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PhD thesis Date: 14/07/2009
Acquisition and Understanding of Process Knowledge Using Problem
Solving Methods
Jose Manuel Gómez Pérez
Facultad de InformáticaUniversidad Politécnica de Madrid
Campus de Montegancedo sn28660 Boadilla del Monte, Madrid
http://www.oeg-upm.net
[email protected]: 34.91.3363670
Fax: 34.91.3524819
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
2
Introduction and motivation
Open research problems and work hypotheses
Acquisition of process knowledge by SMEs
Provenance analysis of process executions by SMEs
Evaluation
Conclusions and future research problems
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Knowledge programming
Knowledge modeling
KA by Knowledge Engineers (KEs)
KA by Subject Matter Experts (SMEs)
Knowledge Acquisition: Towards SME empowerment
3
Subject Matter Expert (SME)
KnowledgeEngineer (KE)
The Knowledge Acquisition Bottleneck
Ontologies
KA Frameworks
Problem Solving Methods
The Role Differentiation
Principle
The Knowledge Level
KRR Languages
Ontology editors
KB edition by SMES
Knowledge formulation by SMEs
KB maintenance
Collaborative knowledge creation
DARPA’s KSE
DARPA’s HPKB & RKF
OCML
KARL
KRAKENDISCIPL-RKF
CHIMAERA
SEMANTIC WIKIS
SHAKEN
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Knowledge types
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RUL(inference)
CLS(classification)
FACT (factual
knowledge)
MAT(mathematics)
CMP(comparison)
TAB(tables)
PCS(processes)
CAUS(cause-effect)
DAT(data structures)
PROC(procedural)
EXP(experiments)
US(underspecified)
TRANS(translation)
NF(non functional)
SPACE(spatial)
PWR(part-whole)
TIME(temporal)
GRA(diagrammatic)
• Processes are special knowledge types that• Relate to the
sequence of
operations and involved resources leading to the production of
some outcome
• Encapsulate preconditions, results, contents, actors, and
causes
• Process knowledge is complex• It builds on top of other
simpler
knowledge types, like facts and rules
Source: the Halo project KR analysis phase for the domains of
Chemistry, Biology, and Physics
“What is released/added/increased upon binding of two amino
acids?”
“A piece of solid calcium is heated in oxygen gas. ...”
“Find correct RNA sequence for a given DNA sequence.”
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Why processes are important
5
• Processes appear in 37% (average) in the domains of Biology,
Chemistry, and Physics
• The most important knowledge type in Chemistry (53%)
• Second in Biology (35%)
• Fourth in Physics (22%)
Source: The Halo project KR analysis phase for the domains of
Chemistry, Biology, and Physics
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Work objectives
6
PCS SMEs
PSMsWhat Whom
How
Objective 1: To enable SMEs to formulate processes without
KEs
Objective 2: To support SMEs in understanding process
executions
Provide reusable guidelines to formulate process knowledge
Support reasoning
Describe the main rationale behind a process
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
PSM perspectives
7
Task-method decomposition
Interaction
Knowledge flow
PSM establishes and controls the sequence of actions required to
perform a task
Defines knowledge required at each task step
Black-box perspective
Knowledge transformation within the PSM
Hierarchically defines how tasks decompose into simpler
(sub)tasks
Describes tasks at several levels of detail
Provides alternative ways to achieve a task
Task
MethodRole
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Provenance analysis of process executions
8
?
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
In summary
• This thesis proposes the use of PSMs as a novel approach for
supporting SMEs both in the formulation of process knowledge and in
the provenance analysis of process executions
• It also explores to what extent it is possible to build such
tools that take KEs out of the formulation and analysis loop
9
• Ultimately, it aims at showing that it is possible to engage
users• To generate computer-readable content
represented in formal languages• To apply knowledge
representation and reasoning
techniques to analyze the outcomes of automated,
knowledge-intensive processes
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
10
Introduction and motivation
Open research problems and work hypotheses
Acquisition of process knowledge by SMEs
Provenance analysis of process executions by SMEs
Evaluation
Conclusions and future research problems
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Open research problems and work hypotheses: Objective 1
11
Objective 1: To provide SMEs with the means to formulate process
knowledge in their domains of expertise without the intervention of
KEs
• H1: Empowering SMEs can increase KB quality and reduce
costs
• H2: The complexity of process knowledge requires providing
SMEs with specific means to acquire and reason with processes
• H3: PSMs can reduce the complexity of acquiring process
knowledge by SMEs
• H4: The proposed methods and tools abstract SMEs from the
underlying representation
• H5: The proposed methods and tools produce sound and complete
executable process models
• H6: The proposed method and tools are flexible and reusable
across domains
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Open research problems and work hypotheses: Objective 2
12
Objective 2: To support SMEs in analyzing and understanding
process executions
• H7: The analytical capabilities of PSMs can provide SMEs with
meaningful interpretations of process executions
• H8: The method proposed identifies the main rationale behind
processes by detecting occurrences of PSMs in their execution
logs
• H9: The method proposed can use the hierarchical structure of
PSMs to describe process executions at different levels of
detail
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
13
Introduction and motivation
Open research problems and work hypotheses
Acquisition of process knowledge by SMEs
Provenance analysis of process executions by SMEs
Evaluation
Conclusions and future research problems
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Acquisition of process knowledge by SMEs
14
• Four main contributions• C1: a process metamodel, which
provides the terminology
necessary to express process entities in scientific domains, and
the relations between them
• C2: a PSM library, which provides high-level, reusable
abstractions for process representation and a method for its
development
Objective 1: To provide SMEs with the means to formulate process
knowledge in their domains of expertise without the intervention of
KEs
• C3: a graphical process modeling and reasoning environment,
which applies the previous contributions in order to enable SMEs to
formulate process knowledge
• C4: a method for the automatic synthesis of executable process
models from SME-authored process diagrams, supported by an
underlying representation and reasoning formalism
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 1: The process metamodel
• Resources (roles)• Containers of domain conceptsthat
can play a particular role
• Actions • Inspired by activities in EO and TOVE
• Relations• Forks
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 2: Building a PSM library for acquisition of
process knowledge
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Identification
Decomposition and abstraction
• 755 AP questions analyzed• >100 domain-specific processes•
Four main process categories
• Join• Split• Modify• Locate
Extends work done in the Halo analysis phase by Omniscience
and
Ontoprise teams Reusable PSM library
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 2: A PSM example (decompose & combine)
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name decompose & combinegoal member(Recombination set,
Element) and
member(Constituents set, Piece) andpart-of(Piece, Element)
andpart-of(Piece, Combination) andproperties(Element, ep)
andproperties(Combination, cp) andnot equal(ep, cp)
actions decompose, combineinput action decomposeoutput action
combineinput roles Recombination set, Decomposer, Combinatoroutput
roles Combination, Byproduct
“Crystallization occurs when certain pairs of oppositely charged
ions attract each other so strongly that they form an insoluble
ionic solid. This process coexists with dissolution processes in
precipitation reactions”
The addition of a colorless solution of potassium iodide (KI) to
a colorless solution of lead nitrate [Pb(NO3)2] produces a yellow
precipitate of lead iodide (Pbl2) that slowly settles to
the bottom of the beaker.
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 3: The graphical process modeling environment
18
Domain-level reasoning and
control flow evaluation
Process metamodel
PSM library (e.g.
decompose & recombine)
Domain process to which this
process diagram is
boundConsistency maintenance
(knowledge base and process data flow)
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 4: The process representation and reasoning
formalism
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• Bridges the gap between the knowledge level and the
operational level
• Focus on three main aspects• Process frame• Data flow• Control
flow
Input action
Output action
“In a long-distance jump competition, an athlete can jump only
after his mitochondria have accumulated enough energy for his
muscles to contract.”
Conditionalprecedence(control flow)
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 4: Addressing the frame problem
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• Two states (pre and post) per process action
• Pre state: portion of the process frame in the scope of an
action
• Post state: updated pre state of the action after its the
execution
• Actions read from their pre state and write into their post
state
• At modeling time we automatically synthesize process rules
that manage the process frame during execution
• Setup rules: build the pre state of the input actions of the
process
• Precedence rules: describe what actions can be connected with
each other by means of their outputs and inputs
• Transition rules: describe the transition between pre and post
states
Explicit manipulation of the process frame allows runtime
management of data and control flow
Pre state of action
Dissolve
Post state of action
Dissolve
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 4: The Code synthesis mechanism
input actions intermediate actionsoutput actions
setup rules x - -
transition rules x x x
precedence rules - x x
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FORALL m, e, v m:mitochondrion@preState(accumulateEnergy) AND
m:TOOL@preState(accumulateEnergy) AND e:
energy@preState(accumulateEnergy) AND
e:RESOURCE@preState(accumulateEnergy) AND e[hasEnergyValue ->
v]@preState(accumulateEnergy) v].
setup
FORALL e, v e:energy@preState(muscleContraction) AND
e[hasEnergyValue -> v]@ preState(muscleContraction) v]@
postState(accumulateEnergy).
precedence
FORALL m, e, j j: jump@postState(muscleContraction) AND j:
OUTPUT@postState(muscleContraction) AND muscleContraction[PROVIDES
-> j] @postState(muscleContraction)
muscleContraction]@preState(muscleContraction) AND e:energy@
preState(muscleContraction) AND
e:RESOURCE@preState(muscleContraction) AND e[IS_CONSUMED_BY
-> muscleContraction] @preState(muscleContraction).
transition
• Action-centric algorithm• Each action results into a set
of process rules in F-logic
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 4: Domain-level reasoning within processes
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“The length of the jump is directly proportional to the amount
of energy accumulated”
“The minimum amount of energy needed to jump are 5 calories”
FORALL length, anEnergy, v aJump(out(hasLength,
length):jump@update(muscleContraction) aJump(out(hasLength, length)
[hasLength -> length]@update(muscleContraction) v]
@preState(muscleContraction) AND multiply(length, 2, v).
FORALL anEnergy, v enough_energy_for_contraction(anEnergy)
@check_enough_energy_for_contraction(accumulateEnergy) v]
@preState(muscleContraction) AND greater(v, 5).
FORALL j, m, e, length j: jump@postState(muscleContraction) AND
j: OUTPUT@postState(muscleContraction) AND muscle
contraction[PROVIDES -> j]@postState(muscleContraction) AND
j[hasLength-> length] @postState(muscleContraction)
muscleContraction]@preState(muscleContraction) AND e:energy@
preState(muscleContraction) AND
e:RESOURCE@preState(muscleContraction) AND e[IS_CONSUMED_BY
-> muscleContraction] @preState(muscleContraction) AND
enough_energy_for_contraction(e)
@check_enough_energy_for_contraction(accumulateEnergy) AND
j:jump@update(muscleContraction) AND j[hasLength ->
length]@update(muscleContraction).
transitioncheck
update
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 4: Sample question
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At least, what amount of energy does a long jump athlete need to
consume in order to jump more than 8m long?
a. 100 cal b. 50 cal c. 250 cal d. 1 cal
energy1:energy[hasValue -> 100].\n FORALL j, oa, v >
oa]@ProcessModule AND j:Jump[hasValue -> v]@postState(oa) AND
greater(v, 8). √√
√
“In a long-distance jump competition, an athlete can jump only
after his mitochondria have accumulated enough energy for his
muscles to contract.”
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 4: Properties of the process models
• Sound and complete• Based on F-logic’s proof theory plus
additional proof for the
process formalism• A process action is sound ↔ its post state
can be deduced from its
pre state• A process action is complete ↔ it allows deducing all
the possible
clauses of its post state from the clauses in the pre state• A
process model is sound and complete ↔ all its actions are sound
and complete• Optimized
• Attribute and concept names ground• person(Peter) instead of
instanceOf(person, Peter)• Allows OntoBroker to index tuples by
class and attribute name
• Process rules are generally stratified• Critical in the
presence of negation (forks and loops)• Avoid costly well-founded
evaluation mode
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
25
Introduction and motivation
Open research problems and work hypotheses
Acquisition of process knowledge by SMEs
Provenance analysis of process executions by SMEs
Evaluation
Conclusions and future research problems
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Provenance analysis of process executions by SMEs
26
• Two main contributions• C5: A method and algorithm that uses
PSMs as high-level,
reusable process abstractions and visualization paradigm to
identify and explain the reasoning strategies and rationale of
executed processes
• C6: An architecture and integrated environment for the
analysis of process executions at the knowledge level
Objective 2: To support SMEs in analyzing and understanding
process executions
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 5: Towards knowledge provenance
27
• Provenance, from a knowledge perspective• How provenance
relates to the execution of a process• Simpler process analysis
proposing decompositions into
simpler subprocesses• Visualize provenance at different levels
of detail
• Supporting SMEs in two main ways• Validation of process
executions• Identification of reasoning patterns in process
executions
• PSMs as semantic overlays on top of existing process
documentation
• Task: What is going to be achieved by executing a process
• PSM: HOW
Source: myGrid
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 5: The twig join function
• Based on XML pattern matching algorithms on Directed Acyclic
Graphs (Bruno et al., 2002)
• twig_join detects the occurrence of a pattern in a XML DAG•
Given
• P, a process• T, a task potentially describing P• M, a PSM
providing a strategy on how to achieve T• i(T), the set of input
roles of T• o(T), the set of output roles of T• D, the DAG
resulting from documenting the execution of P
• twig_join(D,i(T),o(T)) checks whether a twig exists for M that
connects i(T) with o(T) in D
• In this case, PSM M is the pattern to be identified in the
process documentation DAG D
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 5: A twig join example
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PSM entities
Domain entities
Bridges (mapping)
twig join!
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 5: The matching algorithm
30
twig_join(Ti, D)
decompose(Ti)
twig_join(T11, D)
twig_join(T12, D)
twig_join(T13, D)
twig_join(T14, D)
• twig_join recursively appliedat each decomposition level
• Each task decomposed by one or several PSMs (task-method
decomposition view)
• Knowledge flow defines the sequence of evaluation
Backtrackingpossible at PSM and role levels
Interaction
Knowledge flow
Task-method decomposition
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 6: A Knowledge-Oriented Provenance Environment
31
PSM-Ontology bridges
Provenance query
Matching detection
Matching visualization
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
32
Introduction and motivation
Open research problems and work hypotheses
Acquisition of process knowledge by SMEs
Provenance analysis of process executions by SMEs
Evaluation
Conclusions and future research problems
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Objective 1
• Evaluation settings• 2 Chemistry SMEs, 2 Biology SMEs, and 2
Physics SMEs
• Syllabus• Chemistry: Stoichiometry, solutions and equilibrium
(Brown & Lemay,
pages 75-83, 113-133, and 613-653)• Biology: Cell and DNA
structure and processes (Campbell and Reece,
pages 112-124, 217-223, 239-245, 293-301, 304-311, and 317-319)•
Physics: Kinematics and Dynamics (Serway and Faughn, chapters
2,3,
and 4)
• Two main dimensions: usability and utility
33
Judith Lennart Christianne Martina Markus Andreas
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation results: utilization of the PSM library
34
Objective 1
# of
processes modeled
SME1 (Physics) 0 SME2 (Biology) 2 SME3 (Biology) 6
SME4 (Chemistry) 0
SME5 (Chemistry) 3
SME6 (Physics) 0 Total 11
Processes PSMs
SME2 (Biology) Transition from G2 phase to mitosis n.a.
Mitosis n.a.
SME3 (Biology)
Mitosis decompose & combine
Carbohydrate metabolism consume, transform
Cellular respiration decompose, consume
Detoxification transform
Photosynthesis form by combination
Ribosome protein synthesis situate & combine
SME5 (Chemistry)
Complete ionic equation form by combination
Molecular equation decompose & combine
Net ionic equation form by combination
H1: SME empowerment can increase KB quality and reduce costs
H3: PSMs can reduce the complexity of process KA
H6: The proposed methods and tools are flexible and reusable
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation results: performance of process models
35
with respect to configuration C0
Query C0 C1 C2
SME3-q0 31 1,00 0 0,00 16 0,52SME3-q1 63 1,00 16 0,25 16
0,25SME3-q2 31 1,00 16 0,52 16 0,52SME3-q3 47 1,00 16 0,34 16
0,34SME3-q4 15 1,00 0 0,00 0 0,00SME3-q5 32 1,00 16 0,50 0
0,00SME3-q6 203 1,00 219 1,08 234 1,15SME3-q7 63 1,00 31 0,49 31
0,49SME3-q8 47 1,00 31 0,66 16 0,34SME3-q9 62 1,00 32 0,52 16
0,26SME3-q10 203 1,00 218 1,07 203 1,00Average 79,7 1,00 59,5 0,75
56,4 0,71Median 47 1,00 16 0,34 16 0,34Min 15 1,00 0 0,00 0 0,00Max
203 1,00 219 1,08 234 1,151 - slower
H5: The proposed methods and tools produce sound and complete
executable process models
Objective 1
• C0• Well-founded evaluation on• Concept/attr. names ground
off
• C1• Well-founded evaluation on• Concept/attr. names ground
on
• C2• Well-founded evaluation off• Concept/attr. names ground
on
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation results: utility and usability
• Physics SMEs did not use processes• Not so important for
Chemistry SMEs• SME2 (Biology): “It makes the
representation of biological models easier”
• SME3 (Biology): “The modeling of processes is very useful. It
must be possible to ask questions about the various states of a
process. And asking questions with T&D worked okay”
36
• System Usability (SU) scale• SMEs answered a questionnaire
about
the system with a quantitative value ranging between 0 and
100
• Average obtained: 64,5
Objective 1
H2: Due to its complexity, SMEs require specific means for
process KA
H4: The method and tools proposed abstract SMEs from the
underlying KRR formalism
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation settings (Provenance Challenge)
37
Objective 2
37
Brain Atlas Provenance Data
Flow
Brain Atlas Workflow
Catalogation PSM
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation results
38
Objective 2
Perfect matchPartial matchNo match
• Focus on precision and recall metrics
• Identified at three different layered contexts• Method • Task
• Decomposition-level H7: PSMs can provide SMEs with
explanations
of process executions
H8: The method proposed identifies the main rationale behind
processes by detecting PSM occurrences
H9: PSMs describe process executions at different levels of
detail
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
39
Introduction and motivation
Open research problems and work hypotheses
Acquisition of process knowledge by SMEs
Provenance analysis of process executions by SMEs
Evaluation
Conclusions and future research problems
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Conclusions
• Qualitative evidence rather than statistical proof (only 6
SMEs)• However, evidence found that it is possible to engage users
in
acquiring process knowledge without the intervention of KEs•
SMEs using the PSM library (SME3 and SME5) produced more and
better quality process models (82%) than the rest (SME2)• The
method used to create the PSM library has also shown evidence
to be reusable in other domains
40
Objective 1: To enable SMEs to acquire processes without KEs
Objective 2: To support SMEs in understanding process
executions
• Semantic overlays e.g. PSMs on top of process
documentationprovide the required abstractions to analyze
provenance from a knowledge perspective
• Provenance analysis by SMEs favors from a hierarchical
structure in such overlays
• The matching algorithm has not been applied to large PSM
libraries and provenance logs
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
The ubiquity of processes
41
Biology
Healthcare
Climate prediction Ecology
Chemistry
Business
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Jose Manuel Gómez Pérez – Acquisition and Understanding of
Process Knowledge Using Problem Solving Methods, PhD thesis
Future research problems
• The Web is driving a new computing paradigm through the
involvement of users forming online communities
• Additionally, focus change from data to process• The solutions
proposed live in the Semantic Web in the small• Challenge: move to
the Web in the large
42
Process representation and reasoning
More expressivity (events, qualitative
reasoning)
Incomplete, inconsistent, contradictory
knowledge bases
Uncertainty, nonmonotonicity
Performance, coverage,
scaleDistributed reasoning algorithms
Conciliation of partial results
Heuristics (assumptions,
defaults)
Caching
Collaboration in user
communities
Share and reuse processes
Compare and recommend processes
Process-specific query mechanisms
Process validation, trust maintenance
Process reliability and validation
Trust
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PhD thesis Date: 14/07/2009
Acquisition and Understanding of Process Knowledge Using Problem
Solving Methods
Jose Manuel Gómez Pérez
Facultad de InformáticaUniversidad Politécnica de Madrid
Campus de Montegancedo sn28660 Boadilla del Monte, Madrid
http://www.oeg-upm.net
[email protected]: 34.91.3363670
Fax: 34.91.3524819
Acquisition and Understanding of Process Knowledge Using Problem
Solving MethodsOutlineKnowledge Acquisition: Towards SME
empowermentKnowledge typesWhy processes are importantWork
objectivesPSM perspectivesProvenance analysis of process
executionsIn summaryOutlineOpen research problems and work
hypotheses: Objective 1Open research problems and work hypotheses:
Objective 2OutlineAcquisition of process knowledge by
SMEsContribution 1: The process metamodelContribution 2: Building a
PSM library for acquisition of process knowledgeContribution 2: A
PSM example (decompose & combine)Contribution 3: The graphical
process modeling environmentContribution 4: The process
representation and reasoning formalismContribution 4: Addressing
the frame problemContribution 4: The Code synthesis
mechanismContribution 4: Domain-level reasoning within
processesContribution 4: Sample questionContribution 4: Properties
of the process modelsOutlineProvenance analysis of process
executions by SMEsContribution 5: Towards knowledge
provenanceContribution 5: The twig join functionContribution 5: A
twig join exampleContribution 5: The matching algorithmContribution
6: A Knowledge-Oriented Provenance EnvironmentOutlineObjective
1Evaluation results: utilization of the PSM libraryEvaluation
results: performance of process modelsEvaluation results: utility
and usability Evaluation settings (Provenance Challenge)Evaluation
resultsOutlineConclusionsThe ubiquity of processesFuture research
problemsAcquisition and Understanding of Process Knowledge Using
Problem Solving Methods