UGent Research Projects on Linked Data in Architecture and Construction
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Transcript of UGent Research Projects on Linked Data in Architecture and Construction
UGent Research Projectson Linked Data
in Architecture and Construction
Presentation Technion Haifa
18 January 2017
Prof. Dr. Ir.-Arch. Pieter Pauwels
Ghent University, Department of Architecture and Urban Planning
UGent SmartLab
Ghent University
Faculty of Engineering and Architecture
Department of Architecture and Urban Planning
UGent SmartLab
Prof. Ronald De Meyer
Prof. Pieter Pauwels
Dr. Ruben Verstraeten
Dr. Tiemen Strobbe
Mathias Bonduel
Willem Bekers
Sebastiaan Leenknegt
Nino Heirbaut
3
Pieter Pauwels
• 2003-2008: Ba-Ma Civil Engineering - Architecture (UGent)
BIM
• 2008-2012: PhD Civil Engineering - Architecture (UGent)
BIM -> SemWeb
• 2012-2014: Postdoc University of Amsterdam (UvA)
• 2014-2017: Postdoc Ghent University
SemWeb + BIM
4
Current developments and commitments
- Linked Data in Architecture and Construction (LDAC) workshops• 2012: Ghent• 2014: Helsinki• 2015: Eindhoven• 2016: Dijon
- W3C Community Group on Linked Building Data (LBD)• BOT ontology• use cases that rely on combination of datasets
- linked data working group (LDWG) within BuildingSMART International• ifcOWL ontology
=> STANDARDISATION + APPROPRIATE USAGE OF STANDARDS
6
Outline
1. What is Linked Data? What are Semantic Web technologies?
2. The standards: buildingSMART and W3C
3. Research projects
7
The cool and awesome intro movies
https://vimeo.com/36752317
https://www.youtube.com/watch?v=4x_xzT5eF5Q
https://www.youtube.com/watch?v=OM6XIICm_qo8
Linked Open Data cloud (LOD)
http://tomheath.com/blog/2009/03/linked-data-web-of-data-semantic-web-wtf/9
• RDF stands for Resource Description Framework
• RDF is a standard data model for describing web resources– Note: ‘web resources’ can make statements about anything in the real
world: DBPedia, geography, building information, sensors, … anything goes
• RDF is designed to be read and understood by computers
• RDF is not designed for being displayed to people
• RDF is written in XML
• RDF is a W3C Recommendation
http://www.w3schools.com/webservices/ws_rdf_intro.asp
easily used
usually
-> standardisation
not a file format, not a syntax, not a schema, … => a data model
RDF??
10
RDF graphs are DIRECTED, LABELLED GRAPHS
RDF Graphs, what are they not?Hierarchies (cfr. XML)
Relational databases (cfr. SQL)
12
https://www.w3.org/DesignIssues/diagrams/sweb-stack/2006a.png
@prefix b: <http://www.today.net/building#> .
@prefix c: <http://www. today.net/city#> .
<http://www.today.net/today#building_1>
b:hasRoom <http://www. today.net/today#room_1> ;
b:hasName “Virtual Construction Lab";
c:partOfCity <http://cities.com/haifa> .
<http://cities.com/haifa>
c:inCountry <http://cities.com/israel> ;
c:hasName “Haifa” .
Example RDF graph
17
• URI stands for Uniform Resource Identifier
• Purpose: Obtain globally unique identifiers, so that information can be exchanged globally.
• Structure:
<http://www.today.net/today#building_1>Namespace Name
Uniform Resource Identifiers (URIs)
18
• distributed / decentralisedinformation management
• interactive information search and reasoning over the web
• sharing partial data
Main principles
21
Linked Open Data cloud (LOD)
http://tomheath.com/blog/2009/03/linked-data-web-of-data-semantic-web-wtf/
Ontologies
https://www.w3.org/DesignIssues/diagrams/sweb-stack/2006a.png
rvt:hasGirder
rvt:hasSlab
rvt:Corbel
rvt:Girder
rvt:Column
rvt:Slab
rvt:InternalBeam
COL_001
rdf:type
rvt:hasCorbel
rvt:hasGirder
rvt:hasSlab COR_001
GIR_001
COR_002
COL_002
rdf:type
rvt:Column rvt:Column
rdf:type rdf:type
rdf:type
rvt:Girder
rvt:Corbel rvt:Corbel
rvt:Slab
rvt:hasCorbel rvt:hasCorbel
rvt:hasGirder rvt:hasGirder
Basic schema of the ontology: Instance sample:
SLAB_1 SLAB_2 SLAB_3 SLAB_4 SLAB_5
rvt:hasSlab
rdf:type
rvt:hasInternalBeam
G. Costa and P. Pauwels. Building product suggestions for a BIM model based on rule sets and a semantic reasoning engine. Proceedings of the 32nd CIB W78 Conference on Information Technology in Construction 2015. pp 98-107.
BIM
GIS
BEMS
sensor
FM
no full integrationrather on-demand high-quality information exchange
regulations
29
Joining / combining initiatives
W3C LBD Community Group BuildingSMART Linked Data Working Group
linkedbuildingdata.net
www.w3.org/community/lbd/
ifcOWL
linkedbuildingdata people
LDAC event
bSDD
MVD
33
Outline
1. What is Linked Data? What are Semantic Web technologies?
2. The standards: buildingSMART and W3C
3. Research projects
37
BuildingSMART Standards Summit Jeju, Korea25 - 29 September 2016
ISO TC/59 Plenary WeekBerlin, Germany4 - 11 October 2016
CEN TC 442 WG meetingsBerlin, Germany12 - 13 September 2016
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buildingSMART standardisation strategy
bSI
S t a n d a r d i s a t i o n
ISO
CEN
National Standardshttp://buildingsmart.org/
Fit in BuildingSMART activities
http://www.buildingsmart.org/standards/technical-vision/technical-roadmaps/
SingaporeITM
October2015
RotterdamISM April2016
LDAC 2015Eindhoven
CIB W78 2015
Eindhoven
LDAC 2014Helsinki
SWIMingVoCamp
2016Dublin
LDAC 2016Madrid
Toronto ITM
October2014
WatfordITM March
2015
KoreaISM
September2016
SWIMingVoCamp
2016London
44
EXPRESSIFC-SPF
XSDXML
ifcOWLRDF
Pieter Pauwels and Walter Terkaj, EXPRESS to OWL for construction industry: towards a recommendable and usable ifcOWL ontology. Automation in Construction 63: 100-133 (2016).
conversion procedure EXPRESS schema to OWLIFC
Schema
Simple data type
Defined data type
Aggregation data typeSET data type --------
LIST & ARRAY data type --------
Constructed data typeSELECT data type --------
ENUMERATION data type --------
Entity data typeAttributes --------
Derive attrWHERE rules
FunctionsRules
ifcOWLOntology
owl:class + owl:DatatypeProperty restriction
owl:class
owl:class-------- non-functional owl:ObjectProperty-------- indirect subclass of express:List
owl:class-------- rdfs:subClassOf for owl:classes-------- rdf:type for owl:NamedIndividuals
owl:class-------- object properties
----
Pieter Pauwels and Walter Terkaj, EXPRESS to OWL for construction industry: towards a recommendable and usable ifcOWL ontology. Automation in Construction 63: 100-133 (2016).
ifcOWL ontologies available
Ifc2x_all_lf.expIFC2X2_ADD1.expIFC2X2_FINAL.exp
IFC2X2_PLATFORM.expIFC2X3_Final.expIFC2X3_TC1.exp
IFC4.expIFC4_ADD1.exp
not supportednot supportednot supportednot supportedIFC2X3_Final.owl / .ttlIFC2X3_TC1.owl / .ttlIFC4.owl / .ttlIFC4_ADD1.owl / .ttl
http://ifcowl.openbimstandards.org/IFC4_ADD1http://ifcowl.openbimstandards.org/IFC4
http://ifcowl.openbimstandards.org/IFC2X3_Finalhttp://ifcowl.openbimstandards.org/IFC2X3_TC1
52
InfrastructureRoom
Technical Room
Building Room
Product Room
RegulatoryRoom
BuildingSMART
BIMInfra GIS
IDMsMVDsBIM-
Guides
bSDD RulesifcOWL
54
55Jakob Beetz, Henk Schaap, Pieter Pauwels, and Jim Plume. Linked Data for Infrastructure.
Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
56Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog.
Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Image from: Lars Bjørkhaug. Integration of bSDD into the IfcDoc tool. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
IFC-SPF
EXPRESS
MVD subset
MVDxml
SimpleQueryAccess
GAP
SimpleBIM BIMSPARQL
Pieter Pauwels, Ana Roxin. SimpleBIM: from full ifcOWL graphs to simplified building graphs. Proceedings of the 11th ECPPM Conference, pp. 11-18, 2016, Limassol, Cyprus.
Chi Zhang and Jakob Beetz. Querying Linked Building Data Using SPARQL with Functional Extensions. Proceedings of the 11th ECPPM Conference, pp. 11-18, 2016, Limassol, Cyprus.
Outline
1. What is Linked Data? What are Semantic Web technologies?
2. The standards: buildingSMART and W3C
3. Research projects
1. Compliance checking
2. IFC to X3D to STL (and back)
3. Query and reasoning performance benchmark
4. SimpleBIM
5. Linked Data in Infra
60
Logics: overview
First Order Logic (FOL)
Second Order Logic (SOL)
Horn Logic
Datalog
Propositional Logic
Non-monotonic Logic (NML) Defeasible Reasoning
Monotonic Logic
Predicate Logic
Description Logic (DL)
subsets
N3
SWRL
Prolog
64
Monotonic vs. Non-monotonic logic
Non-monotonic Logic (NML) Defeasible Reasoning
Monotonic Logic
Retraction of inferences in the light of new information
Inferences are guaranteed, also when new information is added
65
Order, order!
First Order Logic (FOL)
Second Order Logic (FOL)
Propositional Logic
Variables quantify over individuals and relations
Variables quantifyover individuals
No variables or quantifiers
Predicate Logic
66
FOL subsets: tastes of logic
First Order Logic (FOL)
Horn Logic
Datalog
Predicate Logic
Description Logic (DL)
subsets
SWRL
N3
subsets
Prolog
OWL
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Korean Building Authority (KBA) regulations
• 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 exitTHENPREFIX 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" .
}
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Reasoning with the EYE and Stardog reasoner
inference engine
OWL ontologies
query
User
RDF Repository
interface
IF-THEN rule repository
response in RDF graph
EYE reasoningengine
N3 OWLRDF
SPARQL
RDF / CSV
English
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
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" .
} .
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Serialisations of RDF graphs
https://www.w3.org/DesignIssues/diagrams/n3/venn
Rule-checking scenario
• 2 repositories
• Facts1.ttl + ont.ttl + rs1.ttl
• Facts2.ttl + ont.ttl + rs1.ttl
• SPARQL queries addressing the properties being impacted by the rules in the rule set (rs1.ttl)
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 1
Query 1:
Output facts1:
Output facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 2
Query 2:
Output facts1 and facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 3
Query 3:
Output facts1:
Output facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 4
Query 4:
Output facts1:
Output facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 5
Query 5:
Output facts1 and facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 6
Query 6:
Output facts1:
Output facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 7
Query 7:
Output facts1:
Output facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
Inference: rule 7
Query 7:
Output facts1:
Output facts2:
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
2/5: IFC TO X3D TO STL (andback)Pieter Pauwels, Davy Van Deursen, Jos De Roo, Tim Van Ackere, Ronald De Meyer, Rik Van de Walle and Jan Van Campenhout
Ghent University
3/5: QUERY AND REASONING PERFORMANCE BENCHMARKPieter Pauwels, Tarcisio Mendes de Farias, Chi Zhang, Ana Roxin, Jakob Beetz, Jos De Roo, Christophe Nicolle
99
Performance benchmark variables
Schema (TBox)
• ifcOWL
Instances (ABox)
• 369 ifcOWL-compliantbuilding models
Rules (RBox)
• 68 data transformation rules
101
• Implemented based on the open source APIs of Topbraid SPIN (SPIN API 1.4.0) and Apache Jena (Jena Core 2.11.0, Jena ARQ 2.11.0, Jena TDB 1.0.0)
• Rules are written with TopbraidComposer Free version, and they are exported as RDF Turtle files.
• A small Java program is implemented to read RDF models, schema, rules from the TDB store and query data.
• All the SPARQL queries are configured using the Jena org.apache.jena.sparql.algebrapackage
• To avoid unnecessary reasoning processes, in this test environment only the RDFS vocabulary is supported.
SPIN + Jena TDB
• Version ‘EYE-Winter16.0302.1557’ (‘SWI-Prolog 7.2.3 (amd64): Aug 25 2015, 12:24:59’).
• EYE is a semi-backward reasoner enhanced with Euler path detection.
• As our rule set currently contains only rules using =>, forward reasoning will take place.
• Each command is executed 5 times
• Each command includes the full ontology, the full set of rules and the RDFS vocabulary, as well as one of the 369 building model files and one of the 3 query files.
• No triple store is used: triples are processed directly from the considered files.
EYE
• 4.0.2 Stardog semantic graph database (Java 8, RDF 1.1 graph data model, OWL2 profiles, SPARQL 1.1)
• OWL reasoner + rule engine.
• Support of SWRL rules, backward-chaining reasoning
• Reasoning is performed by applying a query rewriting approach (SWRL rules are taken into account during the query rewriting process).
• Stardog allows attaining a DL-expressivity level of SROIQ(D).
• In this approach, SWRL rules are taken into account during the query rewriting process.
Stardog
102
Queries
• We have built a limited list of 60 queries, each of which triggers at least one of the available rules.
• As we focus here on query execution performance, the considered queries are entirely based on the right-hand sides of the considered rules.
• 3 queries: Query Query Contents
Q1 ?obj sbd:hasProperty ?p
Q2?point sbd:hasCoordinateX ?x .?point sbd:hasCoordinateY ?y .?point sbd:hasCoordinateZ ?z
Q3 ?d rdf:type sbd:ExternalWall
103
Results• Queries applied on 6 hand-picked
building models of varying size
• In the SPIN approach• For Q1 and Q2, the execution time =
backward-chaining inference process + actual query execution time
• For Q3, execution time = query execution time itself
• In the EYE approach• Networking time is ignored
• In the Stardog approach• Execution time = backward-chaining
inference + actual query execution time
QueryBuildingModel
SPIN (s)
EYE (s)
Stardog (s)
Q1(simple,
littleresults)
BM1 135,36 37,11 13,44
BM2 1,47 0,29 0,17
BM3 24,01 4,87 1,4
BM4 41,28 12,95 3,55
BM5 4,99 1,05 0,33
BM6 0,55 0,16 0,08
Q2(simple,
manyresults)
BM1 46,17 2,10 6,82
BM2 92,03 4,20 15,83
BM3 82,68 4,12 15,28
BM4 19,93 1,04 2,81
BM5 3,69 0,21 1,36
BM6 0,74 0,045 1,00
Q3(complex)
BM1 0,001 0,001 0,07
BM2 0,006 0,003 0,12
BM3 0,002 0,003 0,31
BM4 0,005 0,001 0,20
BM5 0,006 0,013 0,20
BM6 0,001 0,001 0,13
104
Query time related to result count• For Q1 for each of the considered
approaches
• (green = SPIN; blue = EYE; black = Stardog)
• For Q2 for each of the considered approaches
• (green = SPIN; blue = EYE; black = Stardog)
105
Findings
Impact on performance from many factors, in order of impact:
1. Indexing algorithms, query rewriting techniques, and rule handling strategies
2. Forward- versus backward-chaining
3. Type of data in the building model
4. Storage in the triple store
5. Number of output results
106
Pieter Pauwels, Ana Roxin. SimpleBIM: from full ifcOWL graphs to simplified building graphs. Proceedings of the 11th European Conference on Product and Process Modelling. p.11-18.
T. Liebich. buildingSMART Data Standards. BuildingSMART International Summit 2012.
ISO 29481
ISO 16739
IFC, MVDs and IDM
IFC-SPF
EXPRESS
MVD subset
MVDxml
SimpleQueryAccess
GAP
SimpleBIM BIMSPARQL
Pieter Pauwels, Ana Roxin. SimpleBIM: from full ifcOWL graphs to simplified building graphs. Proceedings of the 11th European Conference on Product and Process Modelling. p.11-18.
Chi Zhang, Jakob Beetz. Querying Linked Building Data Using SPARQL with Functional Extensions. Proceedings of the 11th European Conference on Product and Process Modelling.
115
inst:IfcWindow_1893 inst:IfcWindow_1842
inst:IfcWallStandardCase_696
simplebim:hasWindow simplebim:hasWindow
Statistics of the test file
• File size: 767kB
• Triple count: 10,173 distinct
• Class instances: 4222 (5535)• 233 / 4222 ifcowl:IfcRelationships• 686 / 4222 list:OWLList• 417 / 686 ifcowl:IfcLengthMeasure_List• 764 / 4222 expr:STRING
117
Simplification strategy
118
1• Removing geometric information
2• Unwrapping data types
3• Rewriting properties
4• IfcRelationship instances
Results (1)
125
1. Removal of geometric information
• 10,173 triples to 6,927 triples
• 767kb to 476kb
• 31% (file size) – 38% (triple count)
2. Unwrapping data types
• 3,897 triples
• 279kb
• 41% (file size) – 44% (triple count)
Results (2)
126
3. Rewriting properties
• 1,630 triples
• 112kb
• 58% (file size) – 59% (triple count)
4. IfcRelationship instances
• 1,339 triples
• 83kb
• 18% (file size) – 26% (triple count)
Results (3)
127
Model File size Triple count
ifcOWL simpleBIM ifcOWL simpleBIM
1 767kb 83kb 10 173 1 339
2 16,7MB 1029kb 225 135 16 836
Average reduction of 91,58% Average reduction of 89%
REDUCTION TO:8,5% of file size10,3% of triple count
Jakob Beetz, GIS / BIM interoperabiliteit: STUMICO presentatie April 2014. http://www.slideshare.net/JakobBeetz/gis-bim-interoperabiliteit-stumico-presentatie-april-2014
Jakob Beetz, GIS / BIM interoperabiliteit: STUMICO presentatie April 2014. http://www.slideshare.net/JakobBeetz/gis-bim-interoperabiliteit-stumico-presentatie-april-2014
Jakob Beetz, Michelle Lindlar, Stefan Dietze, Ujwal Gadiraju, Dag Field Edvardsen, Lars Bjørkhaug, OntologicalFramework for a Semantic Digital Archive. DuraArk Deliverable D3.3.2.
132
Outline
1. What is Linked Data? What are Semantic Web technologies?
2. The standards: buildingSMART and W3C
3. Research projects
1. Compliance checking
2. IFC to X3D to STL (and back)
3. Query and reasoning performance benchmark
4. SimpleBIM
5. Linked Data in Infra
134