BIM and GIS Integration and Interoperability Based on Semantic Web Technology

BIM and GIS Integration and InteroperabilityBased on Semantic Web Technology

Ebrahim P. Karan1; Javier Irizarry, M.ASCE2; and John Haymaker3

Abstract: When making design and construction decisions, planners must consider information from different scales and domains.Currently, building and geospatial data are shared and exchanged through a common data format, such as industry foundation classes (IFC).Because of the diversity and complexity of domain knowledge across building information modeling (BIM) and geographic informationsystem (GIS) systems, however, these syntactic approaches are not capable of completely sharing semantic information that is unique in eachsystem. This study uses semantic web technology to ensure semantic interoperability between existing BIM and GIS tools. The proposedapproach is composed of three main steps: ontology construction, semantic integration through interoperable data formats and standards, andquery of heterogeneous information sources. The completeness of the methodology is validated through a case study. DOI: 10.1061/(ASCE)CP.1943-5487.0000519. © 2015 American Society of Civil Engineers.

Author keywords: Building information modeling (BIM); Geographic information system (GIS); Semantic interoperability; Semantic webtechnology.

Introduction

Building information modeling (BIM) represents building elementssuch as beams, columns, and walls as smart three-dimensional (3D)objects that include embedded data such as geometry details,energy use data, and lifecycle cost information. BIM provides de-tailed information for designers and managers and can help answerquestions such as: What is the quantity of each building object orcomponent? Can a given building design be constructed withinbudget? What is the impact of a given design change on the overallproject scope and schedule? Engineers in the design and construc-tion community can use BIM to manage design geometry and visu-alize the model in two-dimensional (2D) and 3D views and togetherwith the fabricators and owner share and assess various designoptions from cost, constructability, and engineering perspectives.Sometimes due to the lack of spatial analysis capabilities in BIM,building data are incorporated in the form of an input into a geo-graphic information system (GIS) tool to support the diversity ofspatial relationships between topographic and temporary objects.While this integration indicates the presence of a gap in analyzingand processing spatial data within a BIM system, it also indicatesthe potential value of an integrated BIM-GIS model that can beused to enhance the current practice of data sharing between thetools used in the procurement and construction processes.

The GIS has been used successfully to solve the complexities ofpreconstruction planning and to support the wide range of spatialanalysis used in the logistics perspective of the construction activ-ities. It enables addressing questions such as: Where are the optimallocations for the temporary facilities on a construction site? Howcan the construction materials be tracked and monitored throughtheir supply chain? Where are the dangerous or hazardous areason a jobsite? The integration of BIM and GIS can offer substantialbenefits to manage the planning process during the design and con-struction phases. While BIM systems focus on developing objectswith the maximum level of detail in geometry, GIS are applied toanalyze the objects, which already exist in the physical environ-ment, in a most abstract way. The major difficulty in integratingBIM and GIS systems reflects their incompatibility such as themodeling environment and reference system (e.g., GIS data aregeoreferenced and usually two dimensional while the BIM data arethree-dimensional objects located within local coordinate systems).

Although these two technologies have evolved from distinctlydifferent beginnings, both can benefit from each other if they couldexchange data effectively. As BIM technology is mainly centeredon indoor environments, GIS can extend the benefits and appli-cability of existing building models to the outdoor environment.However, it is not an easy task to transfer data from BIM to GISor vice versa without consideration of data format and meaning.Current state-of-the-art BIM (or GIS) tools enable the data ex-change between the systems by using a common data format.Therefore, the users are able to access data from a different softwareprogram and exchange data within the BIM (or GIS) domain. How-ever, it requires the user to have a thorough understanding of bothsystems and their functionalities. The integration tools and currentstandards lack the ability to help the user to convey meaning, whichis interpretable by both construction project participants as well asBIM and GIS tools. In order to fully integrate GIS and BIM, there isa need to provide interoperability at the semantic level.

The current approach to exchange and share building databetween BIM applications is based on the exchange of industryfoundation classes (IFC) files. While this approach was, and stillremains, an effective way to hold and exchange data among variousparticipants in a building, construction, or facility management

1Assistant Professor, Dept. of Applied Engineering, Safety andTechnology (AEST), Millersville Univ., 40 East Frederick St., Millersville,PA 17551 (corresponding author). E-mail: [email protected]

2Associate Professor, School of Building Construction, GeorgiaInstitute of Technology, 280 Ferst Dr., 1st Floor, Atlanta, GA 30332.E-mail: [email protected]

3Assistant Professor, Schools of Architecture and Building Construc-tion, Georgia Institute of Technology, 280 Ferst Dr., 1st Floor, Atlanta,GA 30332. E-mail: [email protected]

Note. This manuscript was submitted on November 5, 2014; approvedon June 16, 2015; published online on July 22, 2015. Discussion periodopen until December 22, 2015; separate discussions must be submittedfor individual papers. This paper is part of the Journal of Computingin Civil Engineering, © ASCE, ISSN 0887-3801/04015043(11)/$25.00.

© ASCE 04015043-1 J. Comput. Civ. Eng.

J. Comput. Civ. Eng.

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project, it does not provide semantic-based representation ofknowledge for another domain (e.g., geospatial domain), and thuslimits the capability of inferring additional knowledge (Törmä et al.2012). Therefore, the use of BIM outside of architecture, engineer-ing, and construction (AEC) domains or the few engineering do-mains that use IFC, and more specifically EXPRESS modelinglanguage, is very limited. Description logics provide means formanaging semantic contents and representing distributed knowl-edge in a given domain of application (Zhang et al. 2007). Theway description logics are able to describe an application domain,in terms of its concepts (or classes) and their properties and rela-tionships (or roles), forms a formal foundation for modern ontologylanguages. Modern ontology languages such as the resource de-scription framework (RDF) and web ontology language (OWL)are based on description logics. As description logics describe thedomain in terms of concepts, roles, and individuals, OWL describesthat in terms of classes (instead of concepts), properties (instead ofroles), and individuals. In particular, the formal specification of theOWL was influenced by description logics and its RDF/XML ex-change syntax was influenced by a requirement for upwards com-patibility with RDF (Horrocks et al. 2003).

Research Objectives

The main contribution of this research is the development of adata framework that not only connects data and users, but alsoexchanges information in a meaningful way between BIM andGIS tools. This study builds on previous work on the developmentof an integrated and interoperable BIM and GIS model (Karan andIrizarry 2015). The objectives of mapping BIM to GIS in thispaper are two-fold. The first is to develop and test an ontologicalframework that can be used to exchange information between twoheterogeneous databases: BIM hierarchy structure and GIS rela-tional database. A second objective is to alleviate the need for afull understanding of the IFC schema in GIS in order to retrieve therequired information. In the early years, several applications fromboth domains were developed to bring the benefits of BIM andGIS technologies together. An integrated GIS-BIM model was pre-sented in an earlier study (Irizarry et al. 2013) manifesting the flowof materials, availability of resources, and map of the respectivesupply chains visually. As claimed by the authors, the proposedsystem suffers from a lack of sematic interoperability across theGIS and BIM domains and it requires the user to have knowledgeabout both systems and their functionalities. For example, afterimporting an IFC file to GIS, the user needs to know how BIMinformation is represented in the GIS model. Although a centralMicrosoft Access database was used for transferring attribute databetween BIM and GIS, this approach is inefficient and lacks se-mantic interoperability. In the validation section, the same casestudy is considered to show the benefits gained from the use ofsemantic interoperability.

In later years, when researchers and practitioners became famil-iar with existing BIM and GIS standards, several data exchangeformats were developed to support interoperability. IFC for GIS(IFG) data model and buildingSMART Data Dictionary (formerlythe International Framework for Dictionaries or IFD) are two greatexamples of efforts for the integration of BIM models with otherengineering domains, such as GIS (IFD 2014; IFG 2014). Whilethe aim of IFG is to exchange building and GIS data throughimporting or exporting a single data type, there are many hetero-geneous classes for representing building and geospatial informa-tion. Thus, applying a different interoperable format for bothbuilding and GIS classes is more reasonable. The IFD Data

Dictionary provides the meaning of information that is exchanged.The present approach makes use of and builds on the representationof the objects and their relationships provided in the IFD DataDictionary to tag all the information in the IFC format with aGlobally Unique ID (GUID).

What distinguishes this research from previous work is thatthe proposed framework incorporates all three main steps towardsemantic interoperability to ensure the highest level of interoper-ability between existing BIM and GIS technologies; ontology con-struction, semantic integration through interoperable data formatsand standards, and query of heterogeneous information sources.This is achieved by applying semantic web techniques, which actas the medium through which BIM and GIS data can be shared,understood, and processed by both tools. The ontology construc-tion and semantic integration are selected as the focus of this study.Thus, the literature review is divided into two sections; First, someof the benefits derived from the integration of BIM and GIS areprovided. Then, the second section provides further details on theontology-based approaches and semantic web technology.

Literature Review

BIM and GIS Integration

Building information models provide a very rich data source forproperties about all of the building elements (e.g., identificationinformation, maintenance information, and lifecycle condition-based information) that are inevitable components of any construc-tion project. Moreover, descriptive information (e.g., transportationnetwork, asset locations, etc.) in GIS should be used to model tem-porary components, locate temporary facilities, reduce transporta-tion and logistics costs, and many other applications. Some of thepotential applications that a construction manager can expect fromintegrating BIM and GIS are described in Table 1.

To fully utilize the benefits of BIM, surrounding landscape-leveldata are also required. However, modeling of the site topographystill remains highly labor-intensive and relies extensively on theproject site surveys. Karan et al. (2014) explored an alternative ap-proach for generating a digital model of site topography, in whichremotely-sensed data are used with GIS analyses. Isikdag et al.(2008) investigated the applicability of BIMs in the geospatial envi-ronment in order to facilitate the data management in site selectionand fire response management processes. Having a 3D model of the

Table 1. BIM-GIS Applications in Different Stages of a ConstructionProject

Project stage BIM/GIS application

Design Digital modeling of building and landscape-levelcomponents (Karan et al. 2014)Site selection and fire response management(Isikdag et al. 2008)

Preconstruction Identifying optimal number and location of towercranes (Irizarry and Karan 2012)

Construction Evaluation and visualization of constructionperformance (Elbeltagi and Dawood 2011)Construction supply chain management (Irizarryet al. 2013)

Operation Detecting and mapping utility networkinformation (Liu and Issa 2012)Facility management supply chain(Karan and Irizarry 2014)



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building along with its surroundings would allow managers toefficiently design a site layout and identify optimal locations fortemporary facilities. Irizarry and Karan (2012) presented a newapproach for integrating GIS and BIM that enables managers tovisualize the 3D model of tower cranes in their optimal locations.In their research, GIS is used to develop a crane location model andBIM is employed to develop building models and to visualize theresults of the GIS model in a 3D virtual world.

In order to support construction supply chain management,Irizarry et al. (2013) integrated BIM and GIS into a unique system,which enables keeping track of the supply chain status and provid-ing warning signals to ensure the delivery of materials. Elbeltagiand Dawood (2011) developed a BIM-GIS visualization systemto evaluate construction performance and to facilitate monitoringof repetitive construction progress. As one of the last stages, facilitymanagement is about planning and managing the life cycle of abuilding. Obviously, facility managers need a massive amount ofinformation for their work. Karan and Irizarry (2014) developed aspatial BIM-GIS framework, which can offer facility managers anintegrated tool to manage the maintenance and repair processes offacility management. Liu and Issa (2012) utilized both technologiesfor detecting and mapping pipe network information. Although thestudy mainly relied on BIM and GIS visualization capabilities, itshowed how facility managers can benefit from an implementationof BIM in geospatial context.

Ontology-Based Approaches for Improving theInteroperability

There have been many applications of ontology-based approachesin civil and construction engineering. Yurchyshyna and Zarli(2009) presented an ontology-based method for the formaliza-tion and application of construction conformance requirements foreffective code checking. Wang and Boukamp (2009) adopted onto-logical modeling to organize essential concepts of job hazard analy-sis knowledge and identify applicable safety rules. Elghamrawyet al. (2009) developed a framework that relies on the use of con-cept ontologies for describing and indexing the construction-problem context information captured through the use of RFID.In a similar effort, Sørensen et al. (2010) created a digital linkbetween the virtual models and the physical components in the con-struction by means of RFID technology and reviewed existingontologies for information sharing between trading partners, easyaccess of information, and reading of data stored in electronic tags.Wang et al. (2011) proposed an ontology-based approach to facili-tate the management of context-sensitive construction informationthat is stored in different textual documents. Zhong et al. (2012)proposed an ontology for construction quality inspection and evalu-ation, CQIEOntology, for improving the support to the constructionquality inspection and management. Park et al. (2013) proposed aconceptual system framework for the proactive defect managementwith three interrelated system solutions, namely defect data col-lection template, defect domain ontology, and augmented reality.Another study used BIM data and ontology to automate the selec-tion and matching work items to the elements of buildings and theirmaterials (Lee et al. 2014). Karshenas and Niknam (2013) devel-oped an ontology-based approach to facilitate project informationsharing between design and estimating domains.

Anumba et al. (2008) reviewed examples and case studies ofontology-based information and knowledge management systemsin the construction delivery process and found that middleware ap-plications, such as semantic web, have the potential to meet some ofthe technical challenges inherent in the development and use ofontologies for construction information. The e-COGNOS Project

(Consistent knowledge management across projects and betweenenterprises in the construction domain) was one of the first attemptsto develop a comprehensive ontology-based system in the construc-tion domain (Lima et al. 2003). Some of the previously-developedclassifications and taxonomies (e.g., the IFC model, the BritishStandard Glossary of Building and Civil Engineering terms, theUniclass, and the W3C DAML+OIL language) were adopted andreused to support the consistent knowledge representation of con-struction items (Lima et al. 2005). Toward this objective, El-Dirabyet al. (2005) presented a domain taxonomy that was developed aspart of the e-COGNOS project. Another study (Wang and Xue2008) adopted the e-COGNOS and presented an ontology-basedsemantic blogging system to facilitate information categorizationand retrieval. Based on this project, El-Gohary and El-Diraby(2010), and El-Diraby and Osman (2011) presented an ontologyfor the infrastructure and construction domain that relates to con-struction aspects of infrastructure products.

Despite the successful applications of e-COGNOS identified intheir research, the next step is to develop a formal ontology forconstruction that allows a user to share and manage domain knowl-edge. e-COGNOS is focused on what is called domain ontology,which represents shared concepts in the AEC domain. However,to integrate different types of information, a higher level of detailsis necessary within the ontology. If we define taxonomy as a setof terms and their definitions that are organized by a hierarchy,ontology provides a framework for representing a concept by itsposition in the hierarchy and its relationships to other concepts.The result of the e-COGNOS project was a pure taxonomy thatonly contains construction terms and their relations in a taxonomictree. Further research is required to explore the full capability andbenefits of mapping this construction-specific taxonomy with otherontologies.

Despite the contributions and practical features of theseontology-based approaches, there is still no guarantee that hetero-geneous information sources can be integrated into one system.Research on the potential application of ontology-driven approacheson the integration and interpretation of heterogeneous informationresources has been recognized only recently in the AEC literature.One of the few relevant researches is the attempt of El-Gohary andEl-Diraby (2011) to develop an ontology merger (Onto-Integrator)based on semantic similarity comparison methods to merge con-cept taxonomies and ontological relations of source ontologies intoan integrated combined ontology. Previous attempts of ontologydevelopment in the AEC have undoubtedly paved the way forseamless integration of building and construction related data,however, no application ontology exists for the building and con-struction domain that encompasses all IFC classes with differentattributes. A gap exists on how to represent BIM and GIS data (andtheir semantics) in such as a way that can be shared, understood,and processed by both tools. The objective of this paper is to bridgethis gap through the development of an application ontology thatcontains a set of building and geospatial terms (e.g., building ele-ments, topography, and geolocations) and their semantics.

Research Methodology

The methodology for extending BIM interoperability to the geospa-tial domain consists of five stages. First, an IFC-compliant ontologydescribing the hierarchy structure of BIM objects, their relation-ships, and their properties is developed. The emphasis is on seman-tic indexing and retrieval of building information from an IFCmodel. This study makes use of existing GIS ontologies that ob-viate the need for the transformation of GIS schema into ontology.



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Second, ontology mapping is used to link similar relationshipsor concepts between the source (e.g., BIM) and target (e.g., GIS)ontologies. The output is an extended ontology that contains allclasses and properties from both BIM and GIS domains that arerelevant to the case study. Then, building’s elements and GIS dataare translated into semantic web standards, and thus can be proc-essed by semantic web applications. Once the information has beengathered from different sources and transformed into an appropriatesemantic web format, the SPARQL query language is used in thefourth section to retrieve this information from a dataset. Finally,the completeness of the methodology is validated through a casestudy. Fig. 1 demonstrates the process of establishing semanticinteroperability and integration and the activities supported by eachstage. Each step in the process is explained in detail in the followingsections. This paper adopts ontology mapping methods that arebeing increasingly used to map BIM and GIS ontologies in thesecond step, Integration, and the process developed by Karanet al. (2015) to translate semantic web query results into the XMLrepresentations of the IFC schema and data in the fourth step,Manipulation.

Step 1: Conceptualization

This section describes how the IFC schema can be elevated to anontological level by using description logic. Depending on thecomponents and the level of detail, ontologies can be divided intogeneric (or upper), domain, and application ontologies. A genericontology describes general concepts such as space, time, role,object, action, etc., which are applicable across a wide range ofdomains. Domain ontology is created with the aim of formalizingand representing shared concepts in a specific domain of interest(e.g., AEC). For instance, a rule about a specific role can be rep-resented: A construction worker uses a tape measure to take a meas-urement; where construction worker is an instance of the conceptworker, tape measure is an instance of the concept measuring tool,and uses and take a measurement are used to identify relationsbetween these conceptual elements. The e-COGNOS (as well asmany other examples mentioned earlier) is a good example ofsuch ontologies. Application ontology is a representation of the se-mantics of a specific, focused application domain, which definesrelevant concepts for a particular application (e.g., BIM or GIS).Given the increasing role of application ontologies in facilitatingthe integration of different types of information, this step examineshow this level of ontology can be used to provide semantic inter-operability between BIM and GIS operations. The focus is on IFCschema items such as attributes, classes, data types, individuals,and relations.

The semantic web community has shown increasing interest inadopting description logic as the formal paradigm to represent theapplication domain in a structured way. Briefly, description logicmodels the application domain by defining the relevant conceptsof the domain and then using these concepts to specify propertiesof objects and individuals occurring in the domain (Studer et al.2007). Also, in simple terms, description logic describes the do-main in terms of concepts, roles (such as relationships and proper-ties), and individuals (or instances). For conceptual modeling, theproposed method organizes (or models) the building and construc-tion concepts by using description logic definitions. There are twotypes of concepts. First, primitive concepts are used in this study torepresent the natural classes of the IFC domain where only neces-sary conditions are specified and they can be recognized by theirdefinition. Second, defined concepts are used to represent sub-classes of the primitive ones (i.e., built using primitive concepts andproperties). Thus, the authors define the IFC classes at the top ofthe hierarchical structure of the ontology as primitive concepts.For example, assume that an individual x is an instance of Ifc-Window (as a primitive concept), thus x possesses the properties ofIfcWindow such as overall height and overall width. The standardwindow, which is inserted into an opening and its profile repre-sents a rectangle within the 2D plane of the opening, is definedby IfcWindowStandardCase. The authors define this IFC entityas a defined concept, so the associated properties of the IfcWindow-StandardCase are necessary and sufficient. Again, assume that anindividual y is an instance of IfcWindowStandardCase (as a definedconcept), thus y possesses the properties of IfcWindowStandard-Case and the y individual that possesses the set of the associatedproperties of IfcWindowStandardCase (e.g., inserted into an open-ing, etc.) suffices to be inferred as an instance of the IfcWindow-StandardCase.

For the purpose of forming the ontology, the authors define theIFC classes (or concepts in description logic) by their supertypeentities and their relations with the other classes. Continuing withthe above examples, IfcWindow can be defined as shown in Fig. 2.

It defines a primitive concept IfcWindow, which is a subtypeof IfcBuildingElement. The keyword defprimclass is used todefine the primitive concepts and introduce a set of necessarybut not sufficient conditions. This expression also states that allIfcWindow classes have at least oneOverallHeight that is a positivemeasure, greater than zero. The defined concept IfcWindow-StandardCase is defined as [defconcept IfcWindowStandardCase(?w IfcWindow) : : : ], where defconcept creates named descriptionsthat describe sets or classes of objects.

The authors use OWL ontologies to create the application on-tology (hereafter referred to as the BIM ontology). The OWLaxioms provide semantics about classes and properties by assigningnecessary and/or sufficient characteristics to a class. The Sub-ClassOf axiom represents subclass/superclass relationship, so sinceIfcWindow is a subclass of IfcBuildingElement, it necessarily in-herits all characteristics of IfcBuildingElement, but not the otherway around. The primitive concepts introduced by defprimconceptare translated to OWL with subclassOf axioms. The Equivalent-Classes axiom states that two or more class expressions consistof the same set of individuals, so they are equivalent to each other

IFC classes and hierarchy of

building objectsConceptualization

BIM ontology builtin OWL/RDF Integration

GIS ontology

Integrated (mapped) BIM-GIS ontology

Available URIs

Formalization

IFC attributes

BIM and GIS Data as RDF Files Manipulation

IFC-Compatible Query Results Validation

Input OutputStep

Fig. 1. Research methodology process

(defprimclass IfcWindow (?be IfcBuildingElement):=> (and (exists (?oh)

(and (OverallHeight ?oh)(>= (OverallHeight ?oh) 0)))

Fig. 2. IfcWindow defined in description logic



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and the subclass relationship is implied to go in both directions.Expressions using defconcepts in description logic correspond tothe equivalentClasses in OWL.

The authors describe the structure of the BIM ontology in termsof a graph, where each IFC entity is a node and edges are the re-lationships between the IFC entities and properties. In this graph,the authors define owl:Thing as the root class and then owl:Entityas the most general class that contains all IFC entities. The elementsin OWL ontologies are identified using Uniform Resource Identi-fiers (URIs), so the authors adopted available URIs to annotate theEXPRESS entities and relations among them (Van Deursen 2010).Fig. 3 shows an example of the BIM ontology as an RDF graph.

Step 2: Integration

Ontology mapping is used in this step to determine semanticallycorresponding entities among BIM and GIS ontologies. Using anexample, the authors provide a brief description of the process. Forfurther details refer to Balachandar et al. (2013) and Hu et al.(2005). Assume that individuals (as BIM users) want to integratetopographic data that are defined in GIS ontology from the Centerof Excellence for Geospatial Information Science (CEGIS) (USGS2013) with their BIM ontology. In order to measure the structuralsimilarities between BIM and GIS ontologies, the authors adoptGraph Matching for Ontologies (GMO) approach. This ontologymatching approach uses RDF bipartite graph model, which wasfirst introduced by Hayes and Gutierrez (2004), to represent ontol-ogies. Fig. 3 shows the RDF graph and bipartite graph of the GISontology. In the bipartite graph, property nodes are represented bycircles, class statements are represented by a rounded rectangle, andedge labels S, P, and O indicate their subject, predicate, and object.

The adjacency matrix of the directed bipartite graph of ontology,denoted by A, has the following block structure:

A ¼

0B@

0 0 AES

0 0 AS

AE AOP 0

1CA

where AES = matrix representing the connections from external en-tities (e.g., rdfs:subClassOf) to statements; AS = matrix represent-ing the connections from ontology entities (internal entities)to statements; AE = matrix representing the connections from state-ments to external entities of the ontology; and AOP = matrix rep-resenting the connections from statements to internal entities.

In the example in Fig. 3, the external entities include somecommon ones (e.g., rdfs:subClassOf, rdfs: isDefinedBy) used intwo ontologies. However, If those entities are not used as subjectsin the Ontology (as in the current example), AES is a zero matrix.The matrix representation of GIS ontology in Fig. 4 is shownin Fig. 5.

Now, it is possible to represent the similarity matrix of BIMontology entities to GIS ontology entities and the external entitiesof GIS ontology to the external entities of BIM ontology. Based onthe formulation in Hu et al. (2005), the structural similarity matrix

BuildingElementsubClassOf

subClassOf

Wall Window SlabsubClassOf

hasAttribute

hasAttribute

hasAttribute

WallStandardCasePartitioningTypeDatatype: String

PredefinedTypeDatatype: String

OverallWidthDatatype: Real

OverallHeightDatatype: Real

Fig. 3. Representation of an EXPRESS entity as an RDF graph

Topography

Surface Water

Terrain

Vegetation

rdf:resource#geopoint

GeoNames String

geo:latitudeDatatype: Real

geo:longitudeDatatype: Real

geo:altitudeDatatype: Real

subClassOfsubClassOf subClassOf

hasAttributehasAttribute

isDefinedBy isDefinedBy

isDefinedBy

USGS Circular

subClassOf

VegetationSurfaceWater

Topography Terrain

isDefinedByrdf:resource#geopoint

(a)

(b)

Fig. 4. (a) RDF graph (reprinted from Automation in Construction,Vol. 53, Ebrahim P. Karan and Javier Irizarry, “Extending BIM inter-operability to preconstruction operations using geospatial analysesand semantic web services,” pp. 1–12, Copyright 2015, with permis-sion from Elsevier); (b) bipartite graph of the GIS ontology example(instance labels have been omitted for clarity)

subClassOfisDefinedByhasAttributeVegetation 1 0 0 0Surface Water 0 1 0 0Topography 0 0 0 0Terrain 0 0 1 0#Geo:Point 0 0 0 1N1 1 0 0 0 0 1 0 0N2 1 0 0 0 0 1 0 0N3 1 0 0 0 0 1 0 0N4 0 1 0 0 0 0 0 1

Fig. 5. Matrix representation of GIS ontology



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of BIM and GIS ontologies is created. The resulting ontology de-termines the relation as well as correspondences between BIM andGIS ontologies.

Step 3: Formalization

In this step, IFC and GML files are converted into RDF graphsand generating RDF/OWL triples. The basic principle of schemamapping (i.e., transformation of data structure of source schema,e.g., BIM schema, into a different target schema, e.g., GIS schema)is that the building model is developed in BIM software (as anobject-oriented building development tool), exported as an IFC file(so we will have access to all the valuable metadata in the BIM file)and then transferred into GIS software as a metadata format forbuilding and geospatial data description. Unlike BIM, the coordi-nate system is georeferenced in a GIS environment. In order toaddress the issue of positioning the building within GIS context,spatial coordinates is defined and transformed from Cartesian toGeospatial (i.e., georeferenced) with the aid of a coordinate trans-formation matrix at the beginning of the process. The header of anIFC file states its name, description, translator version (if used), andschema version. The IFC entities and their attributes (both normaland optional) are specified in the body of the IFC file. Each IFCentity starts with a number sign (#) character followed by a number.Since every individual in OWL needs a unique identifier, the au-thors will use these unique numbers to define the common instan-ces or individuals of different classes. Each new IFC entity isdefined after an equal sign, (=), and its attributes are representedby a set of comma separated values within parentheses. The normalattributes for the IFC entity always get nonnull values, while theoptional attributes may have null values indicated by a dollar sign.Regardless of the type of attributes, each IFC entity is representedas the subject of the RDF statement using an owl:Class and use ardf:about statement to describe that subject. Also, the rdfs:sub-ClassOf property is used to state that one IFC entity is a subtypeof another entity or resource. Consequently, every OWL class is asubclass of owl:Thing.

An IFC attribute can be defined by a value parameter or an IFCentity. In order to reflect the attribute type, the IFC attributes aredivided into three groups in the conversion process: (1) leaf node,(2) simple type, and (3) complex type. A leaf node attribute is de-fined by a value parameter. For example, the IfcOrganization entityhas five leaf node attributes; Id (optional), Name (normal), Descrip-tion (optional), Roles (optional), and Addresses (optional). Thus,this IFC entity can be defined as #1= IFCORGANIZATION($, ‘Autodesk Revit 2014 (ENU)’, $,$,$). The IfcOrganizationentity is defined as shown in Fig. 6.

The type of literal data is defined by rdfs:range and rdfs:domainis used to state that the leaf node attribute is an instance of theIFC entity. Thus, the authors define the Name attribute of theIfcOrganization entity as shown in Fig. 7.

All the values for an OWL class should be written between itsopening and closing angle brackets. If the string value for the Nameattribute is Autodesk Revit, it should be declared as shownin Fig. 8.

An IFC entity is needed to define a simple type attribute. Forinstance, IfcApplication has one simple type attribute, Application-Developer, which is defined by IfcOrganization entity, and threeleaf node attributes; Version, ApplicationFullName, and Applica-tionIdentifier. In the OWL file, the authors use rdfs:isDefinedBy torepresent the simple type attribute. Also, rdfs:domain is used tostate that the simple type attribute is an instance of the IFC entity.The ApplicationDeveloper attribute of the IfcApplication is definedas shown in Fig. 9.

There are some IFC entities such as IfcNormalisedRatio-Measure, IfcRatioMeasure, and IfcSpecularExponent, that can bedefined with single attribute and without any relation to anotherIFC entity. Therefore, these distinct entities are not defined asan OWL class, instead they are defined an OWL data-type property.Similar to the role of rdfs: subClassOf in forming the taxonomyof IFC classes, taxonomy of properties is formed by rdfs:sub-PropertyOf. Since the distinct IFC entities are used to specify anIFC attribute, the authors define them as subPropertyOf other IFCattributes. For instance, IfcMeasureWithUnit entity has two simpletype attributes: (1) ValueComponent defined by IfcRatioMeasuredistinct entity, and (2) UnitComponent defined by IfcSIUnit entity.The ValueComponent attribute of the IfcMeasureWithUnit is de-fined as shown in Fig. 10.

One or more subattributes are needed to describe the propertiesand values of a complex type attribute. For instance, IfcPropertySethas one complex type attribute, HasProperties, which is definedwith a set of 1 to some subattributes (i.e., IfcPropertySingleValue).These subattributes are defined either by distinct IFC entities orby a set of individuals. Again, subPropertyOf is used to define thedistinct IFC classes. The IfcPropertySingleValue attribute of theIfcPropertySet is defined as shown in Fig. 11.

Fig. 12 shows the flowchart of IFC to RDF/OWL translationprocess for different types of IFC attributes. This process continuesuntil no more attribute exists for further translation as shownin Fig. 12.

<owl:Class rdf:about="..IfcOrganization"><rdfs:subClassOf rdf:resource="…IfcEntity"/>

</owl:Class>

Fig. 6. Representation of the IfcOrganization entity as an OWL

<owl:DatatypeProperty rdf:about="…Name"><rdfs:domain rdf:resource="…IfcOrganization"/><rdfs:range rdf:resource="&xsd;string"/>

</owl:DatatypeProperty>

Fig. 7. Name attribute of the IfcOrganization defined as OWL

<owl:NamedIndividual rdf:about="…Name"><…Name rdf:datatype="&xsd;string">Autodesk Revit 2014 (ENU)</…Name>

</owl:NamedIndividual>

Fig. 8. String value of the Name attribute defined as OWL

<owl:DatatypeProperty rdf:about="…ApplicationDeveloper"><rdfs:domain rdf:resource="…IfcApplication"/><rdfs:isDefinedBy rdf:resource="…IfcOrganization"/>


Fig. 9. ApplicationDeveloper attribute of the IfcApplication definedas OWL

<owl:DatatypeProperty rdf:about="…IfcRatioMeasure"><rdfs:subPropertyOf rdf:resource="…ValueComponent"/><rdfs:range rdf:resource="&owl;real"/>


Fig. 10. ValueComponent attribute of the IfcMeasureWithUnit definedas OWL



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Step 4: Manipulation

In this step, the authors use an RDF query language to extract datafrom an RDF/OWL file. A query like select DISTINCT? subject?property? value {?subject owl:Datatype Property OverallHeight.?subject? property? value.} would return the value components ofOverallHeight attribute in the dataset. The authors adopt the frame-work developed by Karan et al. (2015) to represent semantic webquery results as ifcXML building models. Thus, the resultingifcXML document can be loaded into a BIM authoring tool. Also,CSV format is used for expressing the results of a select queryin GIS.

Step 5: Evaluation and Validation

Through a case study, the potential usefulness of the proposedmethodology is validated. The aforementioned procedure is em-ployed for monitoring construction supply chain management of abuilding project. Construction supply chain management (CSCM)is an application area where both BIM and GIS can play a key rolein improving process efficiency (e.g., providing a detailed takeoffusing BIM and managing warehousing and transportation usingGIS). The main focus of this case study is on the procurement phaseof a project in which information pertaining to the location of sup-ply chain assets is visually monitored. While some steps of the casestudy are performed either in BIM or in GIS itself, material andcomponent data are exchanged frequently between these two tech-nologies. In addition to this high level of integration, using the samecase study as the baseline for comparison is another reason forchoosing the CSCM application as the validation approach toevaluate the benefits of the proposed methodology. The methodol-ogy is employed for monitoring CSCM of a building project inCarrollton, Georgia, namely, The School of Nursing at the Univer-sity of West Georgia. The project involved a three-story, 65,000-ft2

<owl:DatatypeProperty rdf:about="… IfcPropertySingleValue "><rdfs:subPropertyOf rdf:resource="… IfcPropertySet "/><rdfs:range rdf:resource="&owl;real"/>


Fig. 11. IfcPropertySingleValue attribute of the IfcPropertySet definedas OWL

Fig. 12. Process flowchart of IFC to RDF/OWL translation



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building accommodating all functions for nursing education andsupport spaces.

The authors define the relevant parameters for each type ofbuilding materials (e.g., engineered-to-order (ETO), made-to-order(MTO), etc.). These relevant parameters (or new attributes) willbe used to determine the status of each material. Therefore, Ifc-PropertySingleValue is used to add cost and schedule data andsupplier information into the IFC in addition to the quantities(e.g., area, volume, weight) specific to each element type. TheIfcPropertySingleValue has four attributes: (1) Name is used to de-fine the name of the new parameter, (2) Description can be used toprovide more information about the parameter, (3) NominalValue isused to assign a single value, and (4) Unit can be used to furtherdescribe the NominalValue. Once defined, the IfcPropertySingle-Value is used as a subattribute for IfcPropertySet in order to defineall extensible properties that apply to specific building products.Fig. 13 shows the IFC entities and their corresponding RDF/OWLclasses translated by the proposed method.

The description of curtain wall is used as an example of theETO product. Curtain wall is represented as an IfcCurtainWallelement and implemented as a subclass of IfcBuildingElement.The IfcCurtainWallType is used to define the specifications ofcurtain wall, such as the shared properties that are common to allcurtain wall types, the optional properties that are common to cer-tain types of curtain wall, and new properties that are added tomanage the supply chain. A typical IfcCurtainWallType entity isrepresented as follows (In the description below, texts in brackets[] refers to the attribute names and they are not presented in the IFCdocument):

#[line number goes here]=IFCCURTAINWALLTYPE([GlobalId as a normal and leaf node attribute], [OwnerHistoryas a normal and simple type attribute], [Name as an optional andleaf node attribute], [Description as an optional and leaf node attrib-ute], [ApplicableOccurrence as an optional and leaf node attribute],([HasPropertySets as a normal and complex type attribute]),[RepresentationMaps as an optional and complex type attribute],[Tag as an optional and leaf node attribute], [ElementType as anoptional and leaf node attribute], [PredefinedType as a normaland leaf node attribute]);

Fig. 14 shows the RDF graph of the IfcCurtainWallType entityand its corresponding RDF/OWL classes. The cost, schedule, andsupplier information added to the IFC model are defined by Ifc-PropertySet and attached by the HasPropertySets attribute. Whilethis attribute is defined as a complex type attribute in the BIMontology, the way it is represented in the IFC model (i.e., withinparentheses) makes it possible to distinguish between the complextype and other types of attributes.

GIS-based spatial analyses such as network analysis and attrib-ute analyses are used to provide an optimal solution to managecosts of supply chain logistics, which combines the cost of orders,warehousing, and transportation. With regard to this need, geo-graphic information of suppliers, quantities, and properties ofbuilding components included in the BIM model are combinedwith network analysis in a GIS. The authors use either IfcElement-Quantity or IfcPropertySet to define a set of quantities (e.g., length,height, gross footprint area, etc.) of an element’s physical property.Users of the proposed methodology should be aware of hierarchiesof attributes and alternatives to define the values and quantities.For instance, it is possible to retrieve area, volume, and lengthquantities for IfcCurtainWall from its IfcPropertySet, and Height,Length, Width, GrossFootPrintArea, and GrossVolume for IfcWall-StandardCase from its IfcElementQuantity. Up to this point, thesupplier locations and material properties defined in the BIMmodelare combined together in the GIS model.

The material status is created as instance parameters in the BIMmodel and assigned to all categories like walls, windows, doors,and columns. All these parameters are defined as a Date–Time var-iable and have two entries: one for schedule and one for actual date.Here, the authors take advantage of the manipulation step of theresearch methodology to translate semantic web queries to seman-tically equivalent IFC entities. The schedule and actual date param-eters are leaf-node attributes, however, their corresponding IFCclass might have simple or complex type attributes. Then, the queryresults are converted into ifcXML that can be imported into a BIMmodel. Following this, the user can compare the actual dates withthe schedule dates to categorize elements based on the delays indelivery. The RDF/OWL documents generated by the proposedmethodology were validated using the W3C validation service.It addition to the syntax and structure, the authors also checked thesemantics of the generated models in the BIM environment.

Current practice and the proposed methodology were used inparallel during this case study. Challenges in BIM and GIS datasharing and interoperability using existing approaches are twofold:1. Data models related to building or geospatial components are

specified and structured in different formats, thus, differentBIM and GIS authoring tools cannot exchange and share theirdata models between each other; and

2. Even if some BIM-related information are transferred to GISor vice versa (e.g., using a data conversion tool), there is noguarantee that another system can interpret the data beingtransferred.

Based on the results of the case study, it can be concluded thatthe semantic web technology enables BIM users to represent(Step 1), share (Steps 2 and 3), and discover (Step 4) building andGIS data through ontologies. Table 2 provides a comparison be-tween the proposed approach and state-of-the-art BIM and GIStools, including Autodesk’s AutoCAD and Revit, Bentley’s Archi-tecture, Graphisoft’s ArchiCAD, and Esri’s ArcGIS, based on thefraction of building and GIS features that can be exchanged (orsupported) between BIM and GIS models without losing theirsemantics (this table only shows some examples of these features).These features are limited to those used for the case study, anddivided into building elements, geometry elements and basic

#...=IfcPropertySingleValue( Name, Description, NominalValue, Unit)

…<owl:Class rdf:about="…IfcPropertySingleValue"><rdfs:subClassOf rdf:resource="…IfcSimpleProperty"/>

</owl:Class> …

<owl:DatatypeProperty rdf:about="…Name"><rdfs:domain rdf:resource="…IfcPropertySingleValue"/><rdfs:range rdf:resource="&xsd;string"/>

</owl:DatatypeProperty> …

#...=IfcPropertySet(GlobalID, OwnerHistory, Name,Description, HasProperties)

…<owl:Class rdf:about="…IfcPropertySet"><rdfs:subClassOf rdf:resource="…IfcPropertySetDefinition"/>

</owl:Class> ……<owl:DatatypeProperty rdf:about="…HasProperties">

<rdfs:domain rdf:resource="…IfcPropertySet"/><hasSetOf rdf:resource="…IfcPropertySingleValue"/>


Lead-node Attribute

Simple Type Attribute

Complex Type Attribute

(CType: Set)

Fig. 13. IFC entities transformation into RDF/OWL classes in thecase study



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constructs. In order to understand what fraction of the semanticsare understood (or returned) by the destination system, the recallindex is categorized as full, partial, and none. If the semantics canbe retrieved in the case of two-way exchanges (i.e., from BIM toGIS and back again), the BIM and GIS features are fully recalled.However, if the semantics of the features can be delivered onlyin one-way exchange, they are partially recalled. For example,in order to transfer a geometric surface from a building model toa GIS model (or vice versa), the user needs to represent the featureas 2D CAD data and then annotate it with appropriate keywords.However, this practice does not convey the meaning of the geom-etry element in the case of two-way exchanges, and the BIM userwill need to define a new element after it transfers back to the build-ing model. Although this feature is partially recalled by state-of-the-art tools, the proposed approach fully recalls the geometricsurface feature. There is no recall when the semantics cannot beshared and reused across BIM and GIS applications. Almost twothird of the features used in this study could not be semanticallyshared by state-of-the-art tools, and only 24% (15 out of 62) can

be partially transferred between BIM and GIS tools. In contrast, theproposed approach partially recalled around 42% (26 out of 62) ofthe BIM and GIS semantics. A large portion of the semantics thatcannot be recalled comes from the data items that are not related toGIS applications (e.g., chiller, column, curtain wall). Moreover, thefull-recall rate using the proposed approach is considerably higherthan that for the existing tools. According to the results of thevalidation step, around 40% of the semantics are retrieved usingthe proposed approach in the case of two-way exchanges, whileonly around 10% can be conveyed using the state-of-the-art tools.These results provide evidence for the effectiveness of the proposedapproach for extending the interoperability between the buildingmodeling and geospatial analysis tools.

Limitations of System

The current lack of formal AEC ontologies can lead to incon-sistencies between the ontologies developed by multidisciplinary

Fig. 14. RDF graph and OWL Classes of IfcCurtainWallType entity



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professionals involved in AEC projects. Thus, two ontologiesmight have different levels of granularity. When there is no equiv-alent of a concept in the destination system, the proposed approachcannot fully capture the semantics of the concept. This is why onlyparts of the semantics are recalled. A potential solution is to con-sider a most similar entity class across the destination ontology;however, this approach is not applied in the present paper.

Because of the extra information about all of the entries em-bedded into RDF and ifcXML files, converting building or GISdata to RDF or query results into ifcXML can make the file sizevery large. The model used in the case study consisted of around60 features, yet the RDF file was almost 660,000 lines of code witha total file size of 36.4 MB. Thus processing all data can be highlycomputationally intensive and time-consuming through a webportal. Ontologies are also evolving and new concepts and fea-tures are added to the AEC and geospatial domains of knowledge.Because the proposed methodology adopted ontology mappingtechniques, it has the ability to overcome the short-term deploy-ment obstacles. However, the long-term effectiveness is dependentupon developing a data framework that automatically integratesitself with the globally-agreed ontologies.

Conclusion and Future Work

The primary objective of this study was to extend the semanticinteroperability between BIM and GIS tools. The contributionsof the proposed model to the existing body of knowledge are two-fold. The first is to enhance data exchange and integration between

BIM and GIS from syntactic level to semantic level by providingsemantics of the data.

Second, the authors constructed a new ontology based on theEXPRESS schema at the application level. This BIM ontology pro-vides a way for seamless integration of building and constructionrelated data that encompasses all IFC classes with different attrib-utes. The inconsistent level of details between BIM and GIS ontol-ogies can hinder the quality of data and information sharing.Therefore, most of the IFC building’s elements cannot be seman-tically transferred into the GIS model. In the current practice, theBIM user can transfer the geometry and descriptive informationthat can be exported either as CAD files or text reports. However,they should be annotated with appropriate keywords and thenmanually imported into the GIS model. In contrast, the proposedmethod enables the user to query the content of the data sources.Further work needs to be done to develop globally-agreed ontol-ogies for the construction domain.

While the results are encouraging for the case study, additionalcase studies would be necessary to examine the applicability ofsemantic web technology to the AEC domain. The fraction ofbuilding and GIS features that can be exchanged by the proposedapproach in comparison with the state-of-the-art tools (Table 2) rec-ognizes the semantic web as a key-enabler for integration of data inthe construction process. It is expected that the proposed approachwould enable process integration that can lead to improvementsin the exchange of BIM and GIS information. How semantic webtechnology enables both data and process integration is not ad-dressed in the case study. Therefore, it is recommended that theproposed approach is applied for different use cases that focuson the interaction between the BIM user and the system (processintegration). The present methodology demonstrates how to gener-ate RDF triples from IFC contents or IFC entities from query re-sults; however, the entire conversion process is not fully automatedand selecting of equivalent (or nearest equivalent) IFC and RDF/OWL entities is undertaken manually. For instance, once the userspecifies an equivalent IFC entity for a query result manually, thenthe proposed manipulation structure translates and fills all queryresults into an ifcXML document automatically. A reasonableextension of the present research is to develop a fully-automatedconverter in one of the server-side programming languages such asphp scripting language.

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Geometry elementsBounding edges — — X — X —Elevation — — X X — —Geometric surface — X — X — —Point X — — X — —Rectangularcoordinate system

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BIM and GIS Integration and Interoperability Based on Semantic Web Technology

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