1. OntoWEDSS
An Ontology-underpinned
Decision-Support System
for Wastewater management
by Luigi Ceccaroni, Ulises Cortés and Miquel Sànchez-Marrè

2. June 26-27, 2002 2
Outline
 Motivating tasks
 Background information
 The OntoWEDSS decision-support
system with the WaWO ontology
 Results
 Conclusions and perspectives

3. 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

4. June 26-27, 2002 4
Outline
 Motivating tasks
 Background information
 The OntoWEDSS decision-support
system with the WaWO ontology
 Results
 Conclusions and perspectives

5. 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.

6. June 26-27, 2002 6
PLANNING / PREDICTION/SUPERVISION
AI
MODELS
STATISTICAL
MODELS
NUMERICAL
MODELS
GIS
(SPATIAL DATA)
DATA BASE
(TEMPORAL DATA)
USER INTERFACE
Background
/ Subjective
Knowledge
ECONOMIC
COSTS
USER
Decision
/ Actuation
ENVIRONMENTAL
/ HEALTH
REGULATIONS
Spatial /
Geographical
data
On-line
data
Off-line data
DATA MINING
KNOWLEDGE ACQUISITION/LEARNING
EXPLANATION ALTERNATIVES
EVAL.
REASONING / MODELS’ INTEGRATION
BIOLOGICAL
/ CHEMICAL
/ PHYSICAL
ANALYSES
SENSORS
ON-LINE /
OFF-LINE
ACTUATORS
Feedback
ENVIRONMENTAL SYSTEM / PROCESS
DECISIONSUPPORTDATAINTERPRETATION
DIAGNOSIS
Environmental decision-support systems

7. June 26-27, 2002 7
Outline
 Motivating tasks
 Background information
 The OntoWEDSS decision-support
system with the WaWO ontology
 Results
 Conclusions and perspectives

8. 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)

9. 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)

10. June 26-27, 2002 10

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WaWO
- Frame-based representation
- Hierarchy used for:
Queries
Language analysis
Reasoning
- Standard but specialized:
Storm is an
Operational-Problem
Bacterium is a
Wastewater-Biological-
-Living–Object
- Metazoan represented:
Nematode
Rotifer

12. June 26-27, 2002 12
Reasoning
with
ontologies
 Role or
Phenomenon
categories
 Occurrents
 Relations

13. June 26-27, 2002 13
SupervisionSupervision
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 β ?
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

14. 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

15. June 26-27, 2002 15
Interface for data exchange

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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)

17. June 26-27, 2002 17
Outline
 Motivating tasks
 Background information
 The OntoWEDSS decision-support
system with the WaWO ontology
 Results
 Conclusions and perspectives

18. 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 labels
Problems

19. June 26-27, 2002 19
Evaluation results: CBRS and RBES
 Focus on the
most
representative
problematic
situation:
bulking
sludge

20. June 26-27, 2002 20
OntoWEDSS evaluation
 Average
successful
outcomes:
65%
 Average
successful
outcomes:
88%

21. June 26-27, 2002 21
Outline
 Motivating tasks
 Background information
 The OntoWEDSS decision-support
system with the WaWO ontology
 Results
 Conclusions and perspectives

22. 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

23. 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

24. …

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…

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…

27. 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.

28. 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)

29. 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

30. 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.

31. 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.

32. 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

33. 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%