Smart Building: Semantic Web Technology Services for
BIM (Location and Device Information)
Muhammad Asfand-e-yar, Adam Kucera and Tomas Pitner
Lab of Software Architecture and Information Systems (LaSArIS),
Faculty of Informatics, Masaryk University,
Botanicka 68a, Ponava, Brno, 602 00, Czech Republic
http://lasaris.fi.muni.cz
Abstract. Smart Building aims to autonomously control devices and systems
in given environment. These application systems are nevertheless supervised by
facility management. The facility management normally is aided by heteroge-
neous application systems. Due to multifarious data of the systems, applications,
and missing integration of data in building automation, the data is manually col-
lected by facility management, for analysis and decision making. Therefore, such
a system is required to integrate the multi-form data of various systems and appli-
cations. Hence, Semantic Web technology is proposed in this paper to integrate
data and to implement front end. Therefore, Semantic Web technology not only
provide base for analysis and decision making for facility management, but also
facilitate developers to focus on front-end application. The aim is to structure the
data, where active devices cannot only be located in a building but also identify
according to its connected systems and subsystems.
1 Introduction
Necessity of each organization is to ensure various aspects of its operation that are not
directly involved in its primary goal, i.e. providing service to customer or selling prod-
ucts. Facility management (FM) covers the aspects, such as space management, help
desk & service desk, maintenance or energy monitoring. International Facility Man-
agement Association (IFMA) defines FM that encompasses multiple disciplines to en-
sure functionality of the built environment by integrating people, place, process and
technology.
FM distinguishes several systems and data sources that support and simplify tasks of
FM. Widely used Computer Aided Facility Management (CAFM) systems cover most
areas of FM. CAFM software serves as repository and user interface for operational
data, for example assigning employees to rooms, log of maintenance plans, requests
& tasks, energy consumption data, and many more. This CAFM is used by facility
managers in FM. The Building Information Model (BIM) is a data source that con-
tains spatial information about building constructions, locations and devices installed
in them. Data from the BIM database serve as an input for CAFM systems. Finally,
the task of FM is tightly connected to modern “intelligent buildings”. These facilities
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Pitner T., Asfand-e-yar M. and Kucera A.
Smart Building: SemanticWeb Technology Services for BIM (Location and Device Information).
DOI: 10.5220/0006144700780089
In European IST Projects - The Quest for Excellence Towards 2020 (EPS Vienna 2014), pages 78-89
ISBN: 978-989-758-101-4
Copyright
c
 2014 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

incorporate a wide scale of automated systems, such as security system, access con-
trol system, fire alarm system or building automation systems that controls Heating,
Ventilation, Air Conditioning (HVAC) devices. Building Management System (BMS)
facilitates remote monitoring and controlling of the building operations. The detailed
description of CAFM and BMS software can be found in [1, 2].
Currently the integration of BMS data with CAFM and BIM is simplified, which
is not effectively queried because the integration is missing. The integration between
them is impossible, without semantic structure because BMS data is determined by net-
work topology. The semantic structure is required for the advanced analytical features
of CAFM software, which are currently not integrated with BMS data. The missing
integration between CAFM, BMS and BIM does not affect small sites with less instal-
lation, as long as data collection and analysis are performed manually. However, for
large sites (i.e. installation of hundreds of devices, thousands of sensors), manual data
collection prevents effective gathering of required information. Despite of large sites,
BMS contains large amount of accurate, up-to-date and detailed data which is valuable
for building operation analysis. This data cannot be collected by any other way, other
than semantic structure (i.e. designing Ontology Model).
Currently the integration of BMS data with CAFM and BIM is simplified to a simple
structure that cannot be effectively queried because the integration part is completely
missing. The integration is impossible because BMS data structure is determined by the
network topology, not by the semantic structure. The semantic structure is required be-
cause the advanced analytical features of CAFM software are currently not integrated
with for BMS data. This does not affect the small installations, where data retrieval
and analysis can be easily performed manually. However, for large sites (hundreds of
devices, thousands of sensors), the amount of data prevents effective gathering of re-
quired information. Despite of this, BMS contains large amount of accurate, up-to-date
and detailed data which are valuable for building operation analysis. This data cannot
be collected by any other way, other than semantic structure (i.e. designing Ontology
Model).
Development of analytical systems for building operations requires expertise in
fields of building automation protocols and building technologies, which is not common
among commercial IT experts. Vendors of building automation systems focus on devel-
opment of the hardware. Software, which is provided by vendors with the hardware, is
used for management and programming the building technologies system in everyday
operations, rather than for analytical operations. Developing the complex systems for
analytical operations is commercially unprofitable in large sites; therefore, development
of such systems is rare in current time. Comparing to small sites, data analysis is per-
formed by simple approaches such as defining manual reports, exporting raw data (an
ad-hoc analysis is performed by end users) or using purely financial data (i.e. invoices)
for operation analysis.
Overcoming the issue of analytical system complexity, the goal of this research pro-
posal is to define a middleware layer. The middleware layer will simplify development
of advanced applications in the field of building operation analysis. It is worth to note,
that the aim of the work is not to provide tools for building operations analysis, but to
develop a middleware layer. The development of middleware tools, models, methods
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and standardized interfaces will allow skilled software developers to apply their knowl-
edge and skills in the field of building operation analysis. These skills cannot be applied
now, because expertise in the field of building automation is required.
One of the main parts of the middleware layer is a Semantic Model. The Seman-
tic Model stores additional information about BIM data, which allows meaningful and
efficient querying mechanism. This paper aims to present Ontology repository imple-
mented for Semantic Model of BIM data. The paper explains related work in section
2. Overview of Semantic based Smart Building and Ontological Model is explained in
detail in section 3. Section 4 and 5 explains use case scenarios and provided solutions
to scenarios using Ontology model, respectively.
2 Related Work
Information technology plays an important role in intelligent buildings, as an increas-
ingly sophisticated demand [3, 4], from decades for comfort living and requirement of
increased occupant control. Indeed, much of the work in regard to building automated
systems was done, but still integration is lacking between the data for analysis.
Various devices communicate and interact, without direct human intervention. Co-
ordination between devices act as supervisors, these devices are devoted to manage
available resources to meet defined requirements. Building management and automa-
tion systems are still far from this vision [5]. Scenarios are defined during implementa-
tion but no dynamic changes occurred. Currently, automatic information management
systems are quite limited.
Ontology engineering is a primary concern for defining concepts and relation be-
tween them. Therefore, main entities of building according to requirements are used as
concepts to design Ontology Model. Hence, relations between concepts facilitate rea-
soning, which ultimately contributes in analysis. For designing self configuration and
self management system, Ambient Intelligent (AmI) system [6] is an example, which
uses ontology for interacting within given environment and exploiting knowledge for
cognitive processes and autonomously managing its own functions. Likewise Wireless
Sensor Network (WSN) is also used in Open Framework Middle-ware [7] for man-
agement in Smart buildings. Open Framework Middle-ware diagnosis faults in sensor
networks. Therefore rule base knowledge management model is designed. This model
facilitates FM applications, such as in energy monitoring, security, water flow con-
trol, etc. Additionally, Home and Building Automation (HBA) [5] is another flexible
multi-agent system. This system applies knowledge base representation and automated
reasoning for resource discovery in building automation.
In Smart Building automation, wireless pervasive computing is introduced to en-
hance life comfort, and importantly reduce maintenance and consumption cost. The
Smart Building automation integrates mobile technology to facilitate maintenance, which
deals with monitoring and life safety plans in case of emergency. An ontological model
is proposed [8] to switch-off lights when no one is in room, scheduling water valves and
pumps accordingly and switching to photovoltaic installation if bright shining sun rises.
Besides this, an approach for embedded systems of sensors is used to detect activities
of visitors and occupants [9], while interacting with smart building. The focus is to sup-
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port FM tasks, such as building management, maintenance, inspection and emergency
response.
Industry Factory Classes (IFC) are extensively used in construction of BIM in smart
buildings. These classes are imported into a Semantic Web Model, where the require-
ments are analyzed by facility manager according to feasibility of a building construc-
tion. Similarly, the Semantic Model is also used to view the 3D building models to vi-
sualize data. Therefore, an approach [10], based on both semantic architecture (named
as CDMF) and IFC 2x3 is used for 3D geometries of a building. In project DRUM/PRE
[11], IFC classes are used for data maintenance & connections, and are linked through
Semantic Web Technology to allow required queries. The IFC classes are also used to
define policies for Energy-Efficient smart buildings, i.e. in Think Home project [12].
The proposed Semantic Model, in this paper, gathered BIM information from Spatial
database. Here, the construction of BIM in Ontology Model facilitates in allocating the
active devices, analyzing effects of readings gathered from devices and also helps in
decision making of device installation in new buildings. In proposed Ontology Model,
BIM is not using IFC for making decisions in selecting construction materials.
With the BIM is used for construction of the Ontology Model, to cover several as-
pects of operational analysis in Smart Building. The Ontology Model is designed to
connect data used in various heterogeneous systems. Domain knowledge of the Model
facilitates in monitoring various devices, provide instant response to concerned end-
users and reasoning & analysis for future decision making, as in [5, 13], which reduces
cost of energy and leads to efficient building operation. Comparing to [5, 7, 8], the On-
tology Model, proposed in this paper, not only identifies device connections with subse-
quent systems & subsystem, automatic reasoning and focuses to support FM tasks but
also to locate the active devices in a building. This is achieved by integrating Spatial and
Technology databases. Due to this integration of databases, building operators perform
quick response in allocating a device in alarming situations. Even when a device re-
quires replacement or adjustments, this additional feature facilitates in immediate time
response. Device allocation also supports the analysis and decision making, for exam-
ple, if various temperature sensors provide different reading in and outside a room, then
this feature helps in identifying that which device is more affected by external tem-
perature of the room. The model reduces information load on building operators, as
discussed in [14], and also reduces location identification time as compared to a manu-
ally locating a device. The proposed Ontology Model is based on existing systems used
at our campus, where Spatial database and Technology database are already in use for
manual analysis and future decision making.
3 Semantic Web Technology & Smart Building
Several concepts are gathered from different systems in analyzing building operation,
these systems are BIM, BMS and CAFM. Table. 1 provides overview of concepts used
in the systems. For example, temperature in a particular room is a Environmental Vari-
able, and is explained as;
– Meaning (physical quantity, ... → room temperature)
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Table 1. Elements of Building Operation Semantics.
Environment Var. BMS BIM
Physical Quantity Device Location Information
Aggregation Type Object (Data Point) Device Information
Environmental Spec. Object Purpose List
Further Specification
– Source (Location Information & Device Information → data from BIM database)
– Available data (device, ... → BMS network addresses for real-time data, historic
data and event triggers)
– Relations (which variable is influenced by & what is influenced by a variable)
Detailed description of BIM is explained below. The Ontology Model is based on prac-
tical experience and requirements required for the campus’s BIM systems. The BIM at
campus integrates 200 buildings in one network and uses BACnet as its communication
protocol. The BMS contains approximately 1000 devices and hundreds of thousands
data points (BACnet objects). The Model is generalized on the abstract concepts that
are common for each of the building’s operation, monitoring and FM systems. The
general architecture of the system is explained in [15].
Location Information in BIM – Location Information is stored in Spatial database
named as “Building Passport”. Location is described by its location code. The location
code serve as a primary key in Spatial database. Usually, room is represented as a loca-
tion in a building. In Spatial database location code is a string defining location data as
Site Code, Building Number, Floor and Room information; figure 1 elaborates Building
Passport.
Device Information in BIM – Device Information is stored in Technology database
named as “Technology Passport”. In Technology, database Device Information repre-
sents a device that describes location of the device, its purpose and its connection to a
particular system in a building. For example, the systems could be building automation
system, security system, CCTV, water supply, power lines, etc. In Technology database,
Technology Passport is a string consisting of System, Sub-System, Device Type and
Device Index; as described in figure 1. The Device Index is used to distinguish similar
devices in a room. The “Building Passport (BP)” is integrated, with Ontology Model,
with “Technology Passport (TP)” to define a complete code for a device, its connections
and its location in a building.
Note that, the object data in BMS are not identical to the devices in BIM, for exam-
ple, temperature sensors are considered as devices in the BIM, but the sensors values
are used in BMS. Therefore, the value of temperature is measured by temperature sen-
sor, which is passed through a particular Programmable Logic Controller (PLC). The
temperature sensor value is communicated through a BACnet address in BMS. There-
fore, relations are defined between BMS objects and BIM devices to describe the data
of original source. The identification codes shown in figure 1 are used as instances in
Ontology Model.
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Fig. 1. Identification codes used in BIM and BMS.
3.1 Semantic Web and Complex Data
The complex data used in BIM systems, is analyzed to construct Ontology Model. The
Ontology Model is constructed in Protege 4.3 tool. BIM system is using different con-
ventions for a single device installed in a room. Therefore, each of the related informa-
tion, used in BIM, is integrated with each other. The information used for integration is
the Location Information and Device Information. After integrated different concepts
used in BIM system, the common data is gathered from the system. The data is gathered
according to the used conventions for each of the device and other related categories.
Categorizing the information helps to simplify the complex data. This complex data
is managed to distinguish between various devices and rooms at different buildings. The
common data, i.e. the information used at BIM, provides a complete view of various
devices used in all buildings and also contributes in grouping variety of available data,
for analysis.
3.2 Ontology Construction
The common data is analyzed to construct concepts for Ontology Model. Based on BIM
systems’ data, it is decided to keep it as a one concept. Therefore, the BIM taxonomy is
extended to two concepts i.e. BP taxonomy and TP taxonomy and categorized according
to Room, Floor, Building and Sitecode for BP and System, Sub-system, Device Type
and Index No. for TP, as shown in figure 1.
The collected common data from various systems use identification codes for each
device and other entities. The common data is used as instances in Ontology Model.
Therefore, the concepts are populated using common data. The concepts are used to
define a device, device location, device connection with systems & sub-systems.
The challenging step is to integrate the concepts by defining relationships between
them. Various identification codes are used to identify devices and other entities in
BIM system; it is complicated to link them, as they are theoretically explained. This
is because that the similar devices have different identification codes. These different
identification codes for similar entities are used by primary data sources to identify the
devices or devices at location. Therefore to link each concept, an identifier is defined
at each step, according to requirements. Hence an identifier is used as concept. The
predicates are assigned to link the identifier with other concepts accordingly. The single
identifierused for BIM system is “BIMIdentifier”, as shown in figure 3. This identifier
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is populated according to total number of Location Information in buildings and total
number of active devices, respectively. An Ontological restriction is used at Root On-
tology, that explains a device is an active device if we are able to get data of location
and device information (i.e. BIM), as shown in figure 3.
Analyzing common data and defining concepts provides an overview of Ontology
Model. Relationships between the concepts define logical association of entities in BIM.
These relationships, i.e. predicates, are conceptual relationships and are used by build-
ing operators at the campus. Therefore, definition of relationship is keenly considered
according to technical aspect of BIM.
3.3 Extending Ontology Model
Defining concepts and relationships in Ontology Model, facilitates in improving final
results according to requirements. Major issues related to repetitive results are solved
using identifiers, but after populating the Ontology for a second building in same site
at Root level of Ontology, again generates repetitive results, this is because of similar
alpha-numeric numbers assigned to floors and rooms in different buildings.
To avoid recursive results, due to similar floor and room numbers in different build-
ings, the Ontology Model is extended to Extended level Ontology, as shown in figure 2.
The common data of BIM is populated at Root level Ontology, but the relations between
instances of BIM are specified at Extended Ontology. Therefore, for each building at
the campus, an Extended Ontology is used, to keep the uniqueness of location and de-
vice information. Hierarchy is used for Ontology Model, which depends on analysis
of relevant concepts in terms of entities and integrated data of BIM [16]. In taxonomic
relations, links are established on canonical structure of concepts and lexico-syntactic
patterns [17] are used to construct unique Ids for meaningful Ontology according to
BMS and BIM.
Fig. 2. Overview: Levels of Ontologies.
4 Scenarios
In this section, use cases explains requirements of facility managers. Facility managers
perform analysis, based on readings generated by active devices. Building operators
compiles a list of active devices and forward it to facility managers. The search is car-
ried out with the help of Ontology Model, to compile the list. Therefore, complete
information of a room (i.e BBA01N01001a, as shown in figure 1), is provided in query
to search active devices. Similarly, it is also required to get information about a list of
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rooms in a building, by providing complete information of a device (i.e. CBBE001).
Therefore, the Ontology Model is able to compile a list of rooms and sent to building
operators, where that specific device is operational.
Fig. 3. Root Ontology Model.
Facility managers also requires to find out all BIM devices or BACnet addresses,
which are installed in a room. The inverse of this use case is also useful, for example
to search a room where specified devices are installed. Such queries facilitate facility
managers to perform analysis for future decision making and planning.
5 Information Filtering
Capability of the model is to filter relevant requirements to facilitate user according to
her queries. The Ontology Model provides available information of connected devices,
its Systems & Sub-Systems, characteristics, functionality and also location information
where devices are installed. A pictorial illustration of developed Ontology is repre-
sented in figure 3.
Selecting List of Devices. Using following logic; list of devices is selected, through
SPARQL query, which is installed in a room. In figure 4, complete information of room,
i.e. BBA01N01001a, is provided at extended level of ontology, to search for deivices
that are installed in that perticular room. The figure 4 shows that only room and floor
information is provided in the query, this is because, as shown in figure 2, query is
applied at the specific Sitecode and Building. Therefore, the query is applied at extended
level. The results of the query are shown in figure 5. If it is required to search the
Sitecode and Building then the Onology is selected through application and the smilar
SPARQL query is applied to other Buildings and Sitecode.
Identifier (?ID) ∧ hasSpecific (?ID, ?Room) ∧ isEquipedWith(?Room, ?DeviceType) ∧
hasPerticular (?ID, ? Floor) ∧ isAllocatedAt (?Floor, ?Building) ∧ isSitutatedAt
(?Building, ?SiteCode) → hasAssigned(?ID, ?DeviceType)
Selecting List of Rooms. Described use cases facilitate facility manager’s require-
ments. The Ontology Model filters all rooms, which are queried according to device
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Fig. 4. SPARQL query for Selecting List of Devices.
Fig. 5. Results of SPARQL - List of Devices.
information. A list of rooms is selected and sent to facility manager, according to com-
plete room information. For filtering required information, following logic is used to
create the query, as shown in figure 6.
Identifier (?ID) ∧ hasAssigned (?ID, ?DeviceType) ∧ hasConnectionWith (?ID,
?System) ∧ hasReferred (?ID, ?SubSystem) ∧ isEquipedWith (?Room, ? DeviceType)
→ hasSpecific (?ID, ?Room)
Fig. 6. SPARQL query for Selecting List of Rooms.
Fig. 7. Results of SPARQL - List of Rooms.
According to the scenario; for example, a building operator search for a list of room
that has Device Type SK connected with System C and Sub-System F. Therefore, she
has to provide the device information in SPARQL query, as shown in figure 5. Initially
the query selects all those identifiers who has Device Type SK, System C and Sub-
System F, therefore a long list of identifiers is selected. In second step, pattern matching
process is performed. Therefore initially, the identifiers of Device Type SK having Sub-
System F are filtered. Then the resultant identifiers from Device Type SK and Sub-
System F are filtered according to System C. The identifiers other than System C are
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removed from filtered list. At this point, all those identifiers are listed, whose System
is C, Sub-System is F and Device Type is SK. Finally, according to the filtered list of
identifiers, complete information of Room is selected according to Site Code, Building
Number, Floor and Room information.
Figure 7, describes results of above defined query. The query is applied on Semantic
Model of one building i.e. BBA at Extended Level of Ontology. The results explains that
the queried device, which is connected to System C and Sub-System F, and is actively
functioning at three rooms of the building. The results also describes that two of the
devices are installed in one room, i.e. the room R001d, shown in figure 1, is at Floor
N01, Building 01 and Site Code BBA. The complete location address of the room is
BBA01N01001d, this address is understandable by end-users at campus.
Selecting List of Rooms using Device Index. Using the Device Index in SPARQL,
as shown in figure 8, it is quit clear from the results of the SPARQL query, as shown
in figure 9, that room information is displayed once where the device is installed. In
section ”Selecting List of Rooms” the room ”BBA01N01001d” is displayed twice in the
SPARQL query result, in figure 7. This difference explains that when Facility Managers
need to know that a specific device, according to device index, is installed in how many
rooms then the SPARQL query shown in figure 8 is used, otherwise to know how many
rooms have the devices (i.e. CFSK) are activly working then SPARQL query shown in
figure 6 is used.
Identifier (?ID) ∧ hasDefined (?ID, ?DeviceIndex) ∧ hasAssigned (?ID, ?DeviceType)
∧ hasConnectionWith (?ID, ?System) ∧ hasReferred (?ID, ?SubSystem) ∧
isEquipedWith (?Room, ? DeviceType) → hasSpecific (?ID, ?Room)
Fig. 8. SPARQL query for Selecting List of Devices.
Fig. 9. Results of SPARQL - List of Devices.
The Ontology Model personalizes the information related to BIM and BMS to
reduce information load by filtering irrelevant data according to requirements. Thus
the Ontology Model is developed according to explained structure of BMS and BIM.
SPARQL queries are applied, subsequently to requirements of building operators and
facility managers. The Ontology Model is personalizing and harmonizing the informa-
tion of BMS and BIM. This saves time by filtering irrelevant data, according to user’s
requirements.
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6 Conclusion
This article focuses on FM and explains the integration of BIM system. The proposed
approach addresses the Semantic Technology used for BIM. Therefore, facility man-
agers are able to perform operation analysis in large-scale environments. The designed
ontology covers the concepts of BIM, used for Location Information and Device Infor-
mation. The Ontology Model enables reasoning the BMS and BIM information based
on defined hierarchical structure. Ontology Model helps the developers to focus on user
interface and analytical methods rather than on collecting integrated data provided at
various systems. Therefore, facility managers are able to perform analysis and decision
making for future planning. This is the significant improvement in current analysis work
flow.
The research is expendable in several areas of large-scale BIM and BMS data anal-
ysis by introducing “Semantic Smart Building Ontology”. Initially, advanced analytical
tools should be developed, based on the historical data gathered in BMS. Additional
research is required in the field of user interfaces, both for the query definition and re-
sults presentation. Next step of the project is to integrate the Semantic Ontology Model
with Indoor Navigation system. This will extend the horizons to use Smart Devices for
Facility Management.
Acknowledgement
This work was carried out during the tenure of an ERCIM ”Alain Bensoussan” Fellow-
ship Program. The research leading to these results has received funding from the Eu-
ropean Union Seventh Framework Program (FP7/2007-2013) under grant Agreement
No. 246016.
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