
 Biologists have recognised
the problem of semantic
inter-operability between
disparate information
sources
 GeneOntology (GO) is effort
to provide common
vocabulary for molecular
biology
 GO has >10.000 terms in
three branches “function”,
“process”, “localisation”
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GeneOntology
 Has 13 levels
 Width broadens to level 6 (3885 terms wide) then shrinks
 Number of leaves per levels broadens to level 6 (1223 leaves) then
shrinks
 Average term has 4 words
 Maximal term has 29 words:
Oxidoreductase
activity, acting on
paired donors,
with incorporation
or reduction of
molecular oxygen,
2-oxoglutarate as
one donor, and
incorporation of
one atom each of
oxygen into both
donors
4500
4000
Breadth of GO
3500
3000
2500
2000
1500
1000
500
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Motivation Summary
 Web in the old days
 HTML (for humans)
 Web these days
 HTML
 XML, Ontologies (for machines)
 Web of the future
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HTML
XML, Ontologies
rules, reasoning, semantics
access to computational resources (a la grid-computing)
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Open Problems
 Part I: Theory of rules and reasoning on the web:
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Knowledge representation: Which level of expressiveness?
Semantics: How to guarantee inter-operability
Reasoning: Fuzzy reasoning and unification
Reactivity: Vivid agents
 Part II: Applications of rules and reasoning on the web:
 Integration and querying of information sources
 Integration: transmembrane prediction tools
 Integration: protein structure DB and structure classification
 Consistency checking
 Ontology: If A is B and B is C, then the ontology should not
explicitly mention A is C, as it is already implicit
 Annotation: Do different tools agree or disagree?
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The wider Picture: www.RuleML.org
 Goal: develop Web language for rules
 using XML markup,
 formal semantics, and
 efficient implementations.
 Rules: derivation rules, transformation rules, and
reaction rules.
 RuleML can thus specify queries and inferences in
Web ontologies, mappings between Web
ontologies, and dynamic Web behaviors of
workflows, services, and agents.
 Currently, some 30 international members and
close collaboration with W3C
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The wider Picture: REWERSE
 Reasoning on the Web with Rules and Semantics
 FP6 Network of Excellence with nearly 30 partners
 Working groups on Infrastructure and Applications
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Composition
Typing
Policies
Querying
Reactivity and evolution
 Personalised Web sites
 Calendar systems
 Bioinformatics
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Part I: Theory
 Motivation: Expressive Knowledge Representation
 Part I.a: Argumentation as LP semantics
 Notions of attack and justified arguments
 Hierarchy of semantics
 Proof procedure
 Part I.b: Fuzzy unification and argumentation
 Fuzzy negation
 Fuzzy argumentation
 Fuzzy unification
 Part I.c: Vivid Agents
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Part I.a: A Hierarchy of Semantics
 RuleML caters for different degrees of knowledge
representation
 A hierarchy of semantics is required to guarantee
inter-operation.
 Analogy: In HTML, <b>Michael</b> will be
interpreted differently in Netscape (Michael) and the
text-based browser Lynx (Michael).
 Problem: How can we guarantee inter-operability
between different interpretations of rules?
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Knowledge representation
 Pete earns 500.000$ p.a.
 earns(pete,500000).
 Cross the street if there are no cars
 cross  not car
 cross   car
 The fridge is quite cheap
 cheap(fridge):70%
 Does Mike live in Londn?
 address(mike,london) = address(mike,londn): 95%
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Knowledge System Cube
z r: relational
z f: fuzzy
z d: deductive
z DB: database
z FB: factbase
fdFB
fdDB
dDB
dFB
fDB
deductive
rDB
fFB
rFB
negation
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Part I.a:
Argumentation as semantics for
Extended Logic Programs
zf: fuzzy
z d: deductive
z DB: database
z FB: factbase
dDB
dFB
fDB
rDB
deductive
fdFB
fdDB
fFB
rFB
negation
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Extended Logic Programming
 Logic Programming with 2 negations
 Default negation:
not p : true if all attempts to prove p fail.
 Explicit negation:
p : falsehood of a literal may be stated explicitly.
 Coherence principle:
p  not p
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Argumentation
 Interaction between agents in order to
 gain knowledge
 revise existing knowledge
 convince the opponent
 solve conflicts
 Elegant way to define semantics for
(extended) logic programming
 Dung
 Kowalski, Toni, Sadri
 Prakken & Sartor
 Etc.
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Arguments
 An argument is a partial proof, with implicitly negated
literals as assumptions.
 Argument = sequence of rules
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Attacking arguments
 Two fundamental kinds of attack:
 A undercuts B = A invalidates premise of B
 P: Let’s go to the lake as it is not snowing anymore
 O: Hang, it is snowing
 A rebuts B = A contradicts B
 P: Let’s go to the lake as it is not snowing
 O: Let’s not, as I’ve got to prepare my talk
 Derived notions of attack used in Literature:
 A attacks B = A u B or A r B
 A defeats B = A u B or (A r B and not B u A)
 A strongly attacks B = A a B and not B u A
 A strongly undercuts B = A u B and not B u A
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Proposition: Hierarchy of attacks
Attacks = a = u  r
Defeats = d = u  ( r - u -1)
Undercuts = u Strongly attacks = sa = (u  r ) - u
Strongly undercuts = su = u - u
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-1
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-1
Fixpoint Semantics
 Argumentation:
 game between proponent and opponent
 argument A is acceptable if opponent’s x-attack is countered by
proponent’s y-attack, which proponent already accepted earlier.
 Acceptable
 Let x,y be notions of attack.
 An argument A is x,y-acceptable w.r.t. a set of arguments S iff
 for every argument B, such that (B,A)  x,
there is a C  S such that (C,B)  y
 Fixpoint semantics


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
Fx/y (S) = { A | A is x,y-acceptable w.r.t. S }
x/y-justified arguments = Least Fixpoint of Fx/y.
x/y-overruled arguments = x-attacked by a justified argument.
x/y-defensible iff neither justified nor overruled
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Theorem: Relationship of semantics
 Weakening opponent or strengthening proponent increases justified
arguments
and
Sartor’s to attack,
If Prakken
opponent
is allowed
 semantics
Different
notions
ofispriorities
acceptability
give rise
IfDung’s
opponent
allowed defeat
, to different argumentation
grounded
w/o
type
of defense does not matter
semantics
WFSX
type of defense
does not matter
argumentation
semantics
su/a=su/d
If opponent is allowed undercut,
su/u
su/sa
defense with (a,u,sa) or without
(su,u) rebut makes a difference
sa/u=sa/d=sa/a
su/su
u/a=u/d=u/sa
sa/su=sa/sa
u/su=u/u
d/su=d/u=d/a=d/d=d/sa
a/su=a/u=a/a=a/d=a/sa
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Proof procedure
 Dialogues:
 x/y-dialogue is sequence of moves such that
 Proponent and Opponent alternate
 Players cannot repeat arguments
 Opponent x-attacks Proponent’s last argument
 Proponent y-attacks Opponent’s last argument
 Player wins dialogue if other player cannot move
 Argument A is provably justified if proponent wins all
branches of dialogue tree with root A
 Concrete implementation SLXA:
 Since u/a=u/d=u/sa=WFSX
 compute justified arguments with top-down proof
procedure SLXA for WFSX [Alferes, Damasio, Pereira]
 SLXA can be adapted for other notions
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Part I.b:
Fuzzy unification and argumentation
z r: relational
z f: fuzzy
z d: deductive
dDB
z DB: database
z FB: factbase
dFB
fDB
rDB
deductive
fdFB
fdDB
fFB
rFB
negation
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Classical Fuzzy Logic
 Solution:
 Truth values in [0,1] instead of {0,1}.
 Assertions:
 p:V (p a formula, V a truth value).
 Conjunction:
 p:V, q:W  p  q : min(V,W)
 Disjunction:
 p:V, q:W  p  q : max(V,W)
 Inference:
 p  q1, …, qn ; q1:V1, …, qn:Vn  p : min(V1, …, Vn)
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Fuzzy Negation
 Classical fuzzy negation:
 L:V  L: 1-V
(Zadeh)
 Our setting (fuzzy adaptation of WFSX):
 L:V and L:V’ with V’  1-V possible
 L and L not directly related.
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Fuzzy Coherence Principle
 If L:V and V > 0, and not L:V’,
then V’ > V.
 “If there is some explicit evidence that L is false, then there is
at least the same evidence that L is false by default.”
 If L:V and V > 0,
then not L: 1.
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Law of excluded...
...contradiction
...middle
 p  p :V
  V > 0 possible
 Contradictory programs!
 not p  p : V
  V > 0 possible
 By coherence principle!
  Contradiction removal
By Michael Schroeder, Biotec, 2003
 not p  p : V
 V > 0
 p  p : V
  V = 0 possible
  p is unknown
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Strength of an argument
 Strength of an argument:
 Fact: value is given
 Rule: minimum of body literals
 Argument: Conclusion
 Least fuzzy value of the facts contributing to the
argument.
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Theorems
 Theorem (Soundness and Completeness)
There is a justified argument of strength V for L
iff
There is a successful T-tree of truth value V for L
 Theorem (Conservative Extension)
Argumentation semantics is a conservative extension of WFSX.
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Application: Fuzzy unification
 Open systems:
 knowledge and ontologies may not match
 interaction with humans
 “Does Mike live in Londn?”
 Approach:
 address(mike,london) = address(mike,londn): 95%
 adapt unification algorithm
(normalised edit distance over trees net)
 embed into argumentation framework
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Finding Mismatches:
Edit distance
 Edit distance between strings A and B:
 minimal number of delete, add, replace operations to
convert A into B.
 efficient implementation with dynamic programming
 Example:
 e(address,adresse)=2, e(007,aa7)=2
 Normalise:
 ne(A,B) = e(A,B) / max{ |A|, |B| }
 Trees:
 net = sum of all mismatches divided by sum of all
max lengths
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Fuzzy unification and arguments
 net is conservative extension of MGU (most general
unifier)
 net(t,t’)  ne(t,t’)
 Adapt definition of argument for fuzzy unification
V-argument: for all L in a body, there is L’ in head such
that net(L,L’)  1-V
A V-undercuts B if A contains not L and B’s head is L’
and net(L,L’)  1-V
A V-rebuts B if A’s head is L and B’s head is L’ and
net(L,L’)  1-V
 Adapt previous definitions accordingly
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Comparison: Argumentation
 Our framework allows us to relate existing and new
argumentation semantics:
 Dung= a/su=a/u=a/a=a/d=a/sa
 Prakken&Sartor = d/su=d/u=d/a=d/d=d/sa
 WFSX = u/a = u/d = u/sa
 Dung  Prakken&Sartor  WFSX
 Proof Theory and Top-down Proof Procedure
adapted from Alferes, Damasio, Pereira’s SLXA
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Comparison: Fuzzy Argumentation
 Wagner:
 Scale: -1 to +1
 Unlike WFSX, he relates F and F:
 F: -V iff F:V
 We adopted his interpretation for not:
not F:1 if F:V, V>0
 Relates his work to stable models, but there is no
top-down proof procedure for stable models
[Alferes&Pereira]
 Our approach conservatively extends WFSX,
hence we can adapt proof procedure SLXA
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Comparison: Fuzzy unification
 Arcelli, Formato, Gerla
 define abstract fuzzy unification/resolution framework
 cannot deal with missing parameters (common
problem [Fung et al.])
 no conservative extension of classical unification
 we use concrete distance: edit distance
 Evaluated idea on bioinfo DB
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Conclusion
 “A database needs two kinds of negation” (Wagner)
 Argumentation is an elegant way of defining semantics
 Our framework allows classification of various new
and existing semantics
 Efficient top-down proof procedure for justified
arguments
 Argumentation as basis for belief revision (REVISE)
 We cover the whole knowledge system cube
including fuzzy argumentation
 Defined fuzzy unification, which is useful in open
systems
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Part I.c: Vivid Agent
 A vivid agent is a software-controlled system,
 whose state is represented by a knowledge base and
 whose behaviour is represented by
 action- and
 reaction rules
 Actions are planned and executed to achieve a goal
 Reactions are triggered by events
Epistemic RR: Effect <- Event, Cond
Physical RR: Action, Effect <- Event, Cond
Interaction RR: Msg, Effect <- Event, Cond
By Michael Schroeder, Biotec, 2003
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Interface
Vivid Agent
Events
Reaction Rules
Perception
Reaction
Cycle
Intentions
Goals
Planner
Believes/
Updates
KB
By Michael Schroeder, Biotec, 2003
Goals
Action rules
Believes
Believes
KB
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Agent State and Transition Semantics
 Agent State:
 Event queue, Plan queue, Goal queue, Knowledge base
 Transition semantics
 Perception
 Add event to agent’s event queue
 Reaction
 Pop event from event queue, execute reactions including
update of knowledge base
 Plan execution
 Execute action of plan in plan queue
 Replanning
 If action fails, replan
 Planning
 Pop goal from goal queue and generate plan
By Michael Schroeder, Biotec, 2003
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Implementation in Prova
 Original Implementation in PVM-Prolog
 Course-grain parallelism (PVM) for each agent and
Prolog threads for an agent’s components
 Currently: Prova
 is a Java-based rule engine
 easy integration of all kinds of data sources. e.g.,
database, web services, etc.
By Michael Schroeder, Biotec, 2003
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Part II: Application to Bioinformatics
 NSF and EU’s strategic research workshop found that
bioinformatics could play the role for the semantic web,
which physics played for the web.
 Why?
 Masses of information
 Masses of publicly accessible online information
 (e.g. 8000 abstracts per month and over 500 tools)
 Data (more and more often) published in XML
 Data standards are accepted and actively developed
 Much valuable information scattered (as production cheap and
hence not centralised)
 Systemsintegration and interoperation prime concern (e.g.
GeneOntology)
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Example: Information Agents for…
 … Protein interactions
 PDB, SCOP
Facilitator
 … Protein annotation
 TOPPred, HMMTOP,…
 Information source
 Wrapper
 Mediator
 Facilitator
Mediator
Wrapper
Source
Wrapper
Source
By Michael Schroeder, Biotec, 2003
Wrapper
Source
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Example 1: Protein Interaction:
 PDB: Protein structures
 SCOP: Structure classification
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Example 1:
PSIMAP: Structural Interactions
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Example 1: Protein Interaction:
How it is currently done
 PDB: 15 Gigabyte in flat files
 SCOP: 3 flat files
 How?
 Download PDB, SCOP files
 Think up DB schema and populate MySQL DB
 Run some Perl scripts on various machines, that
grind through the data and analyse it
 Run some Java to visualise results
 Problem: “Business logic” not separated
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How our Prova system can run execute
Might be held locally in file,
remotely from a DB,
 Declarative and executable
throughspecifications
a web service, on the grid, etc.
 Interaction(Superfamliy1, Superfamliy2) if
 PDB(Protein),
 Domain(Protein,Domain1),
Local or remote computation.
 Domain(Protein,Domain2),
 SCOP Superfamily(Domain1, Superfamily1),
 SCOP Superfamily(Domain2, Superfamily2),
 InteractionDD(Domain1,Domain2, 5 Ang, 5 Residues)
 Separation of information integration workflow
 Easier to maintain
 Platform independence, because of Java
 Flexible, optimized execution
Query optimization and load-balancing of computations
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Actual Prova Code
% ACTUAL PROVA CODE
% Given the open database connection DB
% and a unique protein identifier in Protein
% Data Bank PDB_ID, test whether the provided
% domains with IDs PXA and PXB interact
% (have at least 5 atoms within 5 angstroms)
scop_dom2dom(DB,PDB_ID,PXA,PXB) :access_data(pdb,PDB_ID,Protein),
scop_dom_atoms(DB,Protein,PXA,DomainA),
scop_dom_atoms(DB,Protein,PXB,DomainB),
DomainA.interacts(DomainB).
By Michael Schroeder, Biotec, 2003
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Caching
% Two alternative rules for either retrieving data
% from the cache or accessing the data from its
% original location and caching it.
access_data(Type,ID,Data,CacheData) :% Attempt to retrieve the data
Data=CacheData.get(ID),
% Success, Data (whatever object it is) is returned
!.
access_data(Type,ID,Data,CacheData) :% Retrieve the data from its location and update
the cache
retrieve_data_general(Type,ID,Data),
update_cache(Type,ID,Data,CacheData).
By Michael Schroeder, Biotec, 2003
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Example 2: GoPubmed
By Michael Schroeder, Biotec, 2003
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Consistency of GO
 Simple example:
 Parsimony: If A is-a C is explicitly stated in the
ontology, it should be possible to derive it implicitly
 I.e. Don’t state A is-a C if you have already A is-a B and
B is-a C
 Done with Prova
By Michael Schroeder, Biotec, 2003
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Towards functional annotation through
GoPubmed
Protein Name/Enzyme activity
Pyruvate kinase M1 isozyme
CAMP dpt protein kinase type II
regulatory chain
Galactokinase
Tropomyosin bêta chain
HnRNP DO
kinase
transferase
lyase
isomerase
one other
X
X
X
X
oxireductase
X
X
X
X
X
X
X
X
X
X
X
X
hydrolase
X
cyclase
X
X
X
X
helicase
X
By Michael Schroeder, Biotec, 2003
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Example 3: Consistent Integration of
Protein Annotation
By Michael Schroeder, Biotec, 2003
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Conflicts
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Example: Edit2TrEMBL
 EditToTrEMBL (Steffen Möller, EBI): automate annotation
of DNA sequences by combining results of various tools
and databases, which are online
Analyser
Info object
Host
Dispatcher
Info object
Info object
HostInfo object
Info object
Analyser
Host
Analyser
Host
Info
Info object
Infoobject
object
By Michael Schroeder, Biotec, 2003
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Challenge
 Uncertain, incomplete, vague,
contradictory information
 Wrappers domains overlap: How
Facilitator
can mediator resolve conflicts?
 How can mediator integrate
information consistently?
 How can mediator improve info
Mediator
quality using overlapping info and
Wrapper
inconsistencies
 Mediator contains conflict
resolution component
Source
 Semantic conflict resolution
requires domain knowledge to
Wrapper
Wrapper
identify conflicts
 We use extended logic
programming
Source
Solution:
Common
Problem:
Source
Semantic consistency
Overlapping
information
can lead checking
to inconsistencies
By Michael Schroeder, Biotec, 2003
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Modelling domain knowledge
 Facts, Rules, Assumptions, Integrity Constraints
For example:
 The length of transmembrane regions is limited:
false if ft(AccNo,transmembrane,From,To), To-From >25
false if ft(AccNo,transmembrane,From,To), To-From <15
 Maximal difference in membrane borders
false if
ft(Agent1,Acc,transmembrane,From1,To1),
ft(Agent2,Acc,transmembrane,From2,To2),
(From1>From2,From1<To2;To1>From2,To1<To2),
(abs(From2-From1)>4;abs(To2-To1)>4).
 Assessment of predictions:
probability(ft(tmhmm,p12345,transmem,6,26), 0.5)
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REVISE
 REVISE detects conflicting arguments and
computes minimal set of assumptions, which
removes conflict
 Dropping these assumptions yields minimal
consistent annotation of all predictions
 Minimality is based on probabilities given as part
of predictions
 alternative: cardinality, set-inclusion
By Michael Schroeder, Biotec, 2003
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Vision: A semantic Grid for
Bioinformatics
BioNet
Explorer
Interaction Space:
PSIMAP
Expression
Space:
Space Explorer
Pathway
Space:
Literature Space:
Classification Server
By Michael Schroeder, Biotec, 2003
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Conclusion
 Advanced applications on the web, will require rules
and reasoning
 Part I:
 Argumentation is an elegant way of defining semantics
 Classification of various new and existing semantics
 Fuzzy reasoning and unification
 Reactivity with vivid agents and prova
 Part II:
 Bioinformatics requires a semantic web and the
semantic web requires bioinformatics
By Michael Schroeder, Biotec, 2003
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Acknowledgment
 Ralf Schweimeier (Argumentation semantics)
 Panos Dafas, Dan Bolser (PSIMAP)
 Steffen Moeller (Edit2Trembl)
 David Gilbert (Fuzzy Unification)
 Ralph Delfs, Alexander Kozlenkov (Go, Prova)
 Carlos Damasio (REVISE)
 More information at comas.soi.city.ac.uk
 Email: [email protected]
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