UGent Research Projects on Linked Data in Architecture and Construction

UGent Research Projects
on 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_qo
8

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

LABELLED
DIRECTED
Triple
RDF Graphs, what are they?
11

RDF graphs are
DIRECTED, LABELLED
GRAPHS
RDF Graphs, what are they not?
Hierarchies (cfr. XML)
Relational databases (cfr. SQL)
12

RDF Data Model
predicate
subject object
13

Connecting Triples
SUBJECT OBJECT
PREDICATE
OBJECT
PREDICATE
OBJECT
PREDICATE
OBJECTPREDICATE
14

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

URI
URI
URI
URI
URI
URI
URI
URI
URI
URI
URI
19

MyBuilding Cities
Data integration over the web is now possible
20

• distributed / decentralised
information 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 integration
rather on-demand high-quality information exchange
regulations
29

Bring BIM into the Semantic Web
BIM
30

http://www.buildingsmart-tech.org/
future/linked-data/
31

LDAC 2015
LDAC 2014
LDAC 2012
32

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
37

Standardisation bodies
CEN/TC 442
ISO TC59
Linked Data WG
OpenBIMGuides WG
BuildingSMART Benelux
38

BuildingSMART Standards Summit Jeju,
Korea
25 - 29 September 2016
ISO TC/59 Plenary Week
Berlin, Germany
4 - 11 October 2016
CEN TC 442 WG meetings
Berlin, Germany
12 - 13 September 2016 39

buildingSMART standardisation strategy
bSI
S t a n d a r d i s a t i o n
ISO
CEN
National
Standards
http://buildingsmart.org/

The buildingSMART triangle
http://buildingsmart.org/

Fit in BuildingSMART activities
http://www.buildingsmart.org/standards/technical-vision/technical-roadmaps/

Singapore
ITM
October
2015
Rotterdam
ISM
April
2016
LDAC 2015
Eindhoven
CIB W78
2015
Eindhoven
LDAC 2014
Helsinki
SWIMing
VoCamp
2016
Dublin
LDAC 2016
Madrid
Toronto
ITM
October
2014
Watford
ITM March
2015
Korea
ISM
September
2016
SWIMing
VoCamp
2016
London
44

Image courtesy: Jakob Beetz, TU Eindhoven
IFC
INFRA
SENSOR
GIS
45

Aims:
1. ifcOWL ontology
2. align with buildingSMART efforts
3. LD-oriented support
46

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 OWL
IFC
Schema
Simple data type
Defined data type
Aggregation data type
SET data type --------
LIST & ARRAY data type --------
Constructed data type
SELECT data type --------
ENUMERATION data type --------
Entity data type
Attributes --------
Derive attr
WHERE rules
Functions
Rules
ifcOWL
Ontology
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.exp
IFC2X2_ADD1.exp
IFC2X2_FINAL.exp
IFC2X2_PLATFORM.exp
IFC2X3_Final.exp
IFC2X3_TC1.exp
IFC4.exp
IFC4_ADD1.exp
not supported
not supported
not supported
not supported
IFC2X3_Final.owl / .ttl
IFC2X3_TC1.owl / .ttl
IFC4.owl / .ttl
IFC4_ADD1.owl / .ttl
http://ifcowl.openbimstandards.org/IFC4_ADD1
http://ifcowl.openbimstandards.org/IFC4
http://ifcowl.openbimstandards.org/IFC2X3_Final
http://ifcowl.openbimstandards.org/IFC2X3_TC1
52

Infrastructure
Room
Technical
Room
Building
Room
Product
Room
Regulatory
Room
BuildingSMART
BIM
Infra
GIS
IDMs
MVDs
BIM-
Guides
bSDD RulesifcOWL
54

55
Jakob Beetz, Henk Schaap, Pieter Pauwels, and Jim Plume. Linked Data for Infrastructure.
Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.

56
Ana Roxin, Pieter Pauwels. Reasoning with rules - Applications to (1) N3/EYE and (2) Stardog.

Image from: Lars Bjørkhaug. Integration of bSDD into the IfcDoc tool.

IFC-SPF
EXPRESS
MVD
subset
MVDxml
Simple
Query
Access
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. 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!
Second Order Logic (FOL)
Propositional Logic
Variables quantify over
individuals and relations
Variables quantify
over individuals
No variables or
quantifiers
Predicate Logic
66

FOL subsets: tastes of logic
Horn Logic
Datalog
Predicate Logic
Description Logic (DL)
subsets
SWRL
N3
subsets
Prolog
OWL
67

1/5: COMPLIANCE CHECKING
Pieter Pauwels, Ghent University
Ana Roxin, Université de Bourgogne

Abox – Tbox – Rbox
ABox
TBox
RBox
Instances
Ontology
IF-THEN rules
69

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 exit
THEN
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" .
}

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 reasoning
engine
N3 OWLRDF
SPARQL
RDF / CSV
English

RDF graph
Log: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" .
} .

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)

Inference: rule 1
Query 1:
Output
facts1:
Output
facts2:

Inference: rule 2
Query 2:
Output facts1
and facts2:

Inference: rule 3
Query 3:
Output
facts1:
Output
facts2:

Inference: rule 4
Query 4:
Output
facts1:
Output
facts2:

Inference: rule 5
Query 5:
Output facts1
and facts2:

Inference: rule 6
Query 6:
Output
facts1:
Output
facts2:

Inference: rule 7
Query 7:
Output
facts1:
Output
facts2:

2/5: IFC TO X3D TO STL (and
back)
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 BENCHMARK
Pieter 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-
compliant
building 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 Topbraid
Composer 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.algebra
package
• 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
Query
Building
Model
SPIN
(s)
EYE
(s)
Stardog
(s)
Q1
(simple,
little
results)
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,
many
results)
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

4/5: SIMPLEBIM
107
Pieter Pauwels, Ghent University
Ana Roxin, Université de Bourgogne

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
Simple
Query
Access
GAP
SimpleBIM BIMSPARQL
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.

RDFIFC-SPF
ifcOWLEXPRESS
RDF
simpleBIM
Converter?
Rules?
…?
Converter?
Rules?
…? 113

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

Simplifying IfcRelationship instances
119

Simplifying IfcRelationship instances
120

Removing geometric information
122

Rewriting PSETs and property values
123

124
Rewriting PSETs and property values

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

Results (2)
126
3. Rewriting properties
• 1,630 triples
• 112kb
4. IfcRelationship instances
• 1,339 triples
• 83kb

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 size
10,3% of triple count

5/5: LINKED DATA IN INFRA
128128
Jakob Beetz, Henk Schaap, Pieter Pauwels, Jim Plume

T. Liebich (2013), IFC for Infrastructure, INFRA-BIM Workshop, Helsinki

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, Ontological
Framework for a Semantic Digital Archive. DuraArk Deliverable D3.3.2.
132

Infra as Linked Data – courtesy of Jakob Beetz

Outline
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

Thank you
Pieter Pauwels
pipauwel.pauwels@ugent.be

UGent Research Projects on Linked Data in Architecture and Construction

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