An Ontology-underpinned Decision-Support System for Wastewater managementPresentation Transcript
OntoWEDSSAn Ontology-underpinnedDecision-Support Systemfor Wastewater managementby Luigi Ceccaroni, Ulises Cortés and Miquel Sànchez-Marrè
June 26-27, 2002 2Outline Motivating tasks Background information The OntoWEDSS decision-supportsystem with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 3Motivating tasks Improvement of the modeling of theinformation about the wastewatertreatment process and of wastewatermanagement Solution of complex problems relatedto wastewater using ontologies Integration of ontologies in thereasoning of decision support systems
June 26-27, 2002 4Outline Motivating tasks Background information The OntoWEDSS decision-supportsystem with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 5Ontologies: definition An ontology is a formal and explicitspecification of a shared conceptualization,which is readable by a computer. An ontology describes the shared model ofa domain. Everybody following a particularontology understands all the categories andthe relations comprised in that ontology andbehave accordingly.
June 26-27, 2002 6PLANNING / PREDICTION/SUPERVISIONAIMODELSSTATISTICALMODELSNUMERICALMODELSGIS(SPATIAL DATA)DATA BASE(TEMPORAL DATA)USER INTERFACEBackground/ SubjectiveKnowledgeECONOMICCOSTSUSERDecision/ ActuationENVIRONMENTAL/ HEALTHREGULATIONSSpatial /GeographicaldataOn-linedataOff-line dataDATA MININGKNOWLEDGE ACQUISITION/LEARNINGEXPLANATION ALTERNATIVESEVAL.REASONING / MODELS’ INTEGRATIONBIOLOGICAL/ CHEMICAL/ PHYSICALANALYSESSENSORSON-LINE /OFF-LINEACTUATORSFeedbackENVIRONMENTAL SYSTEM / PROCESSDECISIONSUPPORTDATAINTERPRETATIONDIAGNOSISEnvironmental decision-support systems
June 26-27, 2002 7Outline Motivating tasks Background information The OntoWEDSS decision-supportsystem with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 8OntoWEDSS: profile (1) Use of ontologies in domain modeling andclarification of existing terminologicalconfusion in wastewater domain Automatic, reliable discovery andmanagement of problematic states in real-world domains Composition, interoperation and reuse ofdifferent reasoning systems (rule-based,case-based and ontology-based)
June 26-27, 2002 9 Environmental process supervision andmanagement distributed in 3 layers:perception, diagnosis and decision support Incorporation of wastewater microbiologicalknowledge into the reasoning process andrepresentation of cause-effect relations Resolution of existing reasoning-impassesOntoWEDSS: profile (2)
June 26-27, 2002 10
June 26-27, 2002 11WaWO- Frame-based representation- Hierarchy used for:QueriesLanguage analysisReasoning- Standard but specialized:Storm is anOperational-ProblemBacterium is aWastewater-Biological--Living–Object- Metazoan represented:NematodeRotifer
June 26-27, 2002 12Reasoningwithontologies Role orPhenomenoncategories Occurrents Relations
June 26-27, 2002 13SupervisionSupervisionmodulemoduleRBESDoesRBES’sdiagnosticsexist?CBRSCBRS’sinferenceRBES’sinferenceNoYesNoNoYesNoDoesCBRS’sdiagnosticsexist?RBES’sDiagnostics=CBRS’sDiagnostics?YesCBRS’s >constant β ?YesDoesCBRS’sdiagnosticsexist?NoCBRS’sDiagnosticsYesRBES’sDiagnosticsCBRS’s DiagnosticsRBES’s DiagnosticsCBRS’sDiagnosticsRBES’sDiagnosticsWaWO’sDiagnosticsWaWOReasoningintegration
June 26-27, 2002 14Functionalities 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 currentsituation
June 26-27, 2002 15Interface for data exchange
June 26-27, 2002 16Action 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 17Outline Motivating tasks Background information The OntoWEDSS decision-supportsystem with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 18Database description Initial set: 790 days with 21 quantitative andqualitative 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 labelsProblems
June 26-27, 2002 19Evaluation results: CBRS and RBES Focus on themostrepresentativeproblematicsituation:bulkingsludge
June 26-27, 2002 20OntoWEDSS evaluation Averagesuccessfuloutcomes:65% Averagesuccessfuloutcomes:88%
June 26-27, 2002 21Outline Motivating tasks Background information The OntoWEDSS decision-supportsystem with the WaWO ontology Results Conclusions and perspectives
June 26-27, 2002 22Conclusions Research tool to explore the possibilities andthe potential of introducing ontologies intodecision support systems, using anenvironmental domain as case study Creation of an ontology for the domain ofwastewater treatment process Ontological representation of two kinds ofcause-effect relations: micro-organisms ↔ problematic situations state of the plant ↔ suggested actions
June 26-27, 2002 23Perspectives Further refinement and update ofcurrent AI modules Simulation and prediction of theevolution of a treatment plant’s state Integration of the ontology with sometemporal reasoning Reasoning with variations/transitionsof descriptors’ values
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June 26-27, 2002 27Axioms Example of causality axiom: Physical entities may causally affect otherphysical entities Different views of the same entity may bedescribed with different words, definitionsand axioms. Each category in the hierarchy inherits allthe properties and axioms of every categoryabove it.
June 26-27, 2002 28Ontologies: languages KIF: meta-format for knowledge interchange Ontolingua: KIF-based; object-oriented using aFrame Ontology; Web interface (on-line collaboration);translation to various languages; large repository RDFS: resources as Web addresses; primitives forclasses and properties OIL: RDFS-based; entirely Web-driven; combinationof frame-based modeling and description logic DAML+OIL: designed for Web-agents; richermodeling primitives (e.g., properties with cardinality)
June 26-27, 2002 29Decision-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 30Rule-based expert system These systems express regularities asrules. They typically follow a situation-action paradigm: the set of rules letthem directly suggest what action totake in a given situation. The domain is so complex that causesother than the given action may alsocontribute to a resulting situation.
June 26-27, 2002 31Case-based reasoning system These systems express regularitiesand singularities as cases, each ofwhich encodes some effects of anaction under a specific situation. Theyalso follow a situation-action paradigm:the adaptation of the actions taken inprevious similar situations let themsuggest about the current actions totake.
June 26-27, 2002 32The chicken-and-egg paradoxin modeling and diagnosis The situations (set of descriptors’ values)cannot be defined without first knowing whatdiagnostics they correspond to. And most diagnostics can be hard to defineas such, until the corresponding situationshave been identified. Expert often have to use trial-and-errormethods.Set ofdescriptor valuesDiagnosticsDIAGNOSISSituationmodeling
June 26-27, 2002 33Functional parameters Activation cycle 1 hour (5 min in case of detected emergency) Accuracy (based on focused evaluation) Cost Allegro Common LISPExperiment Numberof dataCorrectclassificationG-1G-2G-381011100%90%70%