An Intelligent Dynamic Context-aware System Using Fuzzy Semantic Language Daehyun Kang1, Jongsoo Sohn2, Kyunglag Kwon1, Bok-Gyu Joo3, and In-Jeong Chung1 1
Department of Computer and Information Science, Korea University
{internetkbs, helpnara, chung}@korea.ac.kr 2
Service Strategy Team, Visual Display, Samsung Electronics
[email protected]
3
Department of Computer and Information Communications, Hong-Ik University
[email protected]
Abstract. The prevalence of smart devices and the wireless Internet environment have enabled users to exploit environmental sensor data in a variety of fields. This has engendered various research issues in the development of context-awareness technology. In this paper, we propose a novel method where semantic web technology and the fuzzy concept are used to perform tasks that express and infer the user’s dynamic context, in distributed heterogeneous computing environments. The proposed method expresses environmental information using numerical values, and converts them into fuzzy OWL. Then, we make inferences based on the user context, using FiRE, a fuzzy inference engine. The suggested method allows us to describe user context information in heterogeneous environments. Because we use fuzzy concepts to represent contextual information, we can easily express its degree or status. Keywords: Context-aware computing, Fuzzy; Knowledge Representation, Inference, Fuzzy Web Ontology Language (OWL)
1
Introduction
For enhanced interaction with users in complex and distributed systems, developing dynamic context awareness systems becomes necessary that can recognize users, as well as information of the surrounding circumstances. Responding dynamically to changes in the application requirements, or the system itself, is also required [1]. With the advent of smart electronic devices, the problem of recognizing and expressing user context information, regardless of computer and language types, has emerged as an important task under the heterogeneous distributed processing system [2]. Since representing the environment that the user is in contact with the real world in crisp sets has some limitations, we introduce the fuzzy set as a more suitable means of representing the degree or status of the environment, than the crisp set [3]. For this purpose, we have chosen to use fuzzy Web Ontology Language (OWL) [4], a fusion
of fuzzy concepts and the standard OWL to represent a user’s dynamic context. In addition, it is used for the efficient description of a user’s context, since it has the ability to represent the real context in a similar form to human thinking, independent of language and computer types, and infer new knowledge from the context data. This paper suggests the following method. First, we represent user contacted environmental information with a numerical value and states, and describe it with OWL. Secondly, we transform the converted OWL context into fuzzy OWL [4]. Finally, we prove that automatic decision making of ambient environment is possible when using the fuzzy inference engine FiRE [5-6]. With the suggested method, we can describe the user context information in the ubiquitous computing environment. This method is effective in expressing both dynamic context information, and environmental status. We can also infer the usercontacted status of the environment. It is possible to enable this system to function automatically in compliance with the inferred state.
2
Related Works
A fuzzy OWL is one of the extended markup languages to represent a fuzzy set to OWL, which OWL itself does not provide [4, 7]. The fuzzy OWL provides a method to convert OWL into fuzzy OWL, and to describe membership functions that OWL is not able to. A fuzzy OWL uses the namespace ‘fdl’ to differentiate it from OWL. An element represented as a crisp set is described using OWL. Table 1 shows four principles to convert OWL into fuzzy OWL. Table 1. Four principles to convert OWL into fuzzy OWL [4]
No 1 2
Principle Every class in OWL is mapped into a corresponding fuzzy class in fuzzy OWL. Every class subsumption or equivalence in OWL has a fuzzy subsumption or equivalence form in fuzzy OWL. 3 Every instance of class in OWL is mapped into a fuzzy constraint with restriction value, 1. 4 Every property in OWL has a primitive fuzzy property form in fuzzy OWL. Every instance of each property can be mapped into a fuzzy constraint with restriction value, 1. The fuzzy OWL defines a namespace, Fuzzy Description Logic (FDL), and fuzzy constraints as shown in Table 2.
Table 2. Fuzzy constraints [4]
Rule A(a) ≥ n
A(a) ≤ n
…
3
Fuzzy Constraints …
Intelligent Context-aware System Using Fuzzy OWL
Fig. 1 illustrates the overall architecture of the proposed system. The system is divided into three parts: (a) data collection, (b) context representation, and (c) context inference.
Fig. 1. Overall architecture of the suggested system
3.1
Data Collection
In the suggested system, data are automatically collected from a variety of sensors, such as door sensor, gas range sensor, and air pollution sensor, etc. in the form of raw or binary data. We then quantify the collected data using a heuristic method based on a fuzzy set. This is because decision-making in the computer is based on discrete data representation, such as set theory, and it is difficult for a computer to decide a situation from those context data. For example, we divide the level of temperature or pollution in a home into ten steps by using a fuzzy set. To be specific, the temperature below 0℃ is level one, temperature between 0℃ and 10℃ is level two, and so on. In the same manner, the value of indoor pollution between 0ppm and 50ppm is level one, the value of indoor pollution between 51ppm and 100ppm is level two, and so on.
Next, we normalize the quantified data as a real number between 0 and 1. This is used for a weight value in the fuzzy inference formula. For instance, the fact that the value of air pollution is very high is more related to a fiery, dangerous situation than the fact that the door is open, or the TV is on. Thus, the former context has a higher value than the latter one. 3.2
Context Representation
Based on the normalized data and their relations to a situation, we describe the situation as fuzzy OWL. There are two steps to describe a context as fuzzy OWL. First, we convert factors into a context, which can be represented by a general set in OWL. A factor can be one of every concrete surrounding element to describe a situation, such as doors, TV, gas range, windows, etc. Second, we transform the OWL contexts into fuzzy OWL using constraints, as shown in Table 2. We specify an ‘fdl’ namespace to describe the context as fuzzy OWL, otherwise an ‘owl’ one. If a factor has an weight value x between 0 and 1, we represent the value as a property ‘fdl:value = x’ in fuzzy OWL, as follows. 3.3
Context Inference
To infer a situation based on the described fuzzy OWL, we classify each factor or element by its characteristics, and define a set of inference rules. If a situation happens at home, for example, we can classify the contexts into several classes, such as electronic home appliances, environmental information, family, location, and so on. Each class has atoms; for example, a class Family has atoms, such as father, mother, brother, sister, daughter, etc., and a class Environment has atoms, such as temperature, air pollution, and so on. Afterwards, we define inference rules using a conjunction operator (∧). We assume a home that is on fire as an example. The situation includes the facts that the level of temperature and air pollution is high, and whether the gas range is turned on or not. We define an inference rule for this fiery situation, as shown in Equation (1). TemperatureLevel(?k) × 0.7 ∧ AirPollutionLevel(?k) × 0.9 ∧ GasRange(?k) → OnFire(?k) (1) In Equation (1), each value represents the weight value of each factor that occurs in the situation of a house on fire. Through the definition of inference rule, we can infer the value of context OnFire(?k) using each parameter k. The parameter k includes a set of properties in each area such as kitchen, living room, and bathroom.
4
Implementation Examples
4.1
Context Representation
In this section, we demonstrate a whole process where the environmental data received from home network sensors are represented in context. We assume that the following environmental data are collected, as shown in the left part of Table 3. Table 3. Collected environmental data, and their conversion to context data
Collected environmental data Converted context data home.mainWindow = closed home.mainWindow = 0.1 home.gasRange.gas = outflow home.gasRange.gas = 0.3 home.gasRange.fire = on home.gasRange.fire = 0.3 home.TV = on home.TV = 0 home.family.daughter = in home.family.daughter = 0 home.temperature = 72.3 home.temperatureLevel = 0.8 home.air.pollutionLevel = 8 home.air.pollutionLevel = 0.9 The data describe that gas is flowing out from a gas range, the indoor temperature is high enough, and the level of indoor pollution is high. We can regard this situation as the scene of a fire. Therefore, we convert these environmental context data into real number values between 0 and 1 with a heuristic method before expressing them using the OWL, as described in the right part of Table 3. We then represent context information in the form of fuzzy OWL, based on the conversion rules proposed in [4]. Fig. 2 shows the fuzzy OWL context representation of the collected environmental context data. If we use fuzzy sets with weighted values between 0 and 1, we can describe the weighted value of fdl:value = “0.9”, as shown in the middle of Fig. 2.
Fig. 2. Fuzzy OWL-based context representation
4.2
Contextual Inference
We use an inference engine called FiRE to infer the user context and its corresponding services. The FiRE is based on Fuzzy Description Logic, and f-SHIN [8] provides sufficient grammars to use Description Logic [9], as shown in Table 4. Table 4. Examples of Inference rules
Inference Rule #1: temperatureLevel(?k) × 0.7 ∧ AirPollutionLevel(?k) × 0.9 ∧ OnFire(?k) GasRange(?k) → OnFire(?k) Inference Rule #2: OnFire(?k) × 0.5 ⋀ Home(?k, ?f) → Danger(?k, ?f) Danger(?k, ?f) In order to utilize the FiRE, we describe declarations of atomic-concept rules, an axiom, and an ABox. In Table 5, a part of ‘atomic-concepts’ represents an enumeration of each element in a set, and ‘roles’ describe the relationships between individuals. Table 6 specifies the axioms to demonstrate how it infers a situation where a fire broke out in the FiRE. Table 7 shows a defined ABox, which describes the converted values using fuzzy OWL. The values can be used by the FiRE. Fig. 3 shows the inference results for ‘Home Danger’ obtained from Table 7. The output value 0.8 means the degree of danger is 0.8 for the given situation in Table 7. Table 5. Atomic-concepts rules and individuals
(signature :atomic-concepts (person mother father daughter Temperature AirPollution gasRange) :roles ((has-gender :transitive t)
(has-descendant :inverse t) (has-child :inverse has-descendant) (has-sibling) (has-degree)) :individuals (home))
Table 6. Axioms
(implies person (and human (some has-gender (or female male)))) (equivalent daughter (and woman (some has-sibling person))) (equivalent OnFire (and Temperature airPollution GasRange)) (equivalent Danger (and OnFire (some inHome person))) Table 7. An example of an ABox
(instance Home temperatureLevel >= 0.8) (instance Home airPollutionLevel >= 0.9)
(instance Home GasRange >= 1.0) (related Home daughter inHome)
Fig. 3. Query result for ‘Home Danger’
5
Conclusions and Future Works
In this paper, we expressed user context information using a fuzzy extended language version of OWL, i.e. fuzzy OWL. Fuzzy OWL is suitable for expressing the user context necessary in a ubiquitous computing environment, while it also provides a basis for the effective representation of crisp sets and fuzzy sets. The method proposed in this paper uses an ontology language in a ubiquitous computing environment to describe user’s dynamic context information, independent of computer types and languages. Using the fuzzy concept, we can express problems and contexts in the real world, which are difficult to represent using the binary values of 0 and 1. We also provide a foundation for making further inferences in real world situations. When constructing an intelligent context awareness system with user context information, the result may vary depending on how we apply and implement the inference rules in the knowledge base. In future, we will provide more examples of real world applications, and implement an inference system using the fuzzy ontology that we have created. Using our complete inference system, we can construct an intelligent dynamic context awareness system for different types of languages and computers. Acknowledgment. This research was partially supported by Korea University.
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