E-Journal of Advanced Maintenance Vol.8-2 (2016) 13- 22
Japan Society of Maintenology

Building Ontology for Fire Emergency Planning and Support
Noel Nuo Wi TAY1 , Naoyuki KUBOTA1 and János BOTZHEIM1
1Tokyo Metropolitan University, 6 Chome-5-4 Asahigaoka, Hino, Tokyo 191-0065, Japan

ABSTRACT
Risk management and maintenance operation needs planning on emergency occurrences like a fire, which
require information on building state. During fire emergencies, parts of a building may have higher risks
compared to other parts of the building. This distribution of risks depends on the location of the fire, placementof hazardous materials in the building, condition of doors and windows around the building, vents and so on.
Before getting to safety, one needs to know where safe places are, and how to approach them safely. This can bedifficult in a large building complex. As the aforementioned attributes can be seen as objects and classes, they
can be assigned their semantic meanings. This includes the description of the rooms, and their relationships withevery other room, such as their adjacency. The building can be modeled by OWL description logic, which
define the building as a graph, where objects are classified into classes, with properties to connect between them.
The generated graph via description logic can be used for further inference for more data and for querying. With
this approach, this work aims to build the building ontology to provide support during fire emergency. Casestudies are done to infer smoke propagation and determination of escape outlet, which can provide the basis for
additional query and inference.
KEYWORDS
Ontology, Description logic, fire emergency, knowledge representation

Article history:
Received 8 April 2016
Accepted 11 July 2016

ARTICLE INFORMATION

1.

Introduction

Planning and simulating occurrences of emergencies given the actual state of a building like
warehouse or factory plant is important for risk management and maintenance operation. Due to suchactivities are generally non-routine and incur downtime, planning has to be done carefully but quickly.
By equipping the building with ambient intelligence and knowledge representation, well-informed
information can be obtained easily by the workers. Such infrastructures are also useful in actual
emergencies for both escapees and rescuers.
Recent progress in information processing and communication technology has made possible thecreation of smart home, smart environments and office buildings that is able to increase comfort,
security and efficiency. Information system has been widely used in the field of maintenance,
designing and manufacturing for factories [2]. Ambient intelligence played a big part, which has theaim of surrounding building inhabitants with intelligence and unobtrusive sensors and actuators toacquire knowledge, adapt and dispense favorable services [1]. Mobile devices are utilized and objects(including non-electronic objects) are assigned with RFID tags that carry their information essential
for the construction of knowledge structure and inference.
This is further enhanced by the progress of web technologies and the introduction of the semantic
web, where various tools for knowledge representation, semantic annotations and querying aredeveloped and standardized by the World Wide Web Consortium (W3C). This progress also brings
forward the Internet of Things (IoT), where everyday objects are equipped with embedded data andcommunications capabilities. With this, IoT realizes the concept of pervasive and ubiquitous
computing that is crucial for ambient intelligence.
Semantic web is introduced by Tim Berners-Lee, which is an extension of the current web where
web information are endowed with semantics such that they can be operated and processed bymachines [3, 4]. Substantial amount on research has been done that utilized Semantic Web

ISSN-1883-9894/10 © 2010 – JSM and the authors. All rights reserved.

N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

Technologies to build intelligent applications in various domains [7, 8, 9]. They have been applied todomotic applications [10, 11], agent communication [12] and emergency management [13] that
exploits the knowledge structure built up via tools such as the ontology markup language (DAML,OWL), rule mark up language (SWRL), querying tool SPARQL and RDF schema, all of which areendorsed by W3C.
This work aims to extend the use of description logic and querying tools introduced by the fieldof semantic web to the construction of knowledge representation for emergency and safety purposes.
In a large building complex with large number of rooms and object distribution, situation can bechaotic given a disaster situation such an earthquake, which can lead to fire in multiple locations of
the building. Given the layout of the building and arrangement of crucial objects like those that are
combustible, and also depending on the state of doors and windows, locating danger zone and escapeoutlet can be hard. The spread of smoke and gases, which closely depends on whether certain doors
are opened/closed, further complicate the situation. Information on the possible danger zone in timesof emergency is thus crucial. Given a building that is equipped with sensors to realize ambient
intelligence and knowledge structure of the building, such information can be obtained
instantaneously through description logic reasoning. Objects in the building can be assigned semantic
meanings. This includes the description of the rooms, and their relationships with every other room,such as their adjacency. Description logic is used to define the building as a graph, where objects areclassified into classes, with properties to connect them. The graph generated via description logic canbe used for further inference for more data, and also be used for querying.
Description logics (DLs) are a family of knowledge representation languages used forontological modeling [5]. DLs are equipped with formal semantics that allows the exchange of
ontologies without ambiguity as to their meaning. They also enable inference to be made given theaxioms stated in the ontology. There are different kinds of DLs. We choose OWL Web Ontology
Language to model the building ontology due to its compatibility with the semantic web endorsed by
W3C (thus, abundant tools are available), and have the benefit of decidable inference that prevents
on-going looping. The advantage of using OWL DL is that they are always guaranteed to terminate
during inference, unlike using pure First Order Logic (FOL).
By modeling a building as a semantic graph described via OWL DL, syntactic risks of every partof the building (given every part are treated as an individual object) can be inferred using an inferenceengine or reasoner. With the reasoner, inference can be made in real-time with current situation. Thisis important, as the world is dynamic, such as doors opening and closing or locked, and hazardous
materials being moved around.
As situations and the effects of fire are highly unpredictable, axioms obtained from description
logic are of course not sufficient to model the real environment. Building fire emergencies need totake into account not just the surrounding, but also the flow-of-influence for object states anddistributions, which requires logical constructs. One can resort to densely modeled rules (which isextremely tedious and difficult to expand) or inferred from probabilistic or analogous models like
probabilistic graphical model and SVM respectively (though they cannot directly support logicalconstructs and notion of causality). Our intention is to provide the latter with representations that are
generated from the logical constructs of the description logic. Case studies done are to demonstrate
the influence of flow due to object relationships inferred via OWL DL, where it is decidable. Learningmodels can use the stable representation to acquire more accurate depiction of the environment.

2.

Building Knowledge Base

2.1. Building Ontology
Ontology is setup such that knowledge can be represented according to its schema, which cansupport reasoning on possible syntactical dangers and escape outlets. In this work, emphasis is given
to dangers from fire emergency. In such a case, building layout and location of combustible objects aswell as possible escape outlets are important in determining the magnitude of danger of different
locations of the building.
Therefore, the building ontology is built according to the building layout (rooms, doors,
windows), and the classification of objects (package, combustible materials). Ontology is built basedN.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

on the OWL DL language. Due to the limitation imposed on OWL in order to ensure that inference
can always terminate, certain crucial functions such as implication rules cannot be directly modeledusing the language. In this case, rules are normally modeled through SWRL. In this work, we use thequery language SPARQL (which is the query language endorsed by W3C) to perform the work of
SWRL. SPARQL is a descriptive query language that is able to support the implementation of rules
and assertion of new axioms in the knowledge base [6]. But care should be exercised when using
SWRL or SPARQL to avoid inconsistency of the ontology.
To simplify notation in this paper, property, which is part of the Resource Description
Framework (RDF) triple, will sometimes be represented in predicate form. For example, an RDF
triple “Adam isA Boy”, where isA is a property, can be represented as predicate isA(Adam, Boy). Thispredicate will return true if the triple “Adam isA Boy.” exists in the knowledge base, and false
otherwise. Besides that, a class can be shown as concept assertions, where triple “Dog rdf:typeAnimal.” can be represented as Animal(Dog).
Also, for new triple assertion, where SPARQL is used, in this paper, First Order Predicate isrepresented for better readability. For example, given the following formula:isA(?a,Boy) → isA(?a,Human)
which means that if an instance is related to Boy via isA, then it is also related to Human via isA. This
formula represents SPARQL Construct query shown below:
CONSTRUCT {?a :isA :Human.}
WHERE {?a :isA :Boy}
More information on the modeling of ontology can be obtained from [6].
2.2. Object Classification
For object classification, objects are assigned to classes that are relevant to their properties. It is
assumed that the objects involved contain RFID tags with their unique ID that can be easily read(although other types of identification method that can also be used for tracking are equallyacceptable). The intention is to provide all objects with a unique ID, and at the same time, be able tobe tracked such that their location is known every time. Given their IDs, their properties can beobtained from the building knowledge base, and thus, inference ensued. Given objects RFID are
detected, they are then represented as instance, and are assigned to a class via rdf:type property. Given
this, objects can be easily assigned to their attributes (combustible, explosive, acidic etc.), functions(product2, fire extinguisher), locations (room1, kitchen) and package group. For simplicity, only
relationships that are relevant to the case studies are described.
Every object is represented as a node in the semantic network graph, which is also an instance.
Objects will be assigned IDs via property hasID.
Certain objects may come in groups with other objects, such as materials in a luggage or acontainer. Since the group is also an object, it has its own reified node. Objects that belong to thegroup are related to it through the property inGroup. Group objects have their own unique class toencase all the objects that belongs in it. Therefore, for every group object, a class is created that is
equipped with the restriction (in Turtle):[a owl:Restriction;
owl:onProperty :inGroup;
owl:hasValue G].
where G is the instance of the group. This means, any instance that is related to the instance G throughproperty inGroup will be assigned under the unique class corresponding to G.
Objects will also be assigned attributes. Attributes that are relevant to the subsequent case studies
are being combustible, explosive and acidic. These attributes are represented as classes, where objects
with such attributes will be assigned to the relevant class. Note that these classes may overlap. The
three attributes (combustible, explosive, acidic) are subsumed by the class dangerousMaterials.
To represent the location of objects, every object instance is related to a location instance throughproperty locatedAt. Care needs to be taken when specifying locations for objects in groups, such that
all of them, including the group itself, are pointing to the same location node.
It is assumed that there are means to detect humans in the rooms, either from direct humandetection via camera or RFID. A room is assigned under haveHuman class if humans are detected.

N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

Knowing whether there are humans and their locations in the building is important for rescuers to takefurther actions, as well as determining whether the humans are under trapped position or not. For thelatter, it can be represented by the following rule:
haveHuman(?r) ∧¬validEscape0(?r) →rescue(?r)
The rule states that if a room has humans and that it is not an escape path, then it requires rescue
by assigning the room under rescue class. Details on validEscape0 are given in Section 2.4.
2.3. Building Layout Modeling
For building layout, ontology needs to be built such that room adjacency and their relationships
with any other rooms can be obtained. These relationships should also be influenced by the
opening/closing of doors, location of escape outlet and where dangers are located.
A door must be associated with two rooms. Its relationship with a room is via property isDoor. A
door belongs to class doorOpen if it is open. Therefore, before inference begins, axioms should beasserted for every door that is open. Doors that are not assigned to this class are considered closed.
Two rooms are adjacent if at least one of the doors between them is open. Adjacency between
two rooms is defined by the property isAdjacent, which has a symmetric property (if room 1 isadjacent to room 2, so is the case for the other way round). Adjacency axiom can be asserted by:
isDoor(?d,?r1) ∧ isDoor(?d,?r2) ∧ doorOpen(?d) → isAdjacent(?r1,?r2)
Another alternative to the above mentioned rule is to use restriction from cardinality on the property
isAdjacent. This eliminates the need to explicitly assert axioms of adjacency as it can be taken care ofby the inference engine, but preliminary test shows that this approach is very slow.
Property isConnected is defined by the rule:
isAdjacent(?r1,?r2) → isConnected(?r1,?r2)
Property isConnected is not equivalent to isAdjacent. isConnected is endowed with transitive
property. Whereas isAdjacent only links two rooms that are next to each other given that there is apath between them, isConnected relates a room (denoted as RoomA) to any rooms that are related toany other rooms that are related to RoomA via isConnected.
A room’s relationship with windows is made through the property isWindow. In this work, no
windows between rooms are assumed, except for windows the links rooms with the outside.
There is a node that reifies the state of being outside of the building, which is the outside node. It
is treated the same as any other rooms, except that it provides the point of an escape outlet. Windows
and doors that leads to outside of the building, given that they are open, will cause the corresponding
room to be adjacent to the outside, which is described as:
isDoor(?d,outside) ∧ isDoor(?d,?r) ∧ doorOpen(?d) → isAdjacent(?r,outside)
isWindow(?w,r) ∧ windowOpen(?w) → isAdjacent(?r,outside)
Given the above rules, since outside provides the point of an escape outlet, the following ruledescribes this property:
isAdjacent(?r,outside) →escape5(?r)
escape5 indicates that the room assigned under it is a potential escape outlet, but not under fullcertainty, as danger like fire may occur in the same room. The details of the class escape5 will begiven in Section 2.4.
2.4. Influence from Adjacency
Given two rooms are adjacent, certain influence of danger or determining whether the current
position will lead to an escape outlet can be inferred from the information of the adjacent room. In thissub-section, two influences from adjacency will be explained, which are 1) the spread of smoke 2)
determination of escape outlet
In a building, it is assumed that there are sensors in place that detects fire and smoke. Any roomwith danger detected will be assigned under the class roomDanger. Danger can be known from thesensors on fire and level of smoke, and also whether there are hazardous materials around. The
remaining rooms will be assigned nonDanger by the following rule:room(?r) ∧ notAssigned(roomDanger,?r) )→ nonDanger(?r)



N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

where notAssigned(a,b) means if b is not assigned under a. Predicate room(a) returns whether a is ofclass room or not. Every instance preceded by ‘R’ in Fig. 1. is of class room.
Smoke level consists of multiple levels, depending on the severity. The number of levels depends
on the individual choosing. In this paper, 6 levels are assigned, starting from level 0 to level 5, wherelevel 5 has the highest severity. A room with a certain level of smoke is assigned to the correspondingclass, denoted by levelClass‘n’, where ‘n’ is the number of levels. Since there are 6 levels, the classes
are levelClass0, levelClass1, levelClass2, levelClass3, levelClass4 and levelClass5. It can be safely
assumed that a class of lower level subsumes the higher class. For example, a room with smoke levelof 5 will have at least a smoke level of 4, and so on.
Given this assumption, flow of influence due to adjacency can be modeled by imposing
restriction on the level class as {levelClass‘n’ with owl:someValuesFrom on property isAdjacent tolevelClass‘n+1’}, which in Turtle is:levelClass‘n’ owl:equivalentClass[a owl:Restriction;
owl:onProperty :isAdjacent;owl:someValuesFrom levelClass‘n+1’].
In this case, the smoke level of a room can cause rooms adjacent to it to be assigned to the class oflower smoke level.
As mentioned before, determining escape outlet can also be influenced by information of theadjacent room. Like wise to smoke level, we will assign 6 levels to signify how near a particular room
is to the nearest escape outlet, where the level class is denoted as escape‘n’, namely escape0, escape1,
escape2, escape3, escape4 and escape5. The higher the level, the nearer the room is to an escapeoutlet. Also, lower level class subsumes the higher level class. But the flow of influence is not as
direct as that in the case of smoke. A room that is deemed dangerous should not propagate influence
regarding escape outlet, which means, flow of influence only works for those rooms assigned under
the class nonDanger. Lets denote the intersection between class escape‘n’ and nonDanger asvalidEscape‘n’. Restrictions is then applied to this class instead as (in Turtle):
escape‘n’ owl:equivalentClass[a owl:Restriction;
owl:onProperty :isAdjacent;owl:someValuesFrom validEscape‘n+1’].
2.5.

Multistage Inference

Inference for the state of the building cannot be done in one stage, where certain axioms can only
be asserted given previous axioms, such as the determination of escape outlet that needs informationon the nonDanger class. In this work, the number of stages is limited to two.
In the first stage, the core ontology is asserted, which includes the symmetric property ofisAdjacent, restrictions on levelClass‘n’, subsumption for levelClass‘n’, etc. The core assertions forthe first stage are shown in Table 1. In this stage, axioms asserted from sensors and assertion of
adjacency from opened doors are also performed, which are:1) isDoor(?d,?r1) ∧ isDoor(?d,?r2) ∧ doorOpen(?d) → isAdjacent(?r1,?r2)2) Assigning rooms to roomDanger if sensors pick up any danger signal
Table 1 Assertions in first stage inference
No.

1
2
3
4
5
6
7
8

Assertion
Restriction to levelClass4 with owl:someValuesFrom on property isAdjacent to levelClass5
Restriction to levelClass3 with owl:someValuesFrom on property isAdjacent to levelClass4
Restriction to levelClass2 with owl:someValuesFrom on property isAdjacent to levelClass3
Restriction to levelClass1 with owl:someValuesFrom on property isAdjacent to levelClass2
Restriction to levelClass0 with owl:someValuesFrom on property isAdjacent to levelClass1
validEscape5 as intersection of escape5 and nonDanger
validEscape4 as intersection of escape4 and nonDanger
validEscape3 as intersection of escape3 and nonDanger

N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

9
10
11
12
13
14
15
16
17
18
19
20
21
22

validEscape2 as intersection of escape2 and nonDanger
validEscape1 as intersection of escape1 and nonDanger
validEscape0 as intersection of escape0 and nonDanger
Restriction to escape4 with owl:someValuesFrom on property isAdjacent to validEscape5
Restriction to escape3 with owl:someValuesFrom on property isAdjacent to validEscape4
Restriction to escape2 with owl:someValuesFrom on property isAdjacent to validEscape3
Restriction to escape1 with owl:someValuesFrom on property isAdjacent to validEscape2
Restriction to escape0 with owl:someValuesFrom on property isAdjacent to validEscape1
Restriction to escape0 with owl:someValuesFrom on property isAdjacent to validEscape0
escape5⊆ escape4⊆ escape3⊆ escape2⊆ escape1⊆ escape0
levelClass5⊆ levelClass4⊆ levelClass3⊆ levelClass2⊆ levelClass1⊆ levelClass0
Combustible ∪ explosive ∪ acidic ⊆ dangerousMaterials
owl:SymmetricProperty for isAdjacent
owl:TransitiveProperty for isConnected
For the second stage,room(?r) ∧ notAssigned(roomDanger,?r) )→ nonDanger(?r)
haveHuman(?r) ∧¬validEscape0(?r) →rescue(?r)
are run. Inference after this will show possible escape outlet for every room.

3.

Case Studies

Case studies are performed to demonstrate the applicability of the designed building ontology to
generate semantic graphs for emergency situation. The main focus is on the detection of danger zones
and determining escape outlet given the point of disaster, distribution of objects, state of doors andpossible escape outlets. For this case study, FaCT++ [14] is used as the description logic inferenceengine. Rules and axiom assertions are implemented through SPARQL, which, in this work, isuniquely written to work with FaCT++.
3.1. Building Layout
Fig.1. shows the building layout used for the case studies. The figure shows rooms (indicated by‘R’), doors (in blue indicated by ‘d’) and window (in red indicated by ‘w’).

Fig. 1. Building Layout. Blue rectangle indicates doors, red rectangle indicates windowsN.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

Given the windows and doors allocation, room R1, R2, R3, R6, R8, R11, R15, R18, R20 and R22
have the potential of being escape outlets if the doors or windows are opened.
Three configurations are demonstrated.
Configuration 1:
Fire occurs in corridor R10. There is an object that is combustible in room R13. Doors d11, d12, d13,
d14 and d23 are opened. All windows are closed.
Configuration 2:
Fire occurs in corridor R12 and R11. There is acidic material in room R4. Doors d1, d2, d3, d4, d5, d6,
d7, d8, d9, d10, d11, d12, d13, d14, d21, d22, d23 and d26 are opened. Window w6 is open.
Configuration 3:
Fire occurs in R3 and R12. All doors are open, and all windows are closed. There are humans in R1,
R21 and R22.
3.2. Smoke Hazard Visualization

(a)

(b)

(c)
Fig. 2. Visualization of smoke spread for Configuration one (a), two (b) and three (c)In times of fire, it is generally useful to know how the smoke will spread given the condition of

N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

doors. This can help escapees and rescuers evaluate the danger of the particular section of the building.
Test is run on the three configurations stated in Section 3.1. Fig.2. shows the visualization of smoke
spread obtained from inference. Smoke spread information can be obtained at the first stage ofmultistage inference.
For configuration 1, fire starts at R10. Smoke is at the highest level in R10, thus, R10 is assigned
under class levelClass5. Smoke spread according to the state of opened doors. Given the adjacentrooms, smoke level is inferred for rooms surrounding the point of fire. Rooms that are unmarked bycolors indicating different levels in the figure are considered safe from the smoke.
For configuration 2, fire starts in R12 and R11. Configuration 2 has more opened doors compared toconfiguration 1, thus, the spread of smoke is wider. In this configuration, the spread of smoke isimportant in determining which rooms require more assistance or the control of vents. This can beobserved from room R23, R15 and R10, where there is a constant level of inferred smoke level.
Whereas, for rooms R1, R2, R3, R4 and R5, the smoke level is significantly lower. In this case, thisinformation is important for both the escapees and rescuers to evaluate the overall situation in termsof smoke propagation.
Since all doors in configuration 3 are opened, visualization shows that the smoke will spreadthroughout the building.
3.3. Escape Outlet
After the second stage of the multistage inference, escape outlet can be obtained. Fig.3. shows
the visualization of escape path obtained from inference. Rooms that are not shaded are considered
not part of the escape path.
For smoke spread information obtained in Section 3.2, smoke propagation does not give detailsof where a person should be headed to. Besides, distribution of hazardous materials will complicate
the situation, causing people to take inefficient or wrong routes to safety. Information on such path,
which takes into account points of danger, is important as it assists in deciding directions to take inorder to reach a potential escape outlet.
Configuration one only has d23 opened, leading to the outside. Therefore, this configuration only
has one escape outlet. Due to fire occurring in R10, and the presence of hazardous materials in R13,
the figure clearly shows the desirable path for people in R15 is to go through R14 instead, which canbe inferred via building ontology.
Configuration two shows two escape outlet of level 5, which is R22 (has a door to the outside)
and R18 (has a window). Although d23 is opened, which makes R11 potentially a level-5 escapeoutlet, but due to fire occurring in R11, this information is not propagated further. For people inrooms other than R18, their only outlet is through d22 in R22. From the result, human may know thatthey need to approach R15 in order to reach the escape outlet safely, as other ways are blocked. Path
via R4 is not being considered a path as there are hazardous materials inside, thus, people from R1,
R2 and R3 should approach R6 to R24 and finally R10 to reach R15 to ensure their safety.
Result from configuration two clearly illustrates the point as stated earlier that smoke spread does
not provide sufficient information on the escape route. From Fig.2., it seems R1, R2, R3, R4 and R5
are the places to be to avoid smoke. But these locations are dead end, and therefore, from Fig.3., it iswiser to follow a safe route to the nearest escape outlet.
Configuration three has humans in rooms R1, R21 and R22. From the result, R1 and R21 areassigned under rescue class because both these rooms are not escape paths. R1 is blocked by the firein R3, and R21 is blocked by the fire in R12. Humans in both these rooms cannot easily reach any
potential escape outlets. This information is crucial for rescuers to assess the situation and reach thevictims. For humans in R22, since the room is an escape path, it is not assigned under rescue class.



N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

(a)

(b)

(c)
Fig. 3. Visualization of escape outlet for Configuration one (a), two (b) and three (c)4.

Conclusion

Two-stage inference of building ontology is built via description logic to support human in timesof fire emergency for escapees and rescuers. Given the state of doors and windows and also the
distribution of hazardous materials, as well as the general layout of the building, human judgments arenegatively affected that is aggravated by the chaos of the situation. Building ontology can provide
information on smoke propagation and determination of escape outlet syntactically, which is obtained
through multistage inference that is decidable. Case studies on a complex building layout to provide
such crucial information demonstrate the effective modeling of the influence flow in times of
emergency. Statistical learning models can use these stable logical constructs for refined inference.
As the current work is only limited to two stage inference for demonstration, in actuality, it canbe extended to higher number of stages to augment much more complex axioms that can aid in

N.N.W. Tay, et al./ Building Ontology for Fire Emergency Planning and Support

situational classification. Besides, buildings can be partitioned into finer sections for more accurate
modeling, but this comes at a cost of processing time. Methods should be devised to divide large
building complexes to process them separately, instead of processing the ontology of the wholebuilding.
Current work concentrates on fire emergency. But since this application depends on the ontologythat is specific, given further modifications, this approach can be applied to wide range of applications
by incorporating information of relationships between objects and factors. Future plans are to be able
to apply this approach to risk management and maintenance for actual plants, complemented withstatistical learning models.

References[1][2]

N. Shadbolt: “Ambient Intelligence”, IEEE Intelligent Systems, Vol.18, pp. 2-3 (2003).
A. Nakajima, T. Onishi, T. Sawaragi: “Building Interoperable System Enabling Failure Information
Knowledge Sharing Between Designing/Manufacturing and Maintenance Departments Focusing on
Information Architecture Differences”, E-Journal of Advanced Maintenance, Vol.2, pp. 120-134 (2010).
[3] T. Berners-Lee, J. Hendler, O. Lassila: “The Semantic Web”, Scientific Amer., pp. 29-37 (2001).
[4] N. Shadbolt, T. Berners-Lee, W. Hall: “The Semantic Web Revisited”, IEEE Intelligent Systems, Vol.21,
No. 3, pp. 96-101 (2006).
[5] M. Krötzsch, F. Simancik, I. Horrocks: “A Description Logic Primer”, arXiv:1201.4089 (2013).
[6] D. Allemang, J. Hendler: “Semantic Web of the Working Ontologist”, Morgan Kaufman, ISBN
9780123859655 (2012).
[7] I.C. Hsu: “Extensible Access Control Markup Language Integrated with Semantic Web Technologies”,
Information Sciences, Vol. 238, pp. 33-51 (2013).
[8] C. De Maio, G. Fenza, M. Gallo, V. Loia, S. Senatore: “Formal and Relational Concept Analysis for
Fuzzy-based Automatic Semantic Annotation”, Applied Intelligence, Vol. 40, No. 1, pp. 154-177 (2013).
[9] A. Razzaq, K. Latif, H.F. Ahmad, A. Hur, Z. Anwar, P.C. Bloodsworth: “Semantic Security Against Web
Application Attacks”, Information Sciences, Vol. 254, pp. 19-38 (2014).
[10] D. Bonino, E. Castellina, F. Corno: “The DOG Gateway: Enabling Ontology-based Intelligent Domotic
Environments”, IEEE Trans. On Consumer Electronics, Vol. 54, No. 4, pp. 1656-1664 (2008).
[11] M. Ruta, F. Scioscia, E. Di Sciascio, G. Loseto: “Semantic-based Enhancement of ISO/IEC 14543-3
EIB/KNX Standard for Building Automation”, IEEE Trans. Of Industrial Informatics, Vol. 7, No. 4, pp.
731-739 (2011).
[12] F.P. Pai, I.C. Hsu, Y.C. Chung: “Semantic Web Technology for Agent Interoperability: A Proposed
Infrastructure”, Applied Intelligence, Vol. 44, No. 1, pp. 1-16 (2015).
[13] Z.U. Shamszaman, S.S. Ara, I. Chong, Y.K. Jeong: “Web-of-Objects (WoO)-based Context Aware
Emergency Fire Management Systems for the Internet of Things”, Sensors, Vol. 14, No. 2, pp. 2944-2966(2014).
[14] D. Tsarkov, I. Horrocks, “FaCT++ Description Logic Reasoner: System Description”, AutomatedReasoning, Vol. 4130, pp. 292-297 (2006).

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