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UGent Research Projects on Linked Data in Architecture and Construction

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UGent Research Projects on Linked Data in Architecture and Construction

  1. 1. 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
  2. 2. 2
  3. 3. 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
  4. 4. 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
  5. 5. 5
  6. 6. 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
  7. 7. Outline 1. What is Linked Data? What are Semantic Web technologies? 2. The standards: buildingSMART and W3C 3. Research projects 7
  8. 8. 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
  9. 9. Linked Open Data cloud (LOD) http://tomheath.com/blog/2009/03/linked-data-web-of-data-semantic-web-wtf/9
  10. 10. • 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
  11. 11. LABELLED DIRECTED Triple RDF Graphs, what are they? 11
  12. 12. RDF graphs are DIRECTED, LABELLED GRAPHS RDF Graphs, what are they not? Hierarchies (cfr. XML) Relational databases (cfr. SQL) 12
  13. 13. RDF Data Model predicate subject object 13
  14. 14. Connecting Triples SUBJECT OBJECT PREDICATE OBJECT PREDICATE OBJECT PREDICATE OBJECTPREDICATE 14
  15. 15. The result: an RDF graph 15
  16. 16. https://www.w3.org/DesignIssues/diagrams/sweb-stack/2006a.png
  17. 17. @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
  18. 18. • 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
  19. 19. URI URI URI URI URI URI URI URI URI URI URI 19
  20. 20. MyBuilding Cities Data integration over the web is now possible 20
  21. 21. • distributed / decentralised information management • interactive information search and reasoning over the web • sharing partial data Main principles 21
  22. 22. Linked Open Data cloud (LOD) http://tomheath.com/blog/2009/03/linked-data-web-of-data-semantic-web-wtf/
  23. 23. 23
  24. 24. 24
  25. 25. 25
  26. 26. Ontologies https://www.w3.org/DesignIssues/diagrams/sweb-stack/2006a.png
  27. 27. 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.
  28. 28. 28
  29. 29. BIM GIS BEMS sensor FM no full integration rather on-demand high-quality information exchange regulations 29
  30. 30. Bring BIM into the Semantic Web BIM 30
  31. 31. http://www.buildingsmart-tech.org/ future/linked-data/ 31
  32. 32. LDAC 2015 LDAC 2014 LDAC 2012 32
  33. 33. 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
  34. 34. 34
  35. 35. 35
  36. 36. 36
  37. 37. Outline 1. What is Linked Data? What are Semantic Web technologies? 2. The standards: buildingSMART and W3C 3. Research projects 37
  38. 38. Standardisation bodies CEN/TC 442 ISO TC59 Linked Data WG OpenBIMGuides WG BuildingSMART Benelux 38
  39. 39. 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
  40. 40. 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/
  41. 41. The buildingSMART triangle http://buildingsmart.org/
  42. 42. Fit in BuildingSMART activities http://www.buildingsmart.org/standards/technical-vision/technical-roadmaps/
  43. 43. 43
  44. 44. 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
  45. 45. Image courtesy: Jakob Beetz, TU Eindhoven IFC INFRA SENSOR GIS 45
  46. 46. Aims: 1. ifcOWL ontology 2. align with buildingSMART efforts 3. LD-oriented support 46
  47. 47. 47
  48. 48. 48
  49. 49. 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).
  50. 50. 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).
  51. 51. 51
  52. 52. 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
  53. 53. 53 HTML documentation pages
  54. 54. Infrastructure Room Technical Room Building Room Product Room Regulatory Room BuildingSMART BIM Infra GIS IDMs MVDs BIM- Guides bSDD RulesifcOWL 54
  55. 55. 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. 56. 56 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.
  57. 57. Image from: Lars Bjørkhaug. Integration of bSDD into the IfcDoc tool. Presentation at BuildingSMART Int’l Standards Summit 2016, Jeju, Korea.
  58. 58. 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.
  59. 59. 59
  60. 60. 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
  61. 61. SOURCE: http://neo4j.com/ 61
  62. 62. 62
  63. 63. 63
  64. 64. 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
  65. 65. 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
  66. 66. Order, order! First Order Logic (FOL) Second Order Logic (FOL) Propositional Logic Variables quantify over individuals and relations Variables quantify over individuals No variables or quantifiers Predicate Logic 66
  67. 67. FOL subsets: tastes of logic First Order Logic (FOL) Horn Logic Datalog Predicate Logic Description Logic (DL) subsets SWRL N3 subsets Prolog OWL 67
  68. 68. 1/5: COMPLIANCE CHECKING Pieter Pauwels, Ghent University Ana Roxin, Université de Bourgogne
  69. 69. Abox – Tbox – Rbox ABox TBox RBox Instances Ontology IF-THEN rules 69
  70. 70. 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" . } 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.
  71. 71. 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 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.
  72. 72. 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" . } . 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.
  73. 73. Serialisations of RDF graphs https://www.w3.org/DesignIssues/diagrams/n3/venn
  74. 74. 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.
  75. 75. 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.
  76. 76. 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.
  77. 77. 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.
  78. 78. 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.
  79. 79. 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.
  80. 80. 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.
  81. 81. 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.
  82. 82. 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.
  83. 83. 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
  84. 84. 84
  85. 85. 85
  86. 86. 86
  87. 87. 87
  88. 88. 88
  89. 89. 89
  90. 90. 90
  91. 91. 91
  92. 92. 92
  93. 93. 93
  94. 94. 94
  95. 95. 95
  96. 96. 96
  97. 97. 97
  98. 98. 98
  99. 99. 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
  100. 100. 100
  101. 101. Performance benchmark variables Schema (TBox) • ifcOWL Instances (ABox) • 369 ifcOWL- compliant building models Rules (RBox) • 68 data transformation rules 101
  102. 102. • 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
  103. 103. 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
  104. 104. 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
  105. 105. 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
  106. 106. 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
  107. 107. 4/5: SIMPLEBIM 107 Pieter Pauwels, Ghent University Ana Roxin, Université de Bourgogne
  108. 108. 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.
  109. 109. T. Liebich. buildingSMART Data Standards. BuildingSMART International Summit 2012. ISO 29481 ISO 16739 IFC, MVDs and IDM
  110. 110. MVDusability 110
  111. 111. SimpleBIM
  112. 112. 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 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.
  113. 113. RDFIFC-SPF ifcOWLEXPRESS RDF simpleBIM Converter? Rules? …? Converter? Rules? …? 113
  114. 114. 114
  115. 115. 115 inst:IfcWindow_1893 inst:IfcWindow_1842 inst:IfcWallStandardCase_696 simplebim:hasWindow simplebim:hasWindow
  116. 116. 116
  117. 117. 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
  118. 118. Simplification strategy 118 1 •Removing geometric information 2 •Unwrapping data types 3 •Rewriting properties 4 •IfcRelationship instances
  119. 119. Simplifying IfcRelationship instances 119
  120. 120. Simplifying IfcRelationship instances 120
  121. 121. Unwrapping data types 121
  122. 122. Removing geometric information 122
  123. 123. Rewriting PSETs and property values 123
  124. 124. 124 Rewriting PSETs and property values
  125. 125. 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)
  126. 126. 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)
  127. 127. 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
  128. 128. 5/5: LINKED DATA IN INFRA 128128 Jakob Beetz, Henk Schaap, Pieter Pauwels, Jim Plume
  129. 129. T. Liebich (2013), IFC for Infrastructure, INFRA-BIM Workshop, Helsinki
  130. 130. Jakob Beetz, GIS / BIM interoperabiliteit: STUMICO presentatie April 2014. http://www.slideshare.net/JakobBeetz/gis-bim-interoperabiliteit-stumico-presentatie-april-2014
  131. 131. Jakob Beetz, GIS / BIM interoperabiliteit: STUMICO presentatie April 2014. http://www.slideshare.net/JakobBeetz/gis-bim-interoperabiliteit-stumico-presentatie-april-2014
  132. 132. 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
  133. 133. Infra as Linked Data – courtesy of Jakob Beetz
  134. 134. 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
  135. 135. Thank you Pieter Pauwels pipauwel.pauwels@ugent.be

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