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Pieter Pauwels
Ana Roxin
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Agenda
Scenario
EYE/N3 approach
STARDOG approach
Results
Further generalisation
2
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SCENARIO
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Input files
◼ 2 IFC files converted into ifcOWL (Turtle format)
� facts1.ttl
� facts2.ttl
◼ Additional statements� @prefix add: <http://www.additionalelements.org/> .
� inst:IfcSpace_1021 add:isConnectedToStair inst:IfcStair_593 .
� inst:IfcSpace_983 add:isConnectedToStair inst:IfcStair_593 .
� inst:IfcSpace_1021 add:isConnectedToStair inst:IfcStair_613 .
� inst:IfcSpace_983 add:isConnectedToStair inst:IfcStair_613 .
◼ An ensemble of 8 rules (IF -> THEN)
4
But, this can
also be
handled
through the
proper
elements in
IFC
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Example Rule – R#7
• A stair is connected to an object having an exit to ground floor
• The distance from the stair to the exit is not greater than 30000IF
• The stair is a valid exitTHEN
5
PREFIX kba: <http://koreanbuildingcode.org/KR-BA-34-01/>PREFIX math: <http://www.w3.org/2000/10/swap/math#>PREFIX add: <http://www.additionalelements.org/>
IF {?s add:isConnectedToStair ?obj .?obj kba:hasExitOnGroundFloor "true" .?s kba:hasEscapeDistanceToStaircase ?value .?value math:notGreaterThan 30000 .
}
THEN {?s kba:isValid "true" .
}
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THE EYE/N3 APPROACH
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The N3/EYE Approach
7
inference engine
OWL ontologies
query
User
RDF Repository
interface
IF-THEN rule repository
response in
RDF graph
EYE reasoning
engine
N3 OWLRDF
SPARQL
RDF / CSV
English
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Rules in EYE/N3
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RDF graphLog:implies(IF THEN)
N3Logic
@prefix kba: <http://koreanbuildingcode.org/KR-BA-34-01/> .@prefix add: <http://www.additionalelements.org/> .@prefix math: <http://www.w3.org/2000/10/swap/math#> .
{?s add:isConnectedToStair ?obj .?obj kba:hasExitOnGroundFloor "true" .?s kba:hasEscapeDistanceToStaircase ?value .?value math:notGreaterThan 30000 .
}=>{?s kba:isValid "true" .
} .
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Serialisation formats for the RDF data model
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Source: https://www.w3.org/DesignIssues/diagrams/n3/venn
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THE STARDOG APPROACH
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Stardog
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Source: http://stardog.com/
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Scenario implementation on Stardog
Adapting rules
• Stardog Rule Syntax
Creating the repositories
• 1 per IFC file
• Rules are added on top of each repository
Querying the data
• Inferences are made on query execution
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Stardog Rule Syntax
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SPARQL "basic graph
patterns" (BGPs)
explicit new bits (IF-THEN)
Stardog RuleSyntax
PREFIX rule: <tag:stardog:api:rule:>[] a rule:SPARQLRule ;
rule:content """PREFIX kba: <http://koreanbuildingcode.org/KR-BA-34-01/>PREFIX add: <http://www.additionalelements.org/>
IF {?s add:isConnectedToStair ?obj .?obj kba:hasExitOnGroundFloor "true" .?s kba:hasEscapeDistanceToStaircase ?value .FILTER (?value <= 30000) .
}
THEN {?s kba:isValid "true" .
}""" .
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Scenario
◼ 2 repositories
� Facts1.ttl + rs1.ttl
� Facts2.ttl + rs1.ttl
◼ SPARQL queries addressing the properties being
impacted by the rules in the rule set (rs1.ttl)
� As Stardog performs reasoning at query time
◼ Web interface for
� Uploading files
� Addressing SPARQL queries
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RESULTS
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Inferences – validating R#1
◼ Query 1
◼ Output Facts1.ttl
◼ Output Facts2.ttl
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Inferences – Validating R#2
◼ Query 2
◼ Output (same for Facts1.ttl & Facts2.ttl)
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Inferences – Validating R#3
◼ Query 3
◼ Output Facts1.ttl
◼ Output Facts2.ttl
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Inferences – Validating R#4
◼ Query 4
◼ Output Facts1.ttl
◼ Output Facts2.ttl
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Inferences – Validating R#5
◼ Query 5
◼ Output (same for Facts1.ttl & Facts2.ttl)
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Inferences – Validating R#6
◼ Query 6
◼ Output Facts1.ttl
◼ Output Facts2.ttl
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R#7 and R#8 – Facts1.ttl
◼
◼
◼
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R#7 and R#8 – Facts2.ttl
◼
◼
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FURTHER GENERALISATION
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Candidate languages
RASE Ifc Constraint
Model Procedural
Coding SWRL
SPARQL and SPIN
DROOLS Legal Doc ML
and Legal Rule ML
BIMRL
Rule Tables JenaRules NLP and Prolog
Commercial Rule Engines (e.g. Stardog)
BERA N3logic
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Structure ????
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SWRL
N3Logic
StarDog
Prolog
SPARQL/SPIN
BERA
RASEIfcConstraint
Procedural
coding
DROOLS
RuleML
BIMRL
Rule
Tables
Jena
Rules
RIF
Natural Language
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Language ‘families’
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SWRLN3Logic StarDog
Prolog SPARQL/SPIN
BERA
RASE
IfcConstraint
Procedural
coding
DROOLS
LegalRuleML
BIMRL
Rule
Tables
Jena RIF
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Cascading the families
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SWRLN3Logic StarDog
Prolog SPARQL/SPIN
BERA
RASE IfcConstraint
Procedural
coding
DROOLS
LegalRuleML
BIMRL
Jena RIF
Natural Language
IMPLEMENTATION
MARKUP
STANDARD
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Pieter Pauwels
Ana Roxin
Thank you for your attention !
![Armand Ajdari arXiv:physics/0611232v1 [physics.chem-ph] 23 ...ΦHS= −n0 ln(1−n3) + n1n2 −nV1nV2 1−n3 + n3 2 24π(1−n3)2 1− n2 V2 n2 2 3 where each of n0,n1,n2,n3,nV1,and](https://img.pdfslide.us/doc/110x75/5f62effb7ca26d52dd2728c9/armand-ajdari-arxivphysics0611232v1-23-hs-an0-ln1an3-n1n2-anv1nv2.jpg)
