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).
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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
James J. (Jong Hyuk) Park et al. (eds.), Mobile, Ubiquitous, and Intelligent Computing, Lecture Notes in Electrical Engineering 274, DOI: 10.1007/978-3-642-40675-1_23, © Springer-Verlag Berlin Heidelberg 2014
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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.
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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 3 4
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. Every instance of class in OWL is mapped into a fuzzy constraint with restriction value, 1. 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.
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Table 2. Fuzzy constraints [4]
Rule A(a) ≥ n
A(a)
≤
Fuzzy Constraints < < dl:individual fdl:name=“a”> = 0.8) (instance Home airPollutionLevel >= 0.9)
(instance Home GasRange >= 1.0) (related Home daughter inHome)
Fig. 3. Query result for ‘Home Danger’
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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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