![Contribution 2: A PSM example (decompose & combine)
“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.
name decompose & combine
goal member(Recombination set, Element) and
member(Constituents set, Piece) and
part-of(Piece, Element) and
part-of(Piece, Combination) and
properties(Element, ep) and
properties(Combination, cp) and
not equal(ep, cp)
actions decompose, combine
input action decompose
output action combine
input roles Recombination set, Decomposer, Combinator
output roles Combination, Byproduct
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 17](https://image.slidesharecdn.com/acquisitionandunderstandingofprocessknowledgev1-1-100630104937-phpapp01/85/Acquisition-And-Understanding-Of-Process-Knowledgev1-1-17-320.jpg)
![Contribution 4: The Code synthesis mechanism
• Action-centric algorithm
• Each action results into a set
of process rules in F-logic
intermediate output
input actions
actions actions
setup rules x - -
transition
x x x
rules setup FORALL m, e, v
m:mitochondrion@preState(accumulateEnergy) AND
precedence m:TOOL@preState(accumulateEnergy) AND
- x x
rules e: energy@preState(accumulateEnergy) AND
transition e:RESOURCE@preState(accumulateEnergy) AND
e[hasEnergyValue -> v]@preState(accumulateEnergy)
FORALL m, e, j <-
j: jump@postState(muscleContraction) AND m:mitochondrion AND
j: OUTPUT@postState(muscleContraction) AND e:energy AND
muscleContraction[PROVIDES -> j] @postState(muscleContraction) e[hasEnergyValue -> v].
<.- precedence
m:muscle @preState(muscleContraction) AND FORALL e, v
m:TOOL@preState(muscleContraction) AND e:energy@preState(muscleContraction) AND
m[IS_USED_BY -> muscleContraction]@preState(muscleContraction) AND e[hasEnergyValue -> v]@ preState(muscleContraction)
e:energy@ preState(muscleContraction) AND <.-
e:RESOURCE@preState(muscleContraction) AND e:energy@postState(accumulateEnergy) AND
e[IS_CONSUMED_BY -> muscleContraction] @preState(muscleContraction). e[hasEnergyValue -> v]@ postState(accumulateEnergy).
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 21](https://image.slidesharecdn.com/acquisitionandunderstandingofprocessknowledgev1-1-100630104937-phpapp01/85/Acquisition-And-Understanding-Of-Process-Knowledgev1-1-21-320.jpg)
![Contribution 4: Domain-level reasoning within processes
“The minimum amount
of energy needed to
jump are 5 calories”
“The length of the jump
is directly proportional
to the amount of
energy accumulated”
transition
check
FORALL j, m, e, length
FORALL anEnergy, v
j: jump@postState(muscleContraction) AND
enough_energy_for_contraction(anEnergy)
j: OUTPUT@postState(muscleContraction) AND
@check_enough_energy_for_contraction(accumulateEnergy)
muscle contraction[PROVIDES -> j]@postState(muscleContraction) AND
<-
j[hasLength-> length] @postState(muscleContraction)
anEnergy:energy @preState(muscleContraction) AND
<.-
anEnergy:energy[hasEnergyValue -> v] @preState(muscleContraction) AND
m:muscle @preState(muscleContraction) AND
greater(v, 5).
m:TOOL@preState(muscleContraction) AND
update m[IS_USED_BY -> muscleContraction]@preState(muscleContraction) AND
FORALL length, anEnergy, v e:energy@ preState(muscleContraction) AND
aJump(out(hasLength, length):jump@update(muscleContraction) e:RESOURCE@preState(muscleContraction) AND
aJump(out(hasLength, length) [hasLength -> length]@update(muscleContraction) e[IS_CONSUMED_BY -> muscleContraction] @preState(muscleContraction) AND
<- enough_energy_for_contraction(e)
anEnergy:energy @preState(muscleContraction) AND @check_enough_energy_for_contraction(accumulateEnergy) AND
anEnergy:energy[hasEnergyValue -> v] @preState(muscleContraction) AND j:jump@update(muscleContraction) AND
multiply(length, 2, v). j[hasLength -> length]@update(muscleContraction).
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 22](https://image.slidesharecdn.com/acquisitionandunderstandingofprocessknowledgev1-1-100630104937-phpapp01/85/Acquisition-And-Understanding-Of-Process-Knowledgev1-1-22-320.jpg)
![Contribution 4: Sample question
“In a long-distance jump competition, an
athlete can jump only after his mitochondria
have accumulated enough energy for his
muscles to contract.”
At least, what amount of energy does a long jump energy1:energy[hasValue -> 100].n FORALL j, oa, v <-
athlete need to consume in order to jump more than “long jump”:PROCESS@ProcessModule AND
8m long? “long jump”[OUTPUT_ACTION ->> oa]@ProcessModule AND
j:Jump[hasValue -> v]@postState(oa) AND
√ a. 100 cal greater(v, 8).
√ b. 50 cal
√ c. 250 cal
d. 1 cal
Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 23](https://image.slidesharecdn.com/acquisitionandunderstandingofprocessknowledgev1-1-100630104937-phpapp01/85/Acquisition-And-Understanding-Of-Process-Knowledgev1-1-23-320.jpg)
Acquisition And Understanding Of Process Knowledgev1 1
- 1. Acquisition and Understanding of Process Knowledge Using Problem Solving Methods Jose Manuel Gómez Pérez Facultad de Informática Universidad Politécnica de Madrid Campus de Montegancedo sn 28660 Boadilla del Monte, Madrid http://www.oeg-upm.net jmgomez@fi.upm.es Phone: 34.91.3363670 Fax: 34.91.3524819 PhD thesis Date: 08/05/2009
- 2. Outline 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 2
- 3. Knowledge Acquisition: Towards SME empowerment DARPA’s KA Frameworks HPKB & RKF The Knowledge The Role Acquisition Bottleneck Differentiation Principle The Knowledge Level KA by Subject Matter Experts DARPA’s KSE KA by (SMEs) Subject Matter Expert (SME) Knowledge Engineers KB edition by SMES (KEs) KRAKEN Knowledge Knowledge DISCIPL-RKF formulation modeling KRR Languages by SMEs KB OCML maintenance Ontologies Ontology editors KARL SHAKEN CHIMAERA Collaborative Knowledge Engineer (KE) Knowledge knowledge creation programming Problem Solving SEMANTIC WIKIS Methods Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 3
- 4. Knowledge types Source: the Halo project KR analysis phase for the domains of Chemistry, Biology, and Physics • Processes are special knowledge RUL CLS FACT MAT types that (classification) (factual (mathematics) (inference) knowledge) • Relate to the sequence of operations and involved CMP TAB PCS CAUS (comparison) (tables) (processes) (cause-effect) resources leading to the production of some outcome DAT PROC EXP US • Encapsulate preconditions, (data structures) (procedural) (experiments) (underspecified) results, contents, actors, and TRANS causes NF SPACE PWR (translation) (non functional) (spatial) (part-whole) • Process knowledge is complex • It builds on top of other simpler TIME GRA (temporal) (diagrammatic) knowledge types, like facts and rules “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 4
- 5. Why processes are important • 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 5
- 6. Work objectives Objective 1: To enable SMEs to Provide reusable guidelines formulate processes without KEs to formulate process How knowledge Objective 2: To support SMEs in Support reasoning understanding process executions Describe the main rationale behind a process What PSMs Whom PCS SMEs Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 6
- 7. PSM perspectives Task-method Interaction decomposition Black-box perspective Knowledge transformation within the PSM Hierarchically defines how PSM establishes and controls tasks decompose into simpler the sequence of actions (sub)tasks required to perform a task Describes tasks at several Defines knowledge required at levels of detail each task step Provides alternative ways to achieve a task Knowledge flow Task Method Role Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 7
- 8. Provenance analysis of process executions ? Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 8
- 9. 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 • 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 9
- 10. Outline 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 10
- 11. Open research problems and work hypotheses: Objective 1 • H1: Empowering SMEs can increase KB Objective 1: To provide SMEs with the means to formulate process knowledge in quality and reduce costs their domains of expertise without the intervention of KEs • 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 11
- 12. Open research problems and work hypotheses: Objective 2 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 12
- 13. Outline 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 13
- 14. Acquisition of process knowledge by SMEs Objective 1: To provide SMEs with the means to formulate process knowledge in their domains of • Four main contributions expertise without the intervention of KEs • 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 • 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 14
- 15. Contribution 1: The process metamodel • Resources (roles) • Actions • Containers of domain conceptsthat • Inspired by activities in EO and TOVE can play a particular role • Relations • Forks Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 15
- 16. Contribution 2: Building a PSM library for acquisition of process knowledge Identification Decomposition and abstraction Extends work done in the Halo analysis phase by Omniscience and Ontoprise teams Reusable PSM library • 755 AP questions analyzed • >100 domain-specific processes • Four main process categories • Join • Split • Modify • Locate Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 16
- 17. Contribution 2: A PSM example (decompose & combine) “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. name decompose & combine goal member(Recombination set, Element) and member(Constituents set, Piece) and part-of(Piece, Element) and part-of(Piece, Combination) and properties(Element, ep) and properties(Combination, cp) and not equal(ep, cp) actions decompose, combine input action decompose output action combine input roles Recombination set, Decomposer, Combinator output roles Combination, Byproduct Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 17
- 18. Contribution 3: The graphical process modeling environment Domain process to which this process diagram is Consistency bound maintenance Process (knowledge base and metamodel process data flow) PSM library (e.g. decompose & recombine) Domain-level reasoning and control flow evaluation Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 18
- 19. Contribution 4: The process representation and reasoning formalism • Bridges the gap between the knowledge level and the operational level • Focus on three main aspects • Process frame • Data flow • Control flow Input action Conditional Output precedence action (control flow) “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 19
- 20. Contribution 4: Addressing the frame problem Pre state of action • Two states (pre and post) per Dissolve process action • Pre state: portion of the process frame in the scope of an action Post state of • Post state: updated pre state of action the action after its the execution Dissolve • 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 Explicit manipulation of process the process frame • Precedence rules: describe what actions can be allows runtime management of data connected with each other by means of their outputs and and control flow inputs • Transition rules: describe the transition between pre and post states Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 20
- 21. Contribution 4: The Code synthesis mechanism • Action-centric algorithm • Each action results into a set of process rules in F-logic intermediate output input actions actions actions setup rules x - - transition x x x rules setup FORALL m, e, v m:mitochondrion@preState(accumulateEnergy) AND precedence m:TOOL@preState(accumulateEnergy) AND - x x rules e: energy@preState(accumulateEnergy) AND transition e:RESOURCE@preState(accumulateEnergy) AND e[hasEnergyValue -> v]@preState(accumulateEnergy) FORALL m, e, j <- j: jump@postState(muscleContraction) AND m:mitochondrion AND j: OUTPUT@postState(muscleContraction) AND e:energy AND muscleContraction[PROVIDES -> j] @postState(muscleContraction) e[hasEnergyValue -> v]. <.- precedence m:muscle @preState(muscleContraction) AND FORALL e, v m:TOOL@preState(muscleContraction) AND e:energy@preState(muscleContraction) AND m[IS_USED_BY -> muscleContraction]@preState(muscleContraction) AND e[hasEnergyValue -> v]@ preState(muscleContraction) e:energy@ preState(muscleContraction) AND <.- e:RESOURCE@preState(muscleContraction) AND e:energy@postState(accumulateEnergy) AND e[IS_CONSUMED_BY -> muscleContraction] @preState(muscleContraction). e[hasEnergyValue -> v]@ postState(accumulateEnergy). Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 21
- 22. Contribution 4: Domain-level reasoning within processes “The minimum amount of energy needed to jump are 5 calories” “The length of the jump is directly proportional to the amount of energy accumulated” transition check FORALL j, m, e, length FORALL anEnergy, v j: jump@postState(muscleContraction) AND enough_energy_for_contraction(anEnergy) j: OUTPUT@postState(muscleContraction) AND @check_enough_energy_for_contraction(accumulateEnergy) muscle contraction[PROVIDES -> j]@postState(muscleContraction) AND <- j[hasLength-> length] @postState(muscleContraction) anEnergy:energy @preState(muscleContraction) AND <.- anEnergy:energy[hasEnergyValue -> v] @preState(muscleContraction) AND m:muscle @preState(muscleContraction) AND greater(v, 5). m:TOOL@preState(muscleContraction) AND update m[IS_USED_BY -> muscleContraction]@preState(muscleContraction) AND FORALL length, anEnergy, v e:energy@ preState(muscleContraction) AND aJump(out(hasLength, length):jump@update(muscleContraction) e:RESOURCE@preState(muscleContraction) AND aJump(out(hasLength, length) [hasLength -> length]@update(muscleContraction) e[IS_CONSUMED_BY -> muscleContraction] @preState(muscleContraction) AND <- enough_energy_for_contraction(e) anEnergy:energy @preState(muscleContraction) AND @check_enough_energy_for_contraction(accumulateEnergy) AND anEnergy:energy[hasEnergyValue -> v] @preState(muscleContraction) AND j:jump@update(muscleContraction) AND multiply(length, 2, v). j[hasLength -> length]@update(muscleContraction). Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 22
- 23. Contribution 4: Sample question “In a long-distance jump competition, an athlete can jump only after his mitochondria have accumulated enough energy for his muscles to contract.” At least, what amount of energy does a long jump energy1:energy[hasValue -> 100].n FORALL j, oa, v <- athlete need to consume in order to jump more than “long jump”:PROCESS@ProcessModule AND 8m long? “long jump”[OUTPUT_ACTION ->> oa]@ProcessModule AND j:Jump[hasValue -> v]@postState(oa) AND √ a. 100 cal greater(v, 8). √ b. 50 cal √ c. 250 cal d. 1 cal Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 23
- 24. 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 Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 24
- 25. Outline 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 25
- 26. Provenance analysis of process executions by SMEs Objective 2: To support SMEs in analyzing and understanding process executions • 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 Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 26
- 27. Contribution 5: Towards knowledge provenance • PSMs as semantic overlays on top of existing process documentation • Task: What is going to be achieved by executing a process • PSM: HOW • 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 Source: myGrid executions Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 27
- 28. 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 Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 28
- 29. Contribution 5: A twig join example Domain Bridges PSM entities (mapping) entities twig join! Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 29
- 30. Contribution 5: The matching algorithm • twig_join recursively applied Task-method decomposition at each decomposition level • Each task decomposed by one or several PSMs (task- twig_join(Ti, D) method decomposition view) • Knowledge flow defines the sequence of evaluation decompose(Ti) twig_join(T11, D) Knowledge flow twig_join(T12, D) twig_join(T13, D) Backtracking possible at PSM and role levels twig_join(T14, D) Interaction Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 30
- 31. Contribution 6: A Knowledge-Oriented Provenance Environment PSM- Matching Ontology visualization bridges Provenance Matching query detection Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 31
- 32. Outline 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 32
- 33. Objective 1 • Evaluation settings • 2 Chemistry SMEs, 2 Biology SMEs, and 2 Physics SMEs Judith Lennart Christianne Martina Markus Andreas • 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 Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 33
- 34. Evaluation results: utilization of the PSM library Objective 1 # of processes modeled SME1 (Physics) 0 H3: PSMs can reduce the H1: SME empowerment SME2 (Biology) 2 complexity of process KA can increase KB quality SME3 (Biology) 6 and reduce costs H6: The proposed SME4 (Chemistry) 0 methods and tools are flexible and reusable SME5 (Chemistry) 3 SME6 (Physics) 0 Total 11 Processes PSMs Transition from G2 phase to mitosis n.a. SME2 (Biology) Mitosis n.a. Mitosis decompose & combine Carbohydrate metabolism consume, transform Cellular respiration decompose, consume SME3 (Biology) Detoxification transform Photosynthesis form by combination Ribosome protein synthesis situate & combine Complete ionic equation form by combination SME5 (Chemistry) Molecular equation decompose & combine Net ionic equation form by combination Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 34
- 35. Evaluation results: performance of process models Objective 1 with respect to configuration C0 H5: The proposed Query C0 C1 C2 methods and tools produce sound and SME3-q0 31 1,00 0 0,00 16 0,52 complete executable SME3-q1 63 1,00 16 0,25 16 0,25 process models SME3-q2 31 1,00 16 0,52 16 0,52 SME3-q3 47 1,00 16 0,34 16 0,34 SME3-q4 15 1,00 0 0,00 0 0,00 SME3-q5 32 1,00 16 0,50 0 0,00 SME3-q6 203 1,00 219 1,08 234 1,15 SME3-q7 63 1,00 31 0,49 31 0,49 • C0 SME3-q8 47 1,00 31 0,66 16 0,34 • Well-founded evaluation on SME3-q9 62 1,00 32 0,52 16 0,26 SME3-q10 203 1,00 218 1,07 203 1,00 • Concept/attr. names ground off Average 79,7 1,00 59,5 56,4 Median 47 1,00 16 0,75 0,34 16 0,71 0,34 • C1 Min 15 1,00 0 0,00 0 0,00 • Well-founded evaluation on Max 203 1,00 219 1,08 234 1,15 <1 - faster • Concept/attr. names ground on =1 - same time as config C0 >1 - slower • 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 35
- 36. Evaluation results: utility and usability Objective 1 • Physics SMEs did not use processes • System Usability (SU) scale • Not so important for Chemistry SMEs • SMEs answered a questionnaire about the system with a quantitative value • SME2 (Biology): “It makes the ranging between 0 and 100 representation of biological models easier” • Average obtained: 64,5 • SME3 (Biology): “The modeling of H2: Due to its H4: The method and processes is very useful. It must be complexity, SMEs tools proposed possible to ask questions about the require specific abstract SMEs from various states of a process. And asking means for process the underlying KRR questions with T&D worked okay” KA formalism Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 36
- 37. Evaluation settings (Provenance Challenge) Objective 2 Brain Atlas Brain Atlas Workflow Provenance Data Flow Catalogation PSM 37 Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 37
- 38. Evaluation results Objective 2 • Focus on precision and recall metrics • Identified at three different layered contexts • Method • Task H7: PSMs can provide SMEs with explanations • Decomposition-level 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 Perfect match Partial match No match Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 38
- 39. Outline 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 39
- 40. Conclusions Objective 1: To enable SMEs to acquire processes without KEs • 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 Objective 2: To support SMEs in understanding process executions • Semantic overlays e.g. PSMs on top of process documentation provide 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 40
- 41. The ubiquity of processes Business Biology Healthcare Climate Ecology prediction Chemistry Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 41
- 42. 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 Process Performance, Collaboration Process representation coverage, in user validation, trust and reasoning scale communities maintenance More expressivity Distributed (events, qualitative reasoning Share and reuse reasoning) algorithms processes Process reliability and validation Incomplete, Conciliation of partial results Compare and inconsistent, recommend contradictory Heuristics processes knowledge bases (assumptions, defaults) Trust Uncertainty, Process-specific nonmonotonicity Caching query mechanisms Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 42
- 43. Acquisition and Understanding of Process Knowledge Using Problem Solving Methods Jose Manuel Gómez Pérez Facultad de Informática Universidad Politécnica de Madrid Campus de Montegancedo sn 28660 Boadilla del Monte, Madrid http://www.oeg-upm.net jmgomez@fi.upm.es Phone: 34.91.3363670 Fax: 34.91.3524819 PhD thesis Date: 08/05/2009
- 44. Previous steps towards provenance analysis • PASOA (Groth et al., 2008) process documentation • Relationship p-assertions • Who exchanges messages • Interaction p-assertions • How input data becomes output data • Actor-state p-assertions PASOA interaction p-assertion • Non-functional properties Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 44
- 45. Contribution 2: PSMs for the acquisition of process knowledge • PSMs separate process from domain knowledge by explicitly representing the different roles domain entities play in a process • Focus on how to achieve processes • Provide SMEs with guidelines and templates to formulate process models Modelling framework for process knowledge, extending TMDA Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 45
- 46. Contribution 2: The PSM library for Process Modelling Reusable across the three domains Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 46
- 47. The Halo Project • “Project Halo aims to create an intelligent computer system able to answer novel questions in scientific domains with expertise equivalent to Advanced Placement (AP )competence level” • Three scientific domains: Chemistry, Biology, and Physics • Focus on tools that allow Subject Matter Experts (SMEs) to author scientific knowledge The elongation of the leading strand during DNA synthesis: The primer that initiates the synthesis of a new DNA strand is usually: a. produces Okazaki fragments b. depends on the action of DNA polymerase a. RNA c. does not require a template strand b. DNA c. an Okazaki fragment d. a structural protein Which part of the animal cell is required only in the first stage e. a thymine dimer of mitosis and what is the name of such stage? a. chromatin and prophase b. chromatid and prometaphase c. centromere and anaphase d. plasma membrane and telophase Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 47
- 48. Validating process models: The testing & debugging perspective Test set Query Test results Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 48
- 49. The process representation and reasoning formalism • Bridges the gap between the knowledge level and the operational level • Three main aspects to address • Process frame • On what portion of the knowledge base can a process action have effect? • How can the effects of actions on the knowledge base be propagated to successive actions? • Data flow: path followed by the data throughout a process • Control flow: run-time evaluation and enactment of the actual flow of data Data flow Data flow Input action Conditional Output action precedence (control flow) Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 49
- 50. Addressing the frame problem • 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 Post state of action Dissolve Pre state of action Dissolve • We automatically synthesize rules at modeling time that create Explicit manipulation of these modules upon process execution the process frame allows runtime • Setup rules: build the pre state of the input actions of the process management of data • Precedence rules: describe what actions can be connected with and control flow each other by means of their outputs and inputs • Transition rules: describe the transition between pre and post states Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 50
- 51. Correlation between the process metamodel and the formalism Process metamodel Process representation and reasoning formalism Action Process rule 1 1..* Setup rule Precedence rule Atomic action Iterative action Transition rule Domain rule 1 * Update rule Check rule Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 51
- 52. F-logic as process representation and reasoning language • Process diagrams authored by SMEs are automatically translated into F-logic (OntoBroker) code • Some key properties • Module system (encoding the process frame; pre and post states) • Negation: (control flow evaluation) • Evaluation methods: dynamic filtering (Kifer and Loziinski, 1986) and well- founded evaluation (vanGelder et a., 1991) • Performing and scalable, as shown by OpenRuleBench (Liang et al., 2009) • Functional advantages • Enabling a single entry point for reasoning across the whole system • Domain-level reasoning within processes, through rules • Keeping introspective properties for reasoning with meta-level information, like subprocesses and intermediate results • Support for expressing and reasoning with processes , allowing inference of process outcomes, grounded in the particular domains of application • The synthesized code implements data and control flow within processes Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 52
- 53. Evaluation results: utilization of process metamodel Objective 1 # of # of Processes # of relations # of forks Total resources actions Transition from G2 phase to 12 21 1 3 37 SME2 mitosis (Biology) Mitosis 34 37 0 5 76 Mitosis 29 11 0 5 45 Carbohydrate 5 6 0 2 13 metabolism Cellular SME3 5 7 0 2 14 respiration (Biology) Detoxification 4 4 0 1 9 Photosynthesis 6 6 0 1 13 Ribosome protein 2 2 0 1 5 synthesis Complete ionic 7 7 0 1 15 equation SME5 Molecular 4 4 0 1 9 (Chemistry) equation Net ionic 3 3 0 1 7 equation Total 111 108 1 23 243 Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 53
- 54. Contribution 6: A hierarchical visualization paradigm Task-method decomposition view Perfect match Partial match No match Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 54
- 55. Future research problems • The Web is driving a new computing paradigm through the involvement of users forming online communities • Migrating from data to process • The solutions proposed live in the Semantic Web in the small • Challenge: move to the Web in the large • Process representation and reasoning • More expressivity required (events, qualitative) • Incomplete, inconsistent, even contradictory knowledge bases • Uncertainty, nonmonotonicity • Performance, coverage, and scale • Distributed reasoning algorithms, conciliation of partial results, heuristics, caching, well-founded assumptions, defaults • Collaboration in communities of users • Share, merge, compare, and recommend process models • Process-specific query mechanisms • Web-scale process validation and trust maintenance • Process reliability, trust and validation Jose Manuel Gómez Pérez – Acquisition and Understanding of Process Knowledge Using Problem Solving Methods, PhD thesis 55