An Ontology-underpinned Decision-Support System for Wastewater management

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



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



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)

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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

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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

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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






WaWO’s Diagnostics

WaWO

Reasoning integration

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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

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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)

June 26-27, 2002 17

Outline



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

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Evaluation results: CBRS and RBES

Focus on the most representative problematic situation: bulking sludge

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OntoWEDSS evaluation

Average successful outcomes: 65%

Average successful outcomes: 88%

June 26-27, 2002 21

Outline



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

…

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…

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…

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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

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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.

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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

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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%

Education

An Ontology-underpinned Decision-Support System for Wastewater management