=Paper=
{{Paper
|id=Vol-3081/13paper
|storemode=property
|title=An evaluation of the strict semantics of owl:sameAs in the field of BIM GIS Integration
|pdfUrl=https://ceur-ws.org/Vol-3081/13paper.pdf
|volume=Vol-3081
|authors=Fritz Beck,Jimmy Abualdenien,André Borrmann
|dblpUrl=https://dblp.org/rec/conf/ldac/BeckAB21
}}
==An evaluation of the strict semantics of owl:sameAs in the field of BIM GIS Integration==
https://ceur-ws.org/Vol-3081/13paper.pdf
Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
An evaluation of the strict meaning of owl:sameAs in the
field of BIM GIS Integration
Fritz Beck1, Jimmy Abualdenien1 and Andre Borrmann1
1Technical University of Munich (TUM), 80333 Munich, Germany
fritz.beck@tum.de
Abstract. Linking heterogeneous information models from the domains Building In-
formation Modelling (BIM) and Geospatial Information Systems (GIS) enables syner-
getic effects between these domains. A common approach to define links between cor-
responding objects are identity relation like owl:sameAs from the Web Ontology Lan-
guage (OWL). Identity relations suggests that everything stated about one entity hold
for the corresponding entity (i.e., they share all of their properties). However, this kind
of semantics is too strict for linking objects from heterogeneous information models.
This paper shows the issue of the strict semantics of identity relations for linking build-
ing elements of heterogeneous information models like Industry Foundation Classes
(IFC) and CityGML. In more detail, related literature about heterogeneities and links
between IFC and CityGML are reviewed. Afterwards, the issue caused by the strict
semantics of identity links for linking IFC and CityGML models is illustrated and al-
ternative approaches to identity links are discussed. As a result, identity links are prone
to be misleading for linking building elements represented in Industry Foundation Clas-
ses (IFC) and CityGML models. However, alternative linking approaches have the
short coming that they generally rely on the correct interpretation of the user.
Keywords: BIM GIS Integration, Linked Data, Semantic Web Technologies,
owl:sameAs, Identity links.
1 Introduction
Linking heterogeneous information models across the domains Building Information
Modeling (BIM) and Geospatial Information Systems (GIS) aims to improve the com-
munication among different disciplines and support decision-making during the life cy-
cle of a construction project [1–4]. In particular, the information models Industry Foun-
dation Classes (IFC) and CityGML are promising for synergies between the domains
BIM and GIS due to both their overlapping and distinct representation scope [5, 6].
While IFC is mainly intended to represent information about building assets for design
purposes, CityGML is intended to represent information about various buildings and
their surroundings for spatial analysis purposes. The consequent heterogeneity between
IFC and CityGML caused by the distinct underlying perspectives and purposes impede
a seamless one-to-one mapping between corresponding objects of these information
models [7–11].
Matching of corresponding objects results in a set of correspondences, which is
called alignment [12, 13] or link model [14]. The resulting correspondences are made
explicit and shareable by means of alignment languages like Web Ontology Language
Copyright 2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0)
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(OWL) [15]. In OWL, the links between corresponding objects at instance-level are
commonly represented by means of the expression owl:sameAs which states that the
linked objects “have the same identity” [15]. However, the meaning of same identity is
not further specified such that it is generally interpreted in terms of logical equality.
A common description of logical equality follows the concept of identity according
to Leibniz’ principles of identity of indiscernibles [16]. Roughly spoken, Leibniz’ prin-
ciples of identity of indiscernibles state that two objects sharing the same identity im-
plies that they share all of their properties and vice versa. Thus, two objects linked
through owl:sameAs are generally interpreted in terms that the properties of one object
holds for the other. Current research has shown that this meaning of owl:sameAs is
often too strict since the linked objects do not necessarily share all of their properties:
Is a ship before the replacement of some components the same as the ship after this
replacement [17]? Is Tim Berners-Lee as child the same as Tim Berners-Lee as adult
[18]? Is drug A the same as drug B when having the same structure but different vendors
[19]? To the best of the authors knowledge, the applicability of owl:sameAs is not dis-
cussed with respect to BIM and GIS, even though the lack of seamless one-to-one map-
pings between objects of IFC- and CityGML-models seams to conflict with the strict
meaning of owl:sameAs.
In this paper, related literature about the strict meaning of owl:sameAs and the align-
ment of heterogeneous information models from the domains BIM and GIS is investi-
gated. Afterwards, the information models IFC, CityGML and the alignment language
OWL are described. In the fourth chapter, the meaning of identity links like owl:sameAs
is discussed and different types of properties for which Leibniz’ laws holds/ does not
hold are introduced. In the subsequent chapter, two examples illustrate difficulties ac-
companying the usage of identity links for linking IFC and CityGML. Last, these dif-
ficulties are summarized and conclusions for the usage of identity links like owl:sameAs
for linking heterogenous information models from the domains BIM and GIS are pro-
vided.
2 Related Literature
2.1 Heterogeneities between IFC and CityGML
The literature review about heterogeneities between IFC and CityGML models indi-
cates and explains mismatches between the two kinds of information models. In sum-
mary, there are three different approaches which describe heterogeneity between IFC
and CityGML at both instance- and schema level. The first approach lists particular
differences from a rather general perspective [3, 4, 20–23]. In contrast to that, another
approach describes heterogeneities by means of defined structures like interoperability
models [3]. The third approach compares entities of IFC and CityGML at schema-level
[7, 10, 11]. Regarding instance-level heterogeneities, there are two major aspects that
are relevant for the discourse of the paper: First, the instantiation is often based on
different types of data acquisition. While IFC models are often modelled manually in
the design phase (prescriptive), CityGML models are often created based on surveying,
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remote sensing or photogrammetry (descriptive) [8, 20, 24]. Therefore, IFC models
often cover idealized geometrical descriptions, while CityGML models often represent
visible geometries in analogy to the real-world asset. Second, the underlying modelling
paradigm differs due to different purposes and types of data acquisition [8, 20, 24].
Building elements in CityGML are geometrically represented by means of boundary
representations, while building elements in IFC are generally described as solids cre-
ated by means of explicit or implicit geometrical representations.
2.2 Instance-level links between IFC and CityGML
There a variety approaches in research to link instance-level information from IFC and
CityGML models: For instance, Hijazi et al. [25] use a relation table representing
GMLIDs of CityGML buildings and the related subproject of an IFC model. Hor et al.
[26] map corresponding objects of IFC and CityGML models based on Semantic Web
Technologies using the link predicates Equivalent, As-is, and Has an attribute. Huang
et al. [27] relate window objects represented as ifcOWL/BOT and CityGML use the
link predicate skos:exactMatch from the SKOS vocabulary. Vilgertshofer et al. [28]
map the globalID of an IFC element to the gml:id of CityGML element using Semantic
Web Technologies without explicitly mentioning any link predicate. Stepien et al. [29]
make use of topological relationships to link several different models like city models
and cadastral maps to an infrastructure alignment of a tunnel using Semantic Web tech-
nologies.
3 Information Models and Alignment Languages
The information model Industry Foundation Classes (IFC) supports the vendor-neu-
tral information exchange throughout the life cycle of building assets and is associated
with the domain BIM [30]. Its development is primarily supported by buildingSMART.
The instance information can be represented through different formats like Standard
Presentation Format (SPF) (ISO 10303-21). The schema of IFC was translated to the
ifcOWL to make it accessible in the field of Semantic Web [31]. However, ifcOWL is
conceived as inadequate for linking information following the idea of Semantic Web,
e.g. due to its complexity and size [32]. Instead, the Building Topology Ontology
(BOT) was developed to overcome these drawbacks [32]. On the other side, CityGML
[33, 34] aims to support modelling, storage, and exchange of city models and refers to
the domain GIS. It is based on Geographic Markup Language (GML) (ISO 19136) and
primarily developed by Open Geospatial Consortium (OGC).
Alignment languages are intended to represent sets of correspondences between en-
tities of information models at either or both levels instance and schema. Examples for
alignment languages are OWL [15], Expressive and Declarative Ontology Alignment
Language (EDOAL) [35], Semantic Web Rule Language (SWRL) [36] and Context-
OWL (C-OWL) [37]. Among others, alignment languages differ regarding their ex-
pressivity and compatibility to formal languages [12]. In this paper, the alignment lan-
guage OWL is discussed due to its prevalence use in the field of Semantic Web. OWL
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is based on Resource Description Framework (RDF) such that linking of IFC and
CityGML requires the syntactical translation from their native format to RDF [31, 38].
OWL is a logic-based language and its current version OWL 2 was developed by World
Wide Web Consortiums (W3C) [37]. OWL Description Logic (DL) is a prominent form
of OWL and allows to link entities at schema- and instance-level following subset of
first order logic SHOIN(D).
4 Semantics of Identity Links
The expressivity of SHOIN(D) allows to relate individuals by means of an identity re-
lation, noted in OWL as “owl:sameAs” and is further expressed by the symbol “=”.
Logical identity is only valid between a thing and itself. Thus, two things cannot be
considered as identical from logical perspective even though referring to the same real-
world object. An approach to formalize identity is provided by Leibniz’ principle ‘The
indiscernibility of identicals’ and its counterpart ‘The identity of indiscernibles’ [16].
The former states that x = y implies that x and y have all the same properties, described
in eq. (1). Vice versa, the latter principle states that x and y have all the same properties
implies x = y, described in eq. (2). The combination of both principles is circular [19],
since x and y are identical if and only if the objects have all the same properties. In
summary, the Leibniz’ principles supports rather the identification of non-identical ob-
jects since the differences between the respective properties imply that the identity re-
lations cannot be established [19].
∀x ∀y [x = y → ∀F (𝐹𝑥 ↔ 𝐹𝑦)] (1)
∀x ∀y [ ∀F (𝐹𝑥 ↔ 𝐹𝑦) → x = y] (2)
For the following discourse about the applicability of identity relation for linking IFC
and CityGML models, three different sets of properties are introduced:
• Ψ: Set of properties for which the first principle (1) is valid. In other words, Ψ covers
properties for which propagation is allowed.
• Φ: Set of properties for which the first principle is invalid.
• Π: Set of properties for which a less strict meaning of the second principle (2) is
valid. The notion “less strict” means that two properties might indicate a match (are
element of Π) even though the properties are not exactly the same. Roughly spoken,
Π refers to properties on which basis the matching is carried out.
Notably, a property must be element of either Ψ or Φ but can be element of Π. These
sets of properties are similar to those defined by Idrissou et al. [39] except of the aspect
that the first principle of Leibniz’ law is not necessarily valid for properties of element
of Π. This is because, properties might be sufficient for matching (Π) but not considered
as valid for the corresponding object (Φ), e.g. the length values of two objects might
indicate a match but differ such that they are not necessarily valid for the respective
corresponding object.
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5 Identity Links across IFC and CityGML
5.1 Overview
The discourse about identity links across IFC and CityGML models is illustrated by
two examples. In these examples, a fictive building (FZK House), is represented as
IFC1 and CityGML2 model. The CityGML model provided by KIT was subsequently
modified such that only visible surfaces are represented. The reduction of the model to
visible surfaces simulates acquisitions methods, like laser scanning or photogrammetry.
The different kinds of data acquisition and different underlying modeling paradigms
result in differences concerning geometrical dimensions (section 2.1) of a beam (Ex-
ample A) and walls (Example B). Notably, the geometric dimensions (e.g. length and
width) are not explicitly available in CityGML but can be calculated based on the avail-
able information.
5.2 Example A: Beam
In Example A, the length of beam object in the IFC model (IfcBeam) and the corre-
sponding beam object in the CityGML (BuildingInstallation) model differs (Fig. 1).
While the length of the IfcBeam refers to the true length, the length of the BuildingIn-
stallation refers to the visible length of the same real-world beam. Furthermore, also
the visible length values of both objects differ due to different data acquisition methods.
Similar to the length of the BuildingInstallation, the visible length of the IfcBeam is not
stored explicitly in the model but can be calculated.
The assignment of the properties concerning the length to Ψ/ Φ is not clear since it
is questionable whether the properties can be considered as valid for the corresponding
object. Is the property describing the true length of the IfcBeam valid for the Build-
ingInstallation, even though the true length is not addressed by BuildingInstallation? Is
the visible length of the IfcBeam valid for the BuildingInstallation, even though its
value differs from the value of the visible length of the BuildingInstallation? Thus, de-
pending on the perspective, which relies on the underlaying purpose, it might be as-
sumed that the length values are part of either Ψ or Φ.
5.3 Example B: Wall
In the second example, IFC- and CityGML objects referring to the same real-world wall
shall be related (Fig. 2). In the IFC model, two walls in top of each other are expressed
through two IfcWall objects. In contrast to that, the corresponding object in CityGML
describe the visible surfaces of the walls by means of the objects InteriorWallSurface
and OuterWallSurface. The OuterWallSurface of the CityGML corresponds to IfcWall
objects and the InteriorWallSurfaces correspond to the respective wall objects of the
IFC model.
1 https://www.ifcwiki.org/index.php?title=KIT_IFC_Examples (last access on 16th of May)
2 https://www.citygmlwiki.org/index.php?title=FZK_Haus (last access on 16th of May)
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Similar to the previous example, the geometric values differ due to modeling para-
digm and types of data acquisition. Again, the deviating geometric values means that
the properties cannot be clearly assigned to a specific set of properties. Moreover, the
described identity relations between the IFC and CityGML models allow to infer that
the lower IfcWall_1 is the same as the upper InteriorWallSurface_2. For sure, this re-
lation is misleading for several properties like the height of IfcWall_1 (Φ) but might be
true for other properties like the width of the IfcWall_1 (Ψ).
Fig. 1. Beam object in IFC (left) and CityGML (right) with different length values caused by
different modeling paradigms and data acquisition methods
Fig. 2. Wall objects in IFC (left) and CityGML (right) with different dimensions caused by dif-
ferent modeling paradigms and data acquisition methods
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6 Alternative approaches
The problem concerning the strict meaning of owl:sameAs is a well-known issue in the
field of Semantic Web Technologies [40] and several approaches were investigated to
overcome this issue. In summary, there are three different approaches aiming to specify
the links between entities:
• First, some approaches introduce alternative vocabulary to owl:sameAs. For in-
stance, the link predicate rdfs:seeAlso does not suggest full identity between the
linked entities but indicate that the related entity provide additional information. Fur-
thermore, Simple Knowledge Organization System (SKOS) [41], Context-OWL (C-
OWL) [37], and Similarity Ontology (SO) provide vocabulary which is less strict
compared to “owl:sameAs”. As an example. C-OWL covers five different relation
types, namely disjoint, equivalence, overlap, general and more specific. Further al-
ternative vocabularies are topological relationships as they are covered by Geo-
SPARQL and BimSPARQL [42]. Overall, these kinds of approaches have in com-
mon that their semantics is rather too weak than too strict: What does it mean when
two building objects are related? Too which degree do two building objects inter-
sect? Thus, making conclusions following these links require deep knowledge about
the linked information.
• Second, another approach focuses on the classification of properties to ensure iden-
tity according to Leibniz’ law. For instance, Raad et al. [43] and Beek et al. [19, 43]
reduce an RDF-graph such that Leibniz’ laws holds in the context of the resulting
subgraph [19, 43]. As mentioned previously, Idrissou et al. [39] propose to distin-
guish between three set of properties that are relevant for the meaning of an identity
link: Properties for which both principles of Leibniz’ laws hold, properties for which
only the second principle hold and properties for which none of the principles is
valid. To the best of the authors knowledge, the idea of Idrissou et al. [39] is con-
ceptual but has never been implemented. The approaches following the classification
of properties have to deal with several issues: For instance, whether a principle of
Leibniz’ law hold for a property depends on the perspective of the user. Does the
length value of the IFC beam hold for the CityGML beam even though their values
differ due to different data acquisition methods? Furthermore, these approaches are
limited to the comparison of properties of the corresponding objects, while other
information like topological information might be also relevant for the semantics of
links. Last, the category to which a property belongs needs to be explicit what result
in a large number of additional triples.
• Third, some approaches aim to enhance specific links or sets of links through addi-
tional data. For instance, the alignment language Expressive and Declarative Ontol-
ogy Alignment Language (EDOAL) [35] specifies patterns for correspondences
(called cells), where the correspondences can be enriched by additional data. Similar
to cells, C-OWL make use of so-called bridge rules. Furthermore, VoID [44] pro-
vides vocabulary for adding meta data to a set of links (like matching algorithms, or
authorship). Further examplary approaches aiming to enrich links with meta data are
SingletonProperty [45], Named graphs [46], and RDF* [47]. As an example, Guha
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[10] and McCarthy [15] propose graph-context pairs to state that the graph holds
only in a specific context. Idrissou et al. [39] use Singleton Properties and VoID to
enrich links with information about matching algorithms to provide more meaning-
ful links. Furthermore, Aljalbout et al. [48] propose OWLC and enrich links with
contextual information to limit the validity scope of these links. However, following
this approach the information relevant for the semantics of the links must be explic-
itly defined what is a challenging task.
In summary, these approaches have in common that they do not exhaustively define the
semantics of the links. Instead, the semantics of the links is left to the interpretation by
the user. What does it mean when two objects are related (first approach)? In which
context are properties assigned to Leibniz’ second principle of identity of indiscernibles
(second approach)? How is the semantics of the link affected when it is enriched with
information about its matching algorithm (third approach)? In short, alternative linking
approaches have the shortcoming that they rely on the correct interpretation by the user.
7 Conclusion
The discourse about identity relations like owl:sameAs for linking building elements of
heterogeneous instance models has shown that these kinds of relations are prone to be
misleading. This is because identity links suggest that the corresponding objects are
exactly the same thing what means that everything stated about one object hold for the
corresponding object. Roughly spoken, both objects must share all of their properties.
However, the properties describing objects like building elements differ due to several
reasons. Most obvious, they differ regarding some meta data like authorship or time of
creation. Furthermore, the properties of corresponding building elements differ due to
different types of data acquisition, and modelling paradigms as illustrated in the previ-
ous examples. Consequently, alternative approaches for linking building elements of
heterogeneous instance models need to be utilized to overcome the misleading charac-
ter of identity relations.
In the field of Semantic Web, there are three different approaches aiming to over-
come the misleading character of identity relations: alternative vocabularies, categoriz-
ing information, and enriching links with meta- or contextual data. However, these ap-
proaches do not exhaustively define the semantics of the links but leave the semantics
of links as a matter of interpretation.
Conclusively, the semantics of links relating heterogeneous information models
from the domains BIM and GIS needs to be exploited in more detail in future research
investigations. A more detailed understanding of these kinds of links would support the
development of methods aiming to link heterogenous information models from the do-
mains BIM and GIS in a more efficient manner.
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