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A Method to generate a Modular ifcOWL Ontology

Abstract and Figures

Building Information Modeling (BIM) and Semantic Web technologies are becoming more and more popular in the Architecture Engineering Construction (AEC) and Facilities Management (FM) industry to support information management , information exchange and data interoperability. One of the key integration gateways between BIM and Semantic Web is represented by the ifcOWL ontology, i.e. the Web Ontology Language (OWL) version of the IFC standard, being one of reference technical standard for AEC/FM. Previous studies have shown how a recommended ifcOWL ontology can be automatically generated by converting the IFC standard from the official EXPRESS schema. However, the resulting ifcOWL is a large monolithic ontology that presents serious limitations for real industrial applications in terms of usability and performance (i.e. querying and reasoning). Possible enhancements to reduce the complexity and the data size consist in (1) modularization of ifcOWL making it easier to use subsets of the entire ontology, and (2) rethinking the contents and structure of an ontology for AEC/FM to better fit in the semantic web scope and make its usage more efficient. The second approach can be enabled by the first one, since it would make it easier to replace some of the ifcOWL modules with new optimized ontologies for the AEC-FM industry. This paper focuses on the first approach presenting a method to automatically generate a modular ifcOWL ontology. The method aims at minimizing the dependencies between modules to better exploit the modularization. The results are compared with simpler and more straightforward solutions.
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A Method to generate a Modular
ifcOWL Ontology
Walter TERKAJ a,1, and Pieter PAUWELSb
aInstitute of Industrial Technologies and Automation (ITIA-CNR), Milan, Italy
bUniversity of Ghent, Ghent, Belgium
Abstract. Building Information Modeling (BIM) and Semantic Web technologies
are becoming more and more popular in the Architecture Engineering Construction
(AEC) and Facilities Management (FM) industry to support information manage-
ment, information exchange and data interoperability. One of the key integration
gateways between BIM and Semantic Web is represented by the ifcOWL ontology,
i.e. the Web Ontology Language (OWL) version of the IFC standard, being one
of reference technical standard for AEC/FM. Previous studies have shown how a
recommended ifcOWL ontology can be automatically generated by converting the
IFC standard from the official EXPRESS schema. However, the resulting ifcOWL
is a large monolithic ontology that presents serious limitations for real industrial
applications in terms of usability and performance (i.e. querying and reasoning).
Possible enhancements to reduce the complexity and the data size consist in (1)
modularization of ifcOWL making it easier to use subsets of the entire ontology,
and (2) rethinking the contents and structure of an ontology for AEC/FM to bet-
ter fit in the semantic web scope and make its usage more efficient. The second
approach can be enabled by the first one, since it would make it easier to replace
some of the ifcOWL modules with new optimized ontologies for the AEC-FM in-
dustry. This paper focuses on the first approach presenting a method to automat-
ically generate a modular ifcOWL ontology. The method aims at minimizing the
dependencies between modules to better exploit the modularization. The results are
compared with simpler and more straight-forward solutions.
Keywords. IFC, ifcOWL, Ontology, Modularization, EXPRESS
1. Introduction
BIM (Building Information Modeling) is gaining more and more relevance in the Ar-
chitecture Engineering Construction (AEC) and Facilities Management (FM) industry to
support the digitalization of the business process. Industry Foundation Classes (IFC) [16]
is one of the standards in the BIM domain and it is widely used in industrial applications.
However, there are barriers limiting its semantic interoperability and adoption on a larger
scale [23]. Indeed, the IFC standard is provided as single schema written in EXPRESS
language [14] that is extremely large and complex, being characterized by an almost
monolithic structure. For instance, the IFC4 ADD1 EXPRESS schema contains 768 En-
1Corresponding Author: Institute of Industrial Technologies and Automation (ITIA-CNR), Milan, Italy; E-
mail: walter.terkaj@itia.cnr.it
tity data types, 206 Enumeration data types, 60 Select data types, 131 defined data types,
46 FUNCTION declarations, and 2 RULE declarations. The complex structure of IFC
jeopardizes its exploitation by industrial domains outside the core AEC applications that
may need a simple model of building, spaces, elements and their relations with geometry,
topology, monitoring, automation and control, safety, etc.
Semantic Web offers opportunities to provide more effective solutions also for the
BIM domain, by exploiting its typical enablers in terms of formal modeling language,
data distribution, extensibility, and automatic reasoning. Possible BIM solutions based
on Semantic Web technologies include:
1. an OWL version of IFC, named ifcOWL. Previous works [22,21] demonstrated
how the ifcOWL can be automatically generated by converting the IFC EX-
PRESS schema to OWL. For example, this conversion leads to an ifcOWL on-
tology [21] for IFC4 ADD1 with 1313 classes, 1580 object properties, 13867
logical axioms, and 1158 individuals.
2. the development of novel ontologies for BIM that are based on the semantic web
principles and designed exploiting modularity and extendability since the begin-
ning. Such approach is currently investigated by the World Wide Web Consor-
tium (W3C) with the Linked Building Data (LBD) Community Group that is
working on a set of loosely related ontologies for Building Topology (BOT) [24],
Product, Geometry, Automation and Control [28], etc.
Given the original complexity of the IFC schema, also the resulting ifcOWL on-
tology is considerably large and complex to load and use. The ifcOWL ontology has
many interdependencies that it becomes a huge challenge to exploit data distribution both
at Tbox and Abox level. The ontology takes full advantage of OWL2 DL expressivity
(SHIQ(D)), which can lead to a high number of assertions when handed to OWL reason-
ing engines because all axioms are loaded when the ontology is referenced by an RDF
graph.
This paper will investigate how the ifcOWL ontology can be split into separate on-
tology modules, so that end users and applications only need to select the modules that
are actually going to be used. The modularization is expected to reduce the complex-
ity and provide enablers also for future extensions and integrations. Section 2 briefly
presents related works on ontology modularization, whereas Section 3 addresses the spe-
cific problem of modularizing the ifcOWL ontology. Section 4 presents the modular-
ization algorithm and Section 5 shows the results of the application of the algorithm to
generate a modular ifcOWL ontology. Finally, conclusions are drawn in Section 6.
2. Related Works
As defined by d’Aquin et al. [10], the task of partitioning an ontology is “the process
of splitting up the set of axioms into a set of modules {M1, ..., Mk}such that each Mi
is an ontology and the union of all modules is semantically equivalent to the original
ontology O”. The topic of ontology modularization has been largely addressed in the
literature [27]. Indeed, modularity can be beneficial both during the design phase and
during the deployment and usage. Some of the benefits of modularity can be mentioned
as follows [19]:
scalability for querying data and reasoning on ontologies
scalability for evolution and maintenance
complexity management
understandability
context-awareness and personalization
reuse
Modularity can be applied to pursue different goals [7] while using different strate-
gies for modularity [19], some times also in a concurrent way:
disjoint or overlapping modules
semantics-driven strategies
structure-driven strategies (e.g. using graph decomposition algorithms)
machine learning strategies
monitoring modularization and making it evolve
Various techniques have been proposed mainly to process large ontologies and ex-
tract modules from them, e.g. [5,9,11,13,15,18]. Modularization has been applied in
various knowledge domains, for instance architectural design [6,3] and biomedical do-
main [20,26,29]. In some cases the ontologies were designed since the beginning in a
modular way, for example the TOVE ontologies [12] supporting the enterprise integra-
tion. Furthermore, when addressing a modularization problem, the definition of the eval-
uation criteria [10] plays a key role and it must be consistent with the overall goals.
3. ifcOWL Ontology and Modularity
The modularization of ifcOWL is an important step in updating the ontology so that it
can be more efficiently used in a web context. Indeed, a modular ifcOWL is expected to
improve:
usability
performance (e.g. query and reasoning)
ease of alignment with other ontologies, also reducing overlapping
At least two strategies can be envisioned to generate a modular ifcOWL:
1. modularization by content, i.e. the definition of classes and properties are sepa-
rated based on the knowledge domain they are related to, e.g. geometry, units of
measurement, building components, HVAC, etc.
2. modularization by axiom type, i.e. separating the different axioms that are in-
cluded in ifcOWL, such as definition of classes, subsumption, data/object proper-
ties, domain/range of properties, equivalent classes, cardinality restrictions apart,
etc. This option might even align with the idea of being able to load an ifcOWL
in specific OWL profiles (OWL2 EL, OWL2 QL, OWL2 RL - see [17]). For ex-
ample, an ifcOWL version not containing cardinality restrictions could be used
to conform with the OWL2 EL profile. On the other hand, most OWL reasoners
allow to specify to which level of expressiveness (RDFS, OWL2 EL, OWL2 QL,
OWL2RL) an ontology should be loaded. Thus, when reasoning is concerned,
this first option is already supported by using an OWL reasoner with appropriate
settings.
Figure 1. The IFC data schema architecture with conceptual layers, as displayed in the introduction of the IFC
specification (IFC4 ADD1) [16]
Herein the attention is focused on the first strategy that is supported by the fact that
the IFC standard was developed in a modular way and each data type (i.e. entity, enu-
meration, select, defined) in the EXPRESS schema belongs to a specific sub-schema, as
reported in its documentation [16]. Indeed, the IFC schema consists of four layers, each
containing sub-schemas (see Figure 1) that define a part of all EXPRESS data types. For
example, the IfcActorResource schema (bottom left in Figure 1) contains 3 enumera-
tions and 8 entities. The corresponding OWL definitions could in theory be kept in a sep-
arate IfcActorResource ontology module, which would be significantly smaller than
the complete ifcOWL ontology, thus resulting in better usability. However, many of the
data types in the IFC schema are tightly interconnected with each other, not only within
a sub-schema but also between different sub-schemas. In addition, in several cases there
are reciprocal dependencies between sub-schemas, even belonging to separate layers.
For example, IfcApprovalResource imports IfcControlExtension and vice versa.
Hence, in order to make a useful modularization, a full investigation of the schema needs
to be made, and the relation between the different sub-schemas (and therefore modules)
would need to be reconsidered to a significant level and detail.
Beetz et al. 2009 [4] already proposed a modular ontology for an earlier version of
ifcOWL. The authors addressed the problem of interwoven interdependencies by mov-
ing some axioms to additional modules, named pivot ontologies, that include a set of
independent semantic clusters. However, the problem of cyclic references was not com-
pletely solved. Furthermore, the author addressed the problem of modularization of the
Abox ontologies.
4. Modularization Algorithm
The proposed modularization algorithm can be applied to convert any EXPRESS schema
(e.g. IFC [16], but also ISO 15531, ISO 14649, etc.) to a modular OWL ontology. A novel
algorithm was developed to exploit the peculiar problem settings, since the modulariza-
tion takes place while converting an EXPRESS schema (e.g. IFC schema) to an OWL
ontology (e.g. ifcOWL), instead of being executed on an already existing large mono-
lithic ontology (e.g. the already generated full ifcOWL). Once the algorithm assigns an
EXPRESS definition to a module, then the conversion to the corresponding OWL axiom
is executed as stated in [21] and described with more details in [22]. It must be noted that
an automatic conversion of a technical standard from an EXPRESS schema to an OWL
ontology may lead to problems related to the lack of a precise definition and meaning of
some concepts, thus hindering semantic interoperability. Therefore, a proper ontological
analysis of the original standard should be carried out, as addressed in the works [2,25,8].
However, such analysis goes beyond the scope of this work.
The goal of the algorithm consists in finding the best way of implementing a given
input modularization by minimizing the number of direct import relations between mod-
ules. Moreover, the algorithm must avoid to create reciprocal dependencies between
modules because it would lead to circular import paths. Even though circular import
is not forbidden according to OWL2, still it is not desirable because it would actually
weaken the modularization. Indeed, a direct import of any node in a circular path will
lead to indirectly importing all the nodes in the circle; thus the final effect is that all the
modules in a circular path are merged.
In summary, the algorithm receives as input the following pieces of information:
content of a parsed EXPRESS schema in terms of data types (i.e. defined data, en-
tity, select, enumeration), subsumption relationships and attributes of each entity
data type.
input modularization in terms of mapping between EXPRESS data types and
modules. This mapping can be the results of more or less sophisticated method-
ologies, or it can be provided in a technical documentation (as in the case of
IFC [16]), or it can be simply set by the user based on his/her needs.
priority level associated with each module. This priority is used to set import rela-
tions between modules. Ceteris paribus, the module with lower priority will im-
port the module with higher priority. For instance, the priority may be associated
with the layer in the whole IFC schema, giving highest priority to the modules in
the Resource Layer and the lowest to the modules in Domain Layer.
The modularization algorithm is decomposed into two routines Algorithm 1 and Al-
gorithm 2. Algorithm 1, via the function GenModularETO, elaborates the various EX-
PRESS definitions that must be converted to a corresponding OWL axiom. The OWL
axiom is serialized as a set of triples that are added to a specific module based on the
result of the function SetModule in Algorithm 2. Thus, the function SetModule incre-
mentally adds import relationships between modules based on the actual needs derived
from the inter-module dependencies between EXPRESS data types. After STEP 4 of Al-
gorithm 1 all the OWL axioms required to convert the EXPRESS schema are assigned to
a specific module. Moreover, the full set of dependencies (i.e. import relations involving
the term owl:import) between modules is available and can be represented as a directed
graph, where the modules are nodes and the import relations are arcs. With reference to
the notation adopted in the algorithm, the graph can be defined as G= (M,I), where M
is the set of modules (i.e. nodes) and Iis the set of direct import relations (i.e. arcs). If
(w,z)I, then it means that module wMdirectly imports module zM.
Algorithm 1 Modularization Algorithm
Input: set Ent of EXPRESS entities
set Enu of EXPRESS enumerations
set Sof EXPRESS selects
set Dof EXPRESS defined data types
set of supertypes sup(t)of EXPRESS data type t(Ent Enu SD)
set of items it(s)belonging to the EXPRESS select sS
set attr(e)of attributes of entity eEnt
data type ran(e,a)(Ent E nu SD)being the range of attribute aattr(e)
set Mof modules
module mod(t)Mto which the data type t(Ent E nu SD)is assigned
set Iof ordered pairs of modules defining direct import relations
function GEN MODULA RE TO(E nt,E nu,S,D,sup,it,att r,ran,M,mod)
for all t(Ent E nu SD)do .STEP 1
add the OWL axiom defining cto module mod(t)
for all t(Ent E nu SD)do .STEP 2
for all asup(t)do
add the OWL axiom defining the subsumption realtionship to the module returned
by SE TMODULE(mod(t),mod(a),I)
for all sSdo .STEP 3
for all ait(s)do
add the OWL axiom defining the subsumption relation between sand ato the
module returned by SE TMODULE(mod(s),mod(a),I)
for all eEnt do .STEP 4
for all aattr(e)do
add the OWL axiom defining the attribute relation (i.e. property defini-
tion and restrictions) between eand ran(e,a)to the module returned by
SET MODULE(mod(e),mod(ran(e,a)),I)
Apply the transitive reduction to the graph G= (M,I).STEP 5
The result of Algorithm 1) and 2 is a directed acyclic graph (DAG), i.e. cycles in the
graph are avoided. It can be demonstrated that the resulting graph is a DAG by consider-
ing that a topological ordering is possible if and only if the graph has no directed cycles.
A topological ordering can be generated from the resulting graph because each pair of
nodes (i.e. modules) can be ordered, since Algorithm 2 guarantees that there is only one
import direction (direct or indirect) between them. Axioms involving atoms belonging to
two different modules are added always to the same module, thus solving the problem of
circular imports without needing to merge modules.
The resulting graph can be further optimized by applying a transitive reduction [1]
that allows to obtain a graph with fewer arcs but the same reachability (cf. STEP 5 of Al-
Algorithm 2 Set Module Algorithm
Input: set Mof modules
priority p(m)of module mM
set Iof ordered pairs of modules defining direct import relations
Output: selected module
updated set I
function SET MODULE(x,y,I)
if x=ythen
return x
else
Calculate the transitive closure of graph G= (M,I)to obtain the set of reachability
relations R
if (x,y)Rthen
return x
else if (y,x)Rthen
return y
else if p(x)>p(y)then
add (y,x)to the set I,return y
else
add (x,y)to the set I,return x
gorithm 1). In case of a DAG the transitive reduction is unique and consists in a subgraph
of the original graph that minimizes the number of arcs, i.e. the number of the imports.
5. Experiments
This section presents the experiments related to the generation of a modular ifcOWL on-
tology from the IFC4 EXPRESS schema2. As reported in Table 1, the input modulariza-
tion is based on the 38 IFC sub-schemas (Figure 1), plus the ontology modules express3
and list4that are automatically included during the EXPRESS to OWL conversion.
Table 1 reports also the priority level associated with each module, as required to execute
the algorithm. Three different versions of modularization algorithm have been tested to
demonstrate the benefits of the full version presented in Section 4:
1. Simple version, i.e. the modularization algorithm consisting of Algorithm 1 and
Algorithm 3 that represents a simplification of Algorithm 2.
2. Basic version, the modularization algorithm consisting of Algorithm 1 and Algo-
rithm 2, but without STEP 5 in Algorithm 1.
3. Full version, i.e. the modularization algorithm consisting of Algorithm 1 and
Algorithm 2.
Algorithm 3, used in the Simple version, implements the selection of the module
where the OWL axioms are added by looking at the incumbent need, without considering
the already set module dependencies. This simplification leads to a higher number of
direct import relations (189) compared to the Basic version (95). Moreover, the Simple
version causes the realization of circular import patterns (e.g. modules 10 and 11 import
each other), thus disabling the chance to execute a straightforward and deterministic
transitive reduction.
2http://www.ontoeng.com/modularIfcOWL/
3https://w3id.org/express
4https://w3id.org/list
Table 1. Modules of the ifcOWL ontology with definition of id and priority level.
Module IFC Layer Label Priority
list N/A 1 5
express N/A 2 5
IFCACTORRESOURCE Resource 3 4
IFCAPPROVALRESOURCE Resource 4 4
IFCCONSTRAINTRESOURCE Resource 5 4
IFCCOSTRESOURCE Resource 6 4
IFCDATETIMERESOURCE Resource 7 4
IFCEXTERNALREFERENCERESOURCE Resource 8 4
IFCGEOMETRICCONSTRAINTRESOURCE Resource 9 4
IFCGEOMETRICMODELRESOURCE Resource 10 4
IFCGEOMETRYRESOURCE Resource 11 4
IFCMATERIALRESOURCE Resource 12 4
IFCMEASURERESOURCE Resource 13 4
IFCPRESENTATIONAPPEARANCERESOURCE Resource 14 4
IFCPRESENTATIONDEFINITIONRESOURCE Resource 15 4
IFCPRESENTATIONORGANIZATIONRESOURCE Resource 16 4
IFCPROFILERESOURCE Resource 17 4
IFCPROPERTYRESOURCE Resource 18 4
IFCQUANTITYRESOURCE Resource 19 4
IFCREPRESENTATIONRESOURCE Resource 20 4
IFCSTRUCTURALLOADRESOURCE Resource 21 4
IFCTOPOLOGYRESOURCE Resource 22 4
IFCUTILITYRESOURCE Resource 23 4
IFCKERNEL Core 25 3
IFCCONTROLEXTENSION Core 24 2
IFCPROCESSEXTENSION Core 26 2
IFCPRODUCTEXTENSION Core 27 2
IFCSHAREDBLDGELEMENTS Interoperability 28 1
IFCSHAREDBLDGSERVICEELEMENTS Interoperability 29 1
IFCSHAREDCOMPONENTELEMENTS Interoperability 30 1
IFCSHAREDFACILITIESELEMENTS Interoperability 31 1
IFCSHAREDMGMTELEMENTS Interoperability 32 1
IFCARCHITECTUREDOMAIN Domain 33 0
IFCBUILDINGCONTROLSDOMAIN Domain 34 0
IFCCONSTRUCTIONMGMTDOMAIN Domain 35 0
IFCELECTRICALDOMAIN Domain 36 0
IFCHVACDOMAIN Domain 37 0
IFCPLUMBINGFIREPROTECTIONDOMAIN Domain 38 0
IFCSTRUCTURALANALYSISDOMAIN Domain 39 0
IFCSTRUCTURALELEMENTSDOMAIN Domain 40 0
Algorithm 3 Simple version of Set Module Algorithm
Input: set Mof modules
priority p(m)of module mM
set Iof ordered pairs of modules defining direct import relations
Output: selected module
updated set I
1: function SET MODULE(x,y,I)
2: if (x,y)/Ithen
3: add (x,y)to the set I
4: return x
The comparison between the Basic and Full versions show the impact of the transi-
tive reduction, since the total number of import relations becomes 46. A synthetic com-
parison of the three algorithm versions is reported in Table 2, showing that a great deal
of unnecessary imports can be eliminated. The graph-based representation of the three
modular ifcOWL solutions are shown in Figures 2 and 3 highlighting how strongly in-
terconnected are the IFC sub-schemas. Analyzing Figure 3b, it can be noted that:
there are modules in the Core layer (i.e. IfcControlExtension and IfcProcessEx-
tension) that are not actually used in any of the modules in the upper levels;
just two modules in the Resource layer are not imported (directly or indirectly)
by IfcKernel, i.e. IfcStructuralLoadResource and IfcMaterialResource;
just two modules in the Interoperability layer are imported by modules in the Do-
main layer, i.e.
IfcSharedBldgServiceElements
and
IfcSharedComponentElements
.
Table 2. Synthetic results of the three versions of modularization algorithm for the ifcOWL ontology, consid-
ering modules 3-40 defined in Table 1
Simple Basic Full
n. modules 38 38 38
total n. imports 189 96 47
max n. imports per module 12 6 2
avg n. imports per module 4.97 2.5 1.21
circular imports yes no no
The experiments demonstrate how the proposed algorithm enables to optimize the
number of import relations. This result is important because it leads to a decomposed
ifcOWL ontology with a minimal number of inter-dependencies, thus easing the selection
and extraction of a subset of modules that may better fit the requirements of a user.
Finally, even if the same Full version of the algorithm is adopted, different solutions
can be obtained based on the priority assigned to the modules. Indeed, the final result of
the algorithm is deterministic only if all modules have a different priority. On the other
hand, if an import relation is required between two modules having the same priority,
then the direction of the import depends on the order of the definitions that are elaborated
by Algorithm 1. For example, since modules 14 and 15 need each other (see Figure 2) and
have the same priority, the final solution could include that module 15 imports module 14,
instead of vice versa as in the experiment (see Figures 3a and 3b).
6. Conclusions
This paper presented an approach to generate a modular version of an OWL ontology
that is automatically converted from an EXPRESS schema. The attention was focused
on the case of the IFC schema given the large size of the resulting ifcOWL ontology. A
modular version of ifcOWL can help to solve practical problems related to its usability
and the scalability of software applications based on it. Moreover, the modularization
algorithm can be used also to extract fragments of the ifcOWL that are relevant for the
specific applications. This can be achieved by missing to assign some EXPRESS data
types to any module. Further developments will address:
the generation of additional OWL modules to convert the IFC Property Sets that
are currently not included in the IFC EXPRESS schema
the investigation of other modularization strategies, e.g. the second one presented
in Section 3, and the introduction of criteria to at least partially control the defi-
nition of dependencies between modules, e.g. by optimizing their priorities
testing the benefits of working with a subset of ifcOWL modules from a compu-
tational perspectives
Figure 2. Modular ifcOWL ontology resulting from the Simple version of the algorithm. The labels of the
nodes are defined in Table 1
the integration of a fragment of the ifcOWL ontology with other ontologies
the comparison of the modular ifcOWL with other ontologies for BIM that are
designed to be modular since the beginning [28]
modularization strategies for Abox ontologies [4].
Acknowledgments
This work has been partially funded by the Italian research project Smart Manufactur-
ing 2020 within the Cluster Tecnologico Nazionale Fabbrica Intelligente.
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... In Fig. 12, related ontologies for different information sources are mapped to corresponding classes in the DiCon with different types of relations. For example, the building element (ifc:IfcElement and bot:Element) and spatial element classes (ifc:IfcSpatialElement and bot: Zone) in both the IFC/ifcOWL [50,51] and BOT [52] ontologies are respectively defined as subclasses of the BuildingObject or Location in the DiCon. Such linkages provide a portal to link CW with data from BIM or Linked Building Data (LBD) to use their product or spatial information, or for further related information. ...
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... Modularising the IFC could, hence, simplify several problems that currently occur, such as schema overhead when describing small projects or singular products, filtering large data amounts, or certifying software applications for the entire schema. This is not a new idea, Terkaj and Pauwels [163] already proposed a modularisation based on the RDF serialisation of IFC, the ifcOWL. Nonetheless, it should be noted that the IFC are intended as an exchange schema for digital building models and not product descriptions. ...
... While one of their topics is the Semantic Web and Linked Data, they do not restrict to those technologies but have a broader spectrum of interests, such as the Web of Things and telecommunication in general. xx,28,38,43,50,51,156,[160][161][162][163] ...
... These federated queries -queries that can be distributed amongst several linked data sources -are submitted to a single authority, which responds by querying the distributed members of the database and returning an aggregation of those results. The approach is beneficial because data can be maintained in its original format, though some compatible API must be provided to make each source queryable [43,44]. LBD also admits the concept of a common context; in this approach, relationships between represented entities are captured through relationships that are defined as part of some ontology defined using the Web Ontology Language (OWL). ...
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... The process results in a very rich ontology (1326 classes, 1596 properties, 1162 named individuals), which can pose issues related to its management, reuse, and understandability. Aware of these and other "disadvantages", Terkaj and Pauwels [39] proposed a modularization approach to the ifcOWL ontology. The conversion of the H-BIM IFC model to IfcOWL will be explored in the present paper. ...
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Intervention projects for historical buildings depend on the quality of multidisciplinary data sets; their collection, structure, and semantics. Building information model (BIM) based workflows for historical buildings accumulate some of the data sets in a shared information model that contains the building’s geometry assemblies with associated attributes (such as material). A BIM model of any building can be a source of data for different engineering assessments, for example, solar and wind exposure and seismic vulnerability, but for historic buildings it is particularly important for interventions like conservation, rehabilitation, and improvements such as refurbishment and retrofitting. When the BIM model is abstracted to a semantic model, enabling the use of semantic technologies such as reasoning and querying, semantic links can be established to other historical contexts. The semantic technologies help historic building experts to aggregate data into meaningful form. Ontologies provide them with an accurate knowledge representation of the concepts, relationships, and rules related to the historic building. In the paper, we are proposing an improved workflow for the transformation of a heritage BIM model to a semantic model. In the BIM part the workflow demonstrates how the fully parametric modelling of historical building components is relevant, for example, in terms of reusability and adaptation to a different context. In the semantic model part, ontology reuse, reasoning, and querying mechanisms are applied to validate the usability of the proposed workflow. The presented work will improve knowledge-sharing and reuse among stakeholders involved in historic building projects.
... The schema level mediation aims to reconstruct the model structure of ifcOWL and then apply the modular ifcOWL on a specific purpose. The concept of modular ifcOWL was firstly proposed by Terkaj & Pauwels [32] and has been adopted in several types of research, such as the Building Topology Ontology [31] and ifcOWL-DfMA Ontology [33]. The instances level mediation is intended to map the concepts with similar semantics between ifcOWL and regulatory ontology by a set of mapping rules. ...
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... However, within the RDF serialisation of IFC, ifcOWL, some of the IFC's issues can be handled. Yet the criticism of the ifcOWL regarding its lack of modularity and verbose modelling of properties due to the literal translation of the IFC schema remainFarias et al. (2015), Terkaj and Pauwels (2017), Pauwels and Roxin (2016). Hence, the ifcOWL does not comply with the presented considerations. ...
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