Ontology Summit March 31, 2016 Steve Ray, CMU · 3/31/2016 · Steve Ray, CMU 1 . IBM Research 2...
Transcript of Ontology Summit March 31, 2016 Steve Ray, CMU · 3/31/2016 · Steve Ray, CMU 1 . IBM Research 2...
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© 2013
Ontology Summit
March 31, 2016
Steve Ray, CMU
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CIM 61968
CIM 61970
CIM 62325
61850
ISO 16484
BACnet
61850-410
hydro
Multispeak
C12.19
Abstract Model – Shared Concepts, Fragments???
61850-420
DER
(solar…) 61400-25-2
wind 61850-420
DER
(solar…)
WS Calendar
NAESB
energy usage
info
EMIX
OASIS energy
interop
Zigbee smart
energy profile ISA88
ISA95
CEA 709
LonTalk
62351-7 comm net
and system mgnt
ICCP
IEEE 1815
dnp3
IEEE c37.239
comfede
naspi
ASHRAE
SPC201
FSGIM
OpenADR
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Testing the integrity and design of smart grid
standards (designed in UML)
–Transform to OWL
–Do integrity testing
–Facilitate harmonization with adjacent standards
Testing conformance to the standards by
implementations
–Build reference data sets
–Explain the values of the data sets
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Semantic representation
Web Ontology Language
(OWL)
Text
UML
XSD
Standards
Model Verification Testing
Model
Transformation
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Converts XMI to RDF triples. SXML
mapping reads the XML tree and
generates instances against OWL class
definitions for UML constructs.
Converts UML-based RDF triples to
OWL classes, properties and
instances.
Iterates over packages selected by
the user and generates an ontology
for each: Copies relevant triples
from the conversion; constructs
import triples to identify dependent
ontologies that were generated
from other packages or coming
from external sources.
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Class Checking
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Exhaustively searches a standard to find errors
that might escape human detection
– Orphan definitions (defined but never used)
– Opportunities for model refactoring (similar classes)
– Disallowed changes to imported standards
– Redundant classes and properties
– Non-standard data type definitions
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Model Healing
– Recommendations to correct errors
– Automatic error correction for native OWL specifications
Conformance Testing
– Does a particular implementation properly represent the information
according to the standard?
– Generation of reference data sets
Standards Harmonization
– Checking for missing information
– Information present in one standard but not in another
– Mapping among different ways of modeling the same information
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To test the integrity and design of smart grid
standards (designed in UML)
–Transform to OWL
–Do integrity testing
–Facilitate harmonization with adjacent standards
To test conformance to the standards by
implementations
–Build reference data sets
–Explain the values of the data sets
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Dataset #1 – System definition
Declare all the entities
– Circuits, ComponentElements, Routers
Declare ConnectionPoints, connect entities to Circuits
Specify RouterConnectionPoint properties
Define the EM hierarchy, and specify which entities are managed by
which EM
Specify Generator properties
Dataset #2 – Power readings
Provide all measured power and
energy data
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79 rules so far
1739 triples inferred (so far) for this power
aggregation reference data set
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ComponentElement
Device
EMUsagePoint
EMPresentData
EM
AllResourcesInEMDomain
BidirectionalCombiner
BidirectionalCombinerAggregationRuleset
EnergyRouter
ConnectionPoint
GridTransferSwitch
GridTransferSwitchAggregationRuleset
TransferSwitch
TransferSwitchAggregationRuleset
UnidirectionalCombiner
UnidirectionalCombinerAggregationRuleset
DispatchableGenerator
PowerMeasurementsSet
Generator
CurtailableLoad
FSGIMEventSignalType
Load ElectricMeter
RoleFlags
PowerApparentQuantity
PowerApparentType
PowerMeasurementRuleset
PowerReactiveQuantity
PowerRealQuantity PowerReactiveQuantityRuleset
PowerReactiveType
PowerRealQuantityRuleset
PowerRealType
PowerThermalQuantity
PowerThermalType
DemandAggregation
DemandRuleset AggregateDemand
AggregationProperties
Instance
creation
Subclass /
superclass
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Input
Data
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Missing
Data
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Reference data sets are useful in traditional
conformance testing exercises
Natural language rules are codified
Explanation mechanism can help implementers
understand how the rules within a standard should
function, using either reference datasets or their
own datasets
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Using semantic representations
– expose inconsistencies in new standards
– codify natural language rules
– use automated reasoning for instantiation and
explanation
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