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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Outline
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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
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
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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
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.”
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Why processes are important
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• 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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Work objectives
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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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
PSM perspectives
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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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Provenance analysis of process executions
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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
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• 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
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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Open research problems and work hypotheses: Objective 1
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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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Open research problems and work hypotheses: Objective 2
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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
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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Acquisition of process knowledge by SMEs
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• 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
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
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.
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 3: The graphical process modeling environment
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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)
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)
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
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
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
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.”
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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Provenance analysis of process executions by SMEs
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• 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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 5: Towards knowledge provenance
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• 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
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!
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 5: The matching algorithm
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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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Contribution 6: A Knowledge-Oriented Provenance Environment
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PSM-Ontology bridges
Provenance query
Matching detection
Matching visualization
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
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
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Judith Lennart Christianne Martina Markus Andreas
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation results: utilization of the PSM library
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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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation results: performance of process models
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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
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”
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• 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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
Evaluation settings (Provenance Challenge)
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Objective 2
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Brain Atlas Provenance Data
Flow
Brain Atlas Workflow
Catalogation PSM
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
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
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
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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
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis
The ubiquity of processes
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Biology
Healthcare
Climate prediction Ecology
Chemistry
Business
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
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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
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
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