Acquisition and Understanding of Process Knowledge Using...

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

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


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


Knowledge types

4

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.”


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


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


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


Provenance analysis of process executions

8

?


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


Outline

10





Evaluation



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


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


Outline

13





Evaluation




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


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

15


Contribution 2: Building a PSM library for acquisition of process knowledge

16

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


Contribution 2: A PSM example (decompose & combine)

17

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.


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)


Contribution 4: The process representation and reasoning formalism

19

• 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)


Contribution 4: Addressing the frame problem

20

• 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


Contribution 4: The Code synthesis mechanism

input actions intermediate actionsoutput actions

setup rules x - -

transition rules x x x

precedence rules - x x

21

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


Contribution 4: Domain-level reasoning within processes

22

“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


Contribution 4: Sample question

23

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.”


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

24


Outline

25





Evaluation




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


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


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

28


Contribution 5: A twig join example

29

PSM entities

Domain entities

Bridges (mapping)

twig join!


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


Contribution 6: A Knowledge-Oriented Provenance Environment

31

PSM-Ontology bridges

Provenance query

Matching detection

Matching visualization


Outline

32





Evaluation



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


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


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


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


Evaluation settings (Provenance Challenge)

37

Objective 2

37

Brain Atlas Provenance Data

Flow

Brain Atlas Workflow

Catalogation PSM


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


Outline

39





Evaluation



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


The ubiquity of processes

41

Biology

Healthcare

Climate prediction Ecology

Chemistry

Business


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

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

Acquisition and Understanding of Process Knowledge Using...

Documents

Transcript of Acquisition and Understanding of Process Knowledge Using...