an ontology-underpinned decision-support system for wastewater management
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OntoWEDSS
An Ontology-underpinned Decision-Support System for Wastewater management
by Luigi Ceccaroni, Ulises Cortés and Miquel Sànchez-Marrè
June 26-27, 2002 2
Outline
Motivating tasks Background information The OntoWEDSS decision-support
system with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 3
Motivating tasks
Improvement of the modeling of the information about the wastewater treatment process and of wastewater management
Solution of complex problems related to wastewater using ontologies
Integration of ontologies in the reasoning of decision support systems
June 26-27, 2002 4
Outline
Motivating tasks Background information The OntoWEDSS decision-support
system with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 5
Ontologies: definition
An ontology is a formal and explicit specification of a shared conceptualization, which is readable by a computer.
An ontology describes the shared model of a domain. Everybody following a particular ontology understands all the categories and the relations comprised in that ontology and behave accordingly.
June 26-27, 2002 6
PLANNING / PREDICTION/SUPERVISION
AIMODELS
STATISTICALMODELS
NUMERICALMODELS
GIS(SPATIAL DATA)
DATA BASE(TEMPORAL DATA)
USER INTERFACE
Background / SubjectiveKnowledge
ECONOMICCOSTS
USER
Decision / Actuation
ENVIRONMENTAL/ HEALTH
REGULATIONS
Spatial /Geographical
data
On-line data
Off-line data
DATA MININGKNOWLEDGE ACQUISITION/LEARNING
EXPLANATION ALTERNATIVES EVAL.
REASONING / MODELS’ INTEGRATION
BIOLOGICAL/ CHEMICAL / PHYSICALANALYSES
SENSORS
ON-LINE / OFF-LINE
ACTUATORS
Feedback
ENVIRONMENTAL SYSTEM / PROCESS
D
EC
ISIO
N S
UP
PO
RT
DA
TA
IN
TE
RP
RE
TA
TIO
ND
IAG
NO
SIS
Environmental decision-support systems
June 26-27, 2002 7
Outline
Motivating tasks Background information The OntoWEDSS decision-support
system with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 8
OntoWEDSS: profile (1)
Use of ontologies in domain modeling and clarification of existing terminological confusion in wastewater domain
Automatic, reliable discovery and management of problematic states in real-world domains
Composition, interoperation and reuse of different reasoning systems (rule-based, case-based and ontology-based)
June 26-27, 2002 9
Environmental process supervision and management distributed in 3 layers: perception, diagnosis and decision support
Incorporation of wastewater microbiological knowledge into the reasoning process and representation of cause-effect relations
Resolution of existing reasoning-impasses
OntoWEDSS: profile (2)
June 26-27, 2002 10
Per
cep
tion
Rea
soni
ng
OntoWEDSS: process model
Dia
gnos
isD
ecis
ion
sup
port
Wastewater treatment plant
Sensors
Biological, chemical andphysical analyses
On-line data
Off-line data
Numerical-control module
WaWO ontology
Reasoning integration
On-line and off-lineactuators
Rule-based reasoningCase-based reasoning
Impasse
Impasse resolution
SupervisionWaRP
Data baseCalculated data
User
User interface
Background knowledge
Decision and actuationA
ctio
n
June 26-27, 2002 11
WaWO- Frame-based representation- Hierarchy used for:
QueriesLanguage analysisReasoning
- Standard but specialized:Storm is an
Operational-ProblemBacterium is a
Wastewater-Biological--Living–Object
- Metazoan represented:NematodeRotifer
June 26-27, 2002 12
Reasoning with ontologies
Role or Phenomenon categories
Occurrents
Relations
Filamentous-Bacteria-
Excessive-Proliferation
Microthrix-Parvicella
Filamentous-Dominant-
AT
Micro-fauna
Filamentous-Bacteria
subClassOf
isEffector
hasResult
Bulking-Sludge-
Filamentous
Dominant-Filamentous
-Bacteria
Bulking-Solution
Specific-Bulking-Solution
Non-Specific-Bulking-Solution
Add-Chemicals-To-Increase-Sludge-Flocs-
Weight
Eliminate-All-Filamentous-
Bacteria
Bulking-Sludge-Consequences-
Avoidance
Bulking-Sludge
...
WWTP-Operational-
State
subClassOf
subClassOf
subClassOf
isEffector
hasResult
subClassOf
subClassOf
subClassOf
subClassOf
subClassOf
subClassOf
subClassOf
June 26-27, 2002 13
Supervision Supervision modulemodule
RBES
Does RBES’s
diagnostics exist?
CBRS
CBRS’s inference
RBES’s inference
No
Yes
No
No
Yes
No
Does CBRS’s
diagnostics exist?
RBES’s Diagnostics
=CBRS’s
Diagnostics?
Yes
CBRS’s > constant ?
Yes
Does CBRS’s
diagnostics exist?
No
CBRS’s Diagnostics
Yes
RBES’s Diagnostics
CBRS’s Diagnostics
RBES’s Diagnostics
CBRS’s Diagnostics
RBES’s Diagnostics
WaWO’s Diagnostics
WaWO
Reasoning integration
June 26-27, 2002 14
Functionalities
Input (modeling and execution) List of descriptors to use Weight of descriptors (optional) New-problem’s descriptors values
Output (execution) Diagnosis of the current state of the WWTP
(with reliability factor) Trace of the reasoning List of actions to take according to the current
situation
June 26-27, 2002 15
Interface for data exchange
June 26-27, 2002 16
Action suggestion
Change Sludge-Recirculation-External to 120 Destruction of filaments via chlorine addition Addition of inorganic coagulant Check out Food-To-Micro-Organism-Ratio Remove aeration-tank and clarifier foam Reduce waste-activated-sludge flow rate
(FlowRate-WAS)
June 26-27, 2002 17
Outline
Motivating tasks Background information The OntoWEDSS decision-support
system with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 18
Database description
Initial set: 790 days with 21 quantitative and qualitative descriptors (out of 170)
Filters: missing values, labels Final set for CBRS training: 186 days Bulking-Sludge labeled: 29 days (16%)
Lack of benchmarks High number of descriptors
Multiple labelsPro
blem
s
June 26-27, 2002 19
Evaluation results: CBRS and RBES
Focus on the most representative problematic situation: bulking sludge
June 26-27, 2002 20
OntoWEDSS evaluation
Average successful outcomes: 65%
Average successful outcomes: 88%
June 26-27, 2002 21
Outline
Motivating tasks Background information The OntoWEDSS decision-support
system with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 22
Conclusions
Research tool to explore the possibilities and the potential of introducing ontologies into decision support systems, using an environmental domain as case study
Creation of an ontology for the domain of wastewater treatment process
Ontological representation of two kinds of cause-effect relations: micro-organisms problematic situations state of the plant suggested actions
June 26-27, 2002 23
Perspectives
Further refinement and update of current AI modules
Simulation and prediction of the evolution of a treatment plant’s state
Integration of the ontology with some temporal reasoning
Reasoning with variations/transitions of descriptors’ values
…
June 26-27, 2002 25
…
June 26-27, 2002 26
…
June 26-27, 2002 27
Axioms
Example of causality axiom: Physical entities may causally affect other
physical entities Different views of the same entity may be
described with different words, definitions and axioms.
Each category in the hierarchy inherits all the properties and axioms of every category above it.
June 26-27, 2002 28
Ontologies: languages
KIF: meta-format for knowledge interchange
Ontolingua: KIF-based; object-oriented using a Frame Ontology; Web interface (on-line collaboration); translation to various languages; large repository
RDFS: resources as Web addresses; primitives for classes and properties
OIL: RDFS-based; entirely Web-driven; combination of frame-based modeling and description logic
DAML+OIL: designed for Web-agents; richer modeling primitives (e.g., properties with cardinality)
June 26-27, 2002 29
Decision-support systems
User friendliness Assistance in problem formulation Framework for information capture Specific KBs Integration of different AI systems
(RBES and CBRS, generally) Generation of different strategies
June 26-27, 2002 30
Rule-based expert system
These systems express regularities as rules. They typically follow a situation-action paradigm: the set of rules let them directly suggest what action to take in a given situation.
The domain is so complex that causes other than the given action may also contribute to a resulting situation.
June 26-27, 2002 31
Case-based reasoning system
These systems express regularities and singularities as cases, each of which encodes some effects of an action under a specific situation. They also follow a situation-action paradigm: the adaptation of the actions taken in previous similar situations let them suggest about the current actions to take.
June 26-27, 2002 32
The chicken-and-egg paradox in modeling and diagnosis
The situations (set of descriptors’ values) cannot be defined without first knowing what diagnostics they correspond to.
And most diagnostics can be hard to define as such, until the corresponding situations have been identified.
Expert often have to use trial-and-error methods.
Set of descriptor values
Diagnostics
DIAGNOSIS
Situation modeling
June 26-27, 2002 33
Functional parameters
Activation cycle 1 hour (5 min in case of detected emergency)
Accuracy (based on focused evaluation)
Cost Allegro Common LISP
Experiment Number of data
Correct classification
G-1
G-2
G-3
8
10
11
100%
90%
70%
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