Federated Approach for Interoperating AEC/FM Ontologies
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This document presents FOWLA (Federated Architecture for OWL Ontologies), an approach for ontology federation that addresses semantic heterogeneity. FOWLA defines autonomous ontologies that are aligned through rules, allowing inference across ontologies. Queries can be expressed using terms from any aligned ontology. Rule selection reduces query execution time compared to using all alignment rules. The approach was demonstrated by aligning ifcOWL and COBieOWL ontologies for building information modeling. FOWLA enables deducing new knowledge, flexible query composition, and reduced query response times.
Federated Approach for Interoperating AEC/FM Ontologies
- 1. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle ana-maria.roxin@u-bourgogne.fr
- 2. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Agenda 11 •Problems with existing knowledge models in AEC/FM 22 •Federated Architecture for OWL Ontologies (FOWLA) 33 •FOWLA Application (IFC and COBie) 2
- 3. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Knowledge models in AEC/FM ifcOWL ifcWOD simpleBIM COBieOWL SIMModel 3
- 5. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Layers of Data Interoperability Semantic Interoperability • Automatically interpret the information exchanged. • To achieve semantic interoperability, both sides must refer to a common information exchange reference model. Organizational Interoperability • Business processes and cross-enterprise collaboration activities Technical Interoperability • Ensures that systems can send and receive data successfully. • Defines the degree to which the information can be successfully “transported” between systems. 5 Source: ISO 19439:2006 Enterprise integration - Framework for enterprise modelling Image sources: http://ecotechitsolutions.com/enterprises/application-interoperability/
- 6. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Achieving Semantic Interoperability 6 Full data integration is only possible considering integration at both Schema and Data level… Semantic Web technologies do not leverage semantic heterogeneity…
- 7. AnaROXIN–ana-maria.roxin@u-bourgogne.fr A double Goal Interoperability at the schema level Rule-based integration Interoperability at the data level Federated architecture 7
- 8. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Federated Architecture for OWL Ontologies • Preserving each system's autonomy Autonomous ontologies • Avoiding data redundancy • Modularizing maintenability Aligned through rules • Reducing the number of alignments to be defined Controlled by inference 8
- 9. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Federated Architecture for OWL Ontologies 9 Autonomous ontologies Mapped through rules Controlled by inference FOWLA OntoN Onto2 Onto1 Onto1 Onto2 OntoN Rule inference performed at query time (backward-chaining): - automatic "translation" between formats - automatic inference of modifications in aligned ontologies
- 10. AnaROXIN–ana-maria.roxin@u-bourgogne.fr FOWLA – General architecture 10 Autonomous ontologies Ontology alignments (rule-based) Inference mechanisms
- 11. AnaROXIN–ana-maria.roxin@u-bourgogne.fr FOWLA Benefits Avoiding data redundancy Inferring new ontology alignments Modularizing maintainability Querying with vocabulary terms issued from different ontologies Improving query execution time 11
- 12. AnaROXIN–ana-maria.roxin@u-bourgogne.fr FOWLA Application Illustration – IFC & COBie ifcOWL • OWL version of IFC2x3 COBieOWL • COBie 2.4 • Semi- automatically conceived Alignment • Construction Operations MVD • Only IFC2x3 mappings 12 FOWLA
- 13. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Avoiding Data Redundancy 13 Contact ≡ IfcActor ifcowl:IfcActor(x) → cobieowl:Contact(x) cobieowl:Contact(x) → ifcowl:IfcActor(x) Floor ≡ IfcBuildingStorey ifcowl:IfcBuildingStorey (x) → cobieowl:Floor(x) cobieowl:Floor(x) → ifcowl:IfcBuildingStorey (x) ?x a cobieowl:Contact . ?x cobieowl:email ?email. ?x a ifcowl:IfcActor . ?x ifcowl:name_IfcRoot ?y. ?y expr:hasString ?z becomes We can directly use a query language to retrieve COBie data originally described using IFC !
- 14. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Zoom on Alignment (Federal Descriptor) 14 ifcOWL TBox ifcOWL ABox COBieOWL TBox COBieOWL ABox swrl1: ifcowl:IfcBuildingStorey (?x) → cobieowl:Floor (?x) swrl2: cobieowl:Floor (?x)→ ifcowl:IfcBuildingStorey (?x) swrl3: ifcowl:IfcBuildingStorey(?x) ∧ ifcowl:description… (?x, ?y) ∧ ifcowl:hasString(?y, ?z) → cobieowl:description(?x,?z) Federal Logic Schema (FOWLA)
- 15. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Alignment (FD) – Instance to class mapping 15 ifcOWL & COBieOWL ABox swrl1: ifcowl:IfcBuildingStorey (?x) → cobieowl:Floor (?x) swrl2: cobieowl:Floor (?x)→ ifcowl:IfcBuildingStorey (?x) swrl3: ifcowl:IfcBuildingStorey(?x) ∧ ifcowl:description… (?x, ?y) ∧ ifcowl:hasString(?y, ?z) → cobieowl:description(?x,?z) Federal Logic Schema (FOWLA) ifcOWL TBox COBieOWL TBox rdf:type
- 16. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Alignment (FD) – Creating missing instances 16 ifcOWL & COBieOWL ABox swrl1: ifcowl:IfcBuildingStorey (?x) → cobieowl:Floor (?x) swrl2: cobieowl:Floor (?x)→ ifcowl:IfcBuildingStorey (?x) swrl3: ifcowl:IfcBuildingStorey(?x) ∧ ifcowl:description… (?x, ?y) ∧ ifcowl:hasString(?y, ?z) → cobieowl:description(?x,?z) swrl4: cobieowl:Floor(?x) ∧ cobieowl:description(?x, ?y) ∧ ifcowl:description…(?x, ?z) ∧ ifcowl:IfcText(?z) → ifcowl:hasString(?z,?y) Federal Logic Schema (FOWLA) ifcOWL TBox COBieOWL TBox ifcowl:hasString rdf:type rdf:type
- 17. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Inferring new Information ◼ Object property cobie:hasDocument defined as an inverse property of cobie:documentTo Automatic inference of new assertions for cobie:hasDocument Based on explicitly asserted cobie:documentTo properties And vice-versa 17 Assertions: cobie:documentTo(doc1,type1) cobie:hasDocument(type2, doc2) Inferences: cobie:documentTo(type2,doc2) cobie:hasDocument(type1, doc1), (type1, type2 instances of cobie:Type) (doc1, doc2 instances of cobie:Document)
- 18. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Query Execution 18 Onto 1 Onto 2 Onto N How to express queries ? How long does it take to get an answer ?
- 19. AnaROXIN–ana-maria.roxin@u-bourgogne.fr How to express queries ? ◼ One can use all terms from any of the aligned ontologies In this example, one can use terms from both ifcOWL and COBieOWL 19 Query name SPARQL Query Q1 SELECT ?x ?y WHERE { ?x cobieowl:name ?y . } Q2 SELECT ?x ?y WHERE { ?x a ifcowl:IfcElement. ?x cobieowl:name ?y.} Q3 SELECT ?x ?y WHERE{ ?x rdf:type ifcowl:IfcBuildingStorey. ?x cobieowl:description ?y }
- 20. AnaROXIN–ana-maria.roxin@u-bourgogne.fr ◼ The number of rules highly impacts query execution time ◼ Our approach allows selecting only the rules that apply to a given query And what about query performance ? 20 ifcOWL 2x3 COBieOWL Aligned through 474 SWRL rules (extracted from COBie MVD) Selection of the subset of rules necessary for answering the query !
- 21. AnaROXIN–ana-maria.roxin@u-bourgogne.fr ◼ Each repository’s ABox contains 1,146,294 triples ◼ Server: Intel Xeon CPU E5-2430 at 2.2GHz with 2 cores out of 6, 8GB of DDR3 RAM memory (Java Heap = 6GB) ◼ Client: Intel Core CPU I7-4790 at 3.6GHz with 4 cores, 8GB of DDR3 RAM memory at 1600MHz (Java Heap = 1GB) Experiment Environment 21 OWL entities COBieOWL ifcOWL v2x3 Classes 30 802 Object properties 32 1292 Data properties 125 247 Inverse properties 7 115 Triples in the Tbox 2212 9978 DL expressivity ALCHIF(D) ALUIF(D) Rules Characteristics KB1 474 All the rules contained in the FLS (all the rules forming the alignment between COBieOWL and ifcOWL) KB2 266 All subsumption rules along with all the rules that have elements from COBieOWL in their head KB3 178 All rules from KB2 minus some of the rules that have elements from COBieOWL in their head (we aimed at reducing the data inferred) KB4 variable All the rules contained in the Activated Rule Set (ARS) conceived by the RS.
- 22. AnaROXIN–ana-maria.roxin@u-bourgogne.fr So let's see query execution time… Query name SPARQL Query Q1 SELECT ?x ?y WHERE { ?x cobieowl:name ?y . } Q2 SELECT ?x ?y WHERE { ?x a ifcowl:IfcElement. ?x cobieowl:name ?y.} Q3 SELECT ?x ?u WHERE { ?x a onto1:C11 . ?y a onto2:C22 . ?x onto1:p12 ?y . ?y onto1:p11 ?x . } 22 Query KB Mean execution time (s) Standard deviation (σσσσ) #RuleSet #Results Q1 KB1 - - 474 0 KB2 - - 266 0 KB3 9.25 12.21 178 1683 KB4 2.23 1.78 16 38318 Q2 KB1 - - 474 0 KB2 - - 266 0 KB3 32.99 0.75 178 74 KB4 0.16 0.04 2 74 Q3 KB1 - - 474 0 KB2 - - 266 0 KB3 71.62 0.95 178 0 KB4 0.88 0.43 5 9
- 23. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Conclusion ◼ An approach for ontology federation ◼ Addresses semantic heterogeneity ◼ Advantages: Deducing new knowledge Flexible query composition Reduced query execution time 23
- 24. AnaROXIN–ana-maria.roxin@u-bourgogne.fr Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle ana-maria.roxin@u-bourgogne.fr Thank you for your attention !