Federated Approach for Interoperating AEC/FM Ontologies

AnaROXIN–ana-maria.roxin@u-bourgogne.fr
Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle
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

Knowledge models in AEC/FM
ifcOWL
ifcWOD
simpleBIM
COBieOWL
SIMModel
3

Related Problem
Data
IntegrationifcOWL
ifcWOD
COBieOWL
simpleBIM
SIMModel
4

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/

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…

A double Goal
Interoperability
at the schema
level
Rule-based
integration
Interoperability
at the data
level
Federated
architecture
7

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

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

FOWLA – General architecture
10
Autonomous
ontologies
Ontology
alignments
(rule-based)
Inference
mechanisms

FOWLA Benefits
Avoiding data redundancy
Inferring new ontology alignments
Modularizing maintainability
Querying with vocabulary terms issued from different ontologies
Improving query execution time
11

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

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 !

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)

Alignment (FD) – Instance to class mapping
15
ifcOWL &
COBieOWL
ABox
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

Alignment (FD) – Creating missing instances
16
ifcOWL &
COBieOWL
ABox
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

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)

Query Execution
18
Onto
1
Onto
2
Onto
N
How to express queries ?
How long does it take to
get an answer ?

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 }

◼ 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 !

◼ 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.

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

Conclusion
◼ An approach for ontology federation
◼ Addresses semantic heterogeneity
◼ Advantages:
Deducing new knowledge
Flexible query composition
Reduced query execution time
23

Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle
ana-maria.roxin@u-bourgogne.fr
Thank you for your attention !

Federated Approach for Interoperating AEC/FM Ontologies

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