A framework for context information management

Journal of Information Science http://jis.sagepub.com

A framework for context information management Stephen Shaoyi Liao, Joy Wei He and Tony Heng Tang Journal of Information Science 2004; 30; 528 DOI: 10.1177/0165551504047829 The online version of this article can be found at: http://jis.sagepub.com/cgi/content/abstract/30/6/528

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A framework for context information management

Stephen Shaoyi Liao, Joy Wei He and Tony Heng Tang Department of Information Systems at City University of Hong Kong, Hong Kong, The People’s Republic of China Received 3 May 2004 Revised 8 July 2004

Abstract. Context information plays an important role in contextaware mobile commerce (m-commerce) applications. Context information must be preprocessed, integrated and modelled before being stored and then used in a contextaware m-commerce application. Based on Knowledge Management (KM) theories and practice, this paper proposes a framework that uses several information technologies to collect, process, integrate, model, store and use context information. This framework will help effectively manage this information, as well as provide integrated context information about location, weather, time and user activities to enable context-awareness of an m-commerce application.

Keywords: context information; context-awareness; mobile commerce; knowledge management

1. Introduction Mobile commerce (m-commerce), considered to be a subset of e-commerce, deals with electronic transactions using mobile communication equipment. Context-awareness is one of the key enabling factors to

Correspondence to: [email protected]

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provide personalized services in m-commerce. A context-aware m-commerce application usually must make decisions like ‘Who is most likely interested in a new promotion?’ so as to deliver information to the users effectively. Context information, covering such aspects as location, time, weather, user activities and user preferences, is vital to this type of decisionmaking activity [1, 2]. In the literature of context-aware applications, discussions are focused on the experience and principles of building up a context-aware system [1,3,4,5]. In [1], a context-aware system framework is proposed. The framework is based on several software components, such as widget, aggregator and interpreter, and is widely accepted among those who build context-aware systems. In [6,7,8], the technology for the implementation of ad hoc systems is discussed. But little research has been done on the management of context information for m-commerce applications. Primitive context information is collected from different sources with different levels of abstraction. Its initial forms or elements therefore are usually not suitable for the necessary decision making. Primitive context information must be processed, integrated, modelled and represented before it is ready for use. Making context information ready for use is a challenging task because of its variety and diversity, which reflect three features of context information. First, the raw data of primitive context information are usually collected from different sources, e.g. different sensors in a context-aware system. The data types and formats from different sources are usually very different from each other. Second, primitive context information usually contains different elements with very different levels of abstraction, i.e. data, information and knowledge might all be included. Third, context information possesses some special characteristics (discussed in a later section) that make managing it even more difficult. A systematic approach is needed to manage the

Journal of Information Science, 30 (6) 2004, pp. 528–539 © CILIP, DOI: 10.1177/0165551504047829 Downloaded from http://jis.sagepub.com at PENNSYLVANIA STATE UNIV on February 5, 2008 © 2004 Chartered Institute of Library and Information Professionals. All rights reserved. Not for commercial use or unauthorized distribution.

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context information more effectively so as to directly use it in the decision making of m-commerce applications, which is the subject of this paper. How does an m-commerce application use information? This question is much harder to answer than it sounds. In order to manage and integrate the information resources, processes and technologies in mcommerce applications, we take a Knowledge Management (KM) view in the development of a framework for context information management. In recent years, the academic world has shown a consistent interest in organizational knowledge and KM [9]. KM is a good solution for improving operations and enhancing customer services [10], which justifies our study of m-commerce applications with the theoretical support of KM. However, it is a widely accepted fact that knowledge and KM are complex and multi-faceted concepts [11]. Many different views of knowledge, such as viewing it as an object or a process [10], lead to various perceptions of KM [12]. Since our research focuses on the management of context information for personalization in m-commerce applications, rather than KM issues at an organizational level, this paper concentrates on the necessary processes of managing context information based on the process view of KM, rather than the strategic, technical, Human Resources Management (HRM), cultural and other dimensions of KM. In this paper, we adopt the process view, which defines KM as a process of capturing, storing, sharing and using knowledge [13,14] in constructing a KM framework. It is our view that context information must be managed in several key processes in which Information Technologies (IT), such as data processing, data modelling and data mining, are well-suited to handle the complexity of information in m-commerce applications. The aim of the proposed framework is to provide guidelines for context information management for use in a context-aware m-commerce application. The framework covers how to manage context information for m-commerce applications from the perspective of the transition from data/information/knowledge to new knowledge, rather than the construction of a contextaware system, which is the main theme in the existing research. The architecture of the m-commerce application introduced by the authors in a previous paper [15] includes a context information centre, which provides the structured knowledge for the storage and utilization of knowledge in m-commerce applications. The framework in the present paper focuses on the processes of handling context information according to its characteristics, instead of the function of each com-

ponent of a context-aware system. It indicates the necessary steps to convert context information (data, information, knowledge) from its initial formats into new knowledge. Once the processes of data/information/knowledge collection, data preprocessing, information integration, information/knowledge modelling and knowledge representation are complete, the new knowledge will be in a structured and integrated format, and then stored in a knowledge base that can serve as a source for a knowledge-matching engine. This paper is organized as follows: section 2 discusses data, information, knowledge and KM as the theoretical foundation of the framework; section 3 elaborates upon context-awareness and its role in mcommerce applications; section 4 presents the framework in detail and discusses how to use the newly generated knowledge to support decision making in a context-aware m-commerce application; and finally, section 5 summarizes and concludes the paper.

2. Data, information, knowledge and KM The original elements of primitive context information exist in three forms: data, information and existing knowledge. A great deal of emphasis has been given to understanding the relationships and differences among data, information and knowledge [11]. It will therefore be useful to clarify the definitions of data, information and knowledge as they are used in this research, because such an understanding is basic in building our theoretical framework. We begin with the alternative views of data, information and knowledge discussed in the IT and organizational theory literature. A commonly held view is that ‘data are raw numbers and facts, information is processed data, and knowledge is authenticated information.’ [11] For example, Davenport defines this issue as ‘a form of content in a continuum starting at data, encompassing information, and finally ending at knowledge.’ [16] Tuomi [17], however, argues for an inverse hierarchy, that knowledge exists first, and when articulated, verbalized and structured, knowledge becomes information; that information becomes data when assigned a fixed representation and standard interpretation. This disagreement reveals that the key to understanding is a proper presumption of the varying dimensions required in various research exercises, such as context, usefulness, or interpretability, along which the hierarchy of data, information and knowledge may change accordingly. We agree with the vision that ‘data, information and


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Context information management

knowledge form a pyramid – with the broad mass of data at the base . . . and that the distillation process from data to knowledge involves value added activities.’ [18] Furthermore, in the context of m-commerce applications in this research, we propose a distinction in terms of two dimensions: usefulness for decision making, and level of knowledge abstraction (Figure 1). Data are the preliminary history from collection by sensors, which are the main source of context information in m-commerce. Therefore, from the perspective of decision making, data are of no use due to its meaningless nature. In this paper, a level of knowledge abstraction describes a degree of semantics extraction, i.e. a higher level of knowledge abstraction represents richer (more structured) semantics of information. This definition conforms to Mylonopoulos’s paper in which the authors believe that ‘KM introduces a higher level of abstraction . . . and this kind of abstract thinking is what enables actors to retool, restructure and redesign the organization.’ [19] After the processing and integration, ‘data are classified, summarized, transferred or corrected in order to add value, and become information within a certain context. Information serves to ‘inform’ or reduce uncertainty within the problem domain.’ [16] Thus, information is an aggregation of facts and figures, etc. Unlike data, information has meaning. It can provide a range of possible alternative actions for the system to choose among, with a higher level of knowledge abstraction. Knowledge, here, can be approximately regarded as personalized information (which may or may not be newly created) related to facts, procedures, interpretations, regulations, observations and judgments [11]. As Davenport says, ‘Knowledge has the highest

value . . . the greatest relevance to decisions and actions.’ [16] It represents the highest level of abstraction and helps us choose the most appropriate solution(s). Notably, one of the most common taxonomies of knowledge is presented by Nonaka [20], who explicates two types of knowledge: tacit and explicit. Tacit knowledge is rooted in action and experience and is embedded in an individual’s mind, while explicit knowledge can be articulated, codified and communicated in symbolic form. Therefore, tacit knowledge can only be processed and shared by means of observations and apprenticeship [18]. Both tacit and explicit knowledge are very important in context-aware m-commerce applications. The proposed framework suggests various techniques to manage these two kinds of knowledge in order to serve decision making. Since the three elements of context information have different data types/formats and different levels of knowledge abstraction, they must be integrated, modelled and represented so as to create new integrated, structured and explicit knowledge to be stored and used in m-commerce applications. We adopt the process view of KM, which focuses on information flows and processes, for the construction of our framework. The process view is one of the main streams in KM research. Generally, there is no consensus on the constituent processes. The steps of diverse process models proposed by different scholars include, for example, create and source, compile and transform, disseminate, apply and realize value [21]; identify, collect, adapt, organize, apply, share, create [22]; and identify, capture, select, store, share, apply, create, sell [23].

Fig. 1. Elements of context information.

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To facilitate the framework construction and demonstration, we take Alavi and Leidner’s frame for reference. They have developed four sets of ‘knowledge processes’ for knowledge systems: creation, storage/retrieval, transfer and application [11]. Incorporated with the characteristics of a context-aware mcommerce application, the context information (which may be data, information and knowledge as the elementary inputs) is collected rather than created, which is one way it differs from organizational knowledge. Moreover, before it is ready for storage and transfer, the context information must go through various processing stages in order to achieve a uniform structure, representation format, level of abstraction, etc. Hence, based on the literature and the special attributes of context information argued above, we propose a framework that includes the following processes: data/ information/knowledge collection, data preprocessing, information integration, information/knowledge modelling, knowledge representation, knowledge storage and utilization. The collection, preprocessing, integration, modelling and knowledge representation activities result in newly generated explicit knowledge. These activities also provide a platform with which we can represent knowledge in a structured and integrated format and, consequently, store it in a context knowledge base for the future decision-making activities of the knowledge-matching engine (Figure 2).

3. Context Awareness and M-commerce Context information in the field of ‘context-awareness’ is defined as ‘knowledge about the user’s and IT devices’ state, including surroundings, situation, and to a less extent, location’. The research topic of contextawareness originated from Mark Weiser’s pioneering presentation in 1991 [24]. As an important application of context-awareness, m-commerce suffers from the limitations of mobile devices (small screen size, troublesome input method, portability concerns, etc.). Development of more effective types of human interaction with computers in m-commerce is therefore necessary, and context information plays a very important role in achieving this goal. Context-awareness is also a great enhancement to today’s m-Customer Relationship Management (m-CRM) applications. In the newest systems, a context knowledge base records a user’s historical context knowledge. A matching engine then integrates context knowledge with content knowledge and user knowledge to make predictions. Therefore, rather than answering a question like, ‘How

can I treat different customers differently?’ as the traditional CRM systems do, a context-awareness-enabled m-CRM system addresses a question like, ‘How can I treat different customers differently according to their different contexts’, so as to provide ‘relevant services to the right customer at the right time in the right way’, which is considered to be one of the key factors in the success of m-commerce. Three capabilities are considered necessary for an mcommerce system to become context-aware [15]: (1) the capability to share a common ontology, (2) the capability to sense context, and (3) the capability to reason about context. Based on these ideas, a context information centre for m-commerce applications is introduced by the authors, which makes use of context information in order to provide task-relevant information to the users and then make the m-commerce application contextaware [15]. The centre is equipped with the three above-mentioned capabilities. First, the context information is defined with a common ontology: user activities, location, user preferences, weather and time. Second, different methods are used to collect context information. Third, a knowledge-matching engine is created to provide relevant information to the right users. Managing context information so as to create such a centre is a challenging task because of its special characteristics, and thus it requires a systematic approach. The special characteristics of the context information are summarized as follows: • To begin with, the context information is temporal. As context-aware systems are typically characterized by frequent changes, the majority of information, such as the noise level or the position, is therefore dynamic. • Next, the context information is usually imperfect. The imperfection may result in negative impacts on computing, reasoning, or decision-making processes. • In addition, the context information is at different levels of abstraction. Raw data collected from different devices is usually abstracted into a hierarchical structure to provide knowledge at different levels of abstraction according to various requirements. • Finally, context information may be highly interrelated. It may include relationships among people and devices. Therefore, the current status of a user could be derived through other users. Based on these characteristics, it is generally recognized that context information must be effectively managed and converted to new, integrated knowledge so as to provide relevant context information to an m-commerce application and then make that application context-aware. This


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motivates the authors to propose the following framework for effective management and use of context information in context-aware m-commerce applications.

4. A framework for context information 4.1. Architecture of the framework The architecture of the proposed framework is presented in Figure 2. The framework includes several processes (data/information/knowledge collection, preprocessing, integration, modelling and representation), a context knowledge base and a knowledgematching engine. The collection process provides an interface for the input of the primitive context information, including raw data, information and tacit/explicit knowledge from various sources. Different techniques are employed in this process to collect different types of primitive context information. The preprocessing process transforms raw data into information. The integration process combines the

information from the two previous processes together to produce integrated information. The modelling process takes existing tacit/explicit knowledge and the integrated information as inputs to produce new, integrated explicit knowledge. The representation process represents the knowledge in a structured format. The knowledge is then stored in the knowledge base. This context knowledge base, together with two other knowledge bases (user knowledge and content knowledge), is ready for use in decision-making activities. The details of the latter two knowledge bases will be discussed in separate papers. But in short, the user knowledge base provides knowledge about user profiles and user preferences while the content knowledge base provides knowledge about the content of products/services from the merchants.

4.2. A scenario to illustrate the framework To help better understand the proposed framework, a simple scenario using a campus network called CTNET

Fig. 2. A scenario to illustrate the framework.

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is presented to illustrate the framework’s ideas and processes. CTNET is a comprehensive system that provides context information about all staff and students for various applications, such as m-commerce. The construction of a user’s context information will involve a variety of sources including data, information, and tacit/explicit knowledge, in order to cover all the necessary areas. For example, the system requires user background information such as the following: Mike is a student at CT University who is fond of PC Games, especially the Star Trek series made by Solaris. The system may also need information about Mike’s current activity, his current mood, etc. Different techniques and processes are then required to manage the user’s context information.

4.3. Data/information/knowledge collection The sources of the context information can be various. Location, orientation, speed, acceleration, temperature and so on could come from any number of different sensors. Information about the users’ future activities and events may come from their schedules and agendas, whereas users’ social situation and relationships (e.g. ‘Davison is Mike’s course tutor’) may come from other context sources. The task of this process is to gather all kinds of context data from various sources, and make them ready for future processing. Having been collected in various ways, such as with infrared beams, wireless devices and so on, the raw data will be sent to the next process for preprocessing. It is worth noting that this process not only collects raw data directly from sensors, it may also gather available context information with higher levels of abstraction, i.e. context information and context knowledge. Context information is considered to be data with a higher level of knowledge abstraction with the following characteristics: (1) contextualized, i.e. the purpose of the data is known; (2) categorized, i.e. the unit of analysis or component is known; (3) calculated, e.g. through a statistical or mathematical analysis; (4) corrected, through removal of errors; and (5) condensed, by being summarized or tabulated. Information is collected from existing codified and documented sources like books, reports, spreadsheets and databases. Since explicit knowledge is knowledge that is also codified and documented in a paper-based or electronic format, the collection of explicit knowledge is done in the same way as information in our framework.

Several techniques are available in the field for tacit knowledge capturing, such as interviews, on-site observation, brainstorming, concept mapping and the Delphi method [25]. Based on the characteristics of context information (location-based, activity-sensitive, etc.), interviews and on-site observation are employed in our framework as the techniques for collecting tacit knowledge. In conclusion, the collection process collects various data, information and tacit/explicit knowledge through different channels, and then sends them to the information integration and knowledge modelling processes, respectively. In our example using CTNET on a university campus, Mike’s present coordinate data are provided by Global Positioning Systems (GPS) devices or indoor beacons [1, 28] and transmitted to temporary storage for preprocessing. His timetable information is collected from the student curriculum schedule. We might also have some explicit knowledge like, ‘Mike likes action movies and people who like action movies also like First-Person Shooter (FPS) games.’ This kind of knowledge can be obtained using a data mining approach such as association rules mining. Moreover, some tacit knowledge about Mike’s profile and behaviour could be, ‘Mike looks relaxed now and I am pretty sure he would enjoy some entertainment activities such as buying and playing FPS games’. This kind of tacit knowledge can be collected through observation, interview, or expert’s judgment.

4.4. Data preprocessing Context information mainly comes from various sensors that are widely used in current context-aware systems [6]. Sensors are portable or already installed in the environment. However, the raw data from those sensors are usually not ready for the use of contextaware applications because of its diversity and variety. Information preprocessing specifically refers to dealing with the raw data coming from sensors and turning it into useful information. In most m-commerce applications, data from sensors are inaccurate, incomplete, or in wrong/different formats according to the specific characteristics of context information. Consequently, some preprocessing procedures to purify raw data and abstract them to a higher level for future use are necessary. These procedures include preparing and analysing the raw data to generate useful information. For example, the change of location and the moving characteristics may involve information about the user’s profile concerning his walking habits, goals and wishes. We can use historical data as the training dataset to discover the


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user’s walking behaviours and activities. The features of the data sequences can also be extracted by analysing the dataset and its distribution chart. When a user moves with portable sensors, the context data will be recorded and then analysed against the rules generated from his profile. The current actions of this user can then be identified. Moreover, an action sequence, which is made up of some simple actions, also reflects a specific activity of this user. For example, from the historical data collected by sensors, an event sequence like, ‘Mike stands up from the chair in the office, turns around for x degrees, walks forward for y meters, then makes a right turn . . .’ indicates that ‘Mike is going to have a cup of coffee in the common room’. Lee [6] and Bieber [7] give examples of how to process raw data from sensors to generate useful user information. Their approaches have proved fairly effective in their context-aware systems. However, the limitations of their studies are also obvious. First, the actions of different users have different characteristics. Second, their activities represented by action sequences also vary. In a context-aware system, which provides services for multiple users, the profile of every user should be created in order to identify users’ activities. Third, analysing context information data from sensors and extracting features, e.g. building user profiles manually, is a difficult job, if not impossible. Therefore, it is very necessary to use appropriate methods to analyse the raw data and automatically create user profiles. The proposed framework adopts several data analysis techniques for the data preprocessing. First, automatic data analysis is a widely studied topic, and many mature techniques and methods are available to provide more accurate data analysis results. Second, these techniques can help analyse a large amount of context information data related to each user automatically and efficiently. Finally, more accurate analysis will help extract more features and characteristics from the same amount of raw data, and then fewer sensors are enough to provide information for further use. The data preparation is one of the most important processes/steps of data mining. Many techniques are employed in this process to prepare the data and make them ready for actually conducting the mining activities such as classification and clustering. Those techniques, including data cleaning, reduction, normalization and discretization (segmentation or interpolation), are major tools for the preprocessing process in the proposed framework. Moreover, it is highly desirable to carry out some data transformation processes before performing the 534

data analysis and mining, as direct, raw-context data processing may lead to incorrect analysis results in addition to a slow speed. An appropriate transformation process will greatly reduce the amount of redundant data, increase the speed and alleviate the influence of noise contained in the raw data. Possible data transforming processes include DFT (Discrete Fourier Transformation) [26] or DWT (Discrete Wavelet Transformation) [27]. In our example using CTNET on a campus, portable sensors installed in Mike’s clothes keep generating realtime data streams related to his activities. Those data streams will be preprocessed with an appropriate technique used in the data mining preparation to eliminate noise, and their features will be extracted to construct Mike’s present sequence of activities. This sequence will be compared with the sequences stored in his profile database, and the most similar one will be used to predict Mike’s current activity.

4.5. Information integration In most context-aware applications, we need to deploy a number of different sensors to collect context information. Using multiple sensors provides better accuracy and more specific inferences. It also increases the reliability of the system, i.e. even if some sensors fail, the system still remains operational. For example, GPS devices, Global System for Mobile Communication (GSM) or General Packet Radio Service (GPRS)based mobile positioning devices and indoor beacons [1, 28] are usually employed to get the accurate position of a user. Therefore, a process of integrating the context information from different sources is needed. Context fusion (also called sensor fusion) [2] is an important technique for the integration process for the following reasons. First, context fusion uses a variety of computational mechanisms (such as parsing, filtering and clustering) to extract context information from raw data collected by a number of sensors. Second, it also results in the basic abstraction of context information, e.g. from GPS coordinates to on-campus or offcampus. Finally, independent context fusion components could increase the reusability and customizability of the system. The impact on the system’s functions from the change of the hardware sensor will be minimized, so little will change except the context fusion mechanisms. Context fusion is the primary fusion and abstraction of context information. It results in preprocessed, refined, fused information with a higher concept level of abstraction. It hides the details of different physical


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Fig. 3. Context fusion.

devices and provides more abstracted context information to the modelling process. From the point of view of the modelling process, the context information from context fusion is regarded as being from logical sensors (Figure 3). In our example of CTNET on a university campus, the portable GPS device indicates that Mike is in the academic building at a particular point in time, and the information from an accelerometer and a gyroscope indicates that Mike is almost stationary. Moreover, Mike’s timetable is also available for use. Fusing these cues about Mike’s present status leads to a confident conclusion that he is attending a class at that particular time and is unlikely to be willing to be disturbed.

Various approaches are available for constructing the context information fusion mechanisms, including the Bayesian network [29,30], fuzzy inference model [31,32], Dempster-Shafer evidence theory [33,34], and others. In the proposed framework, the DempsterShafer theory is utilized. The attraction of this theory over other existing techniques is that it enables representation of the uncertainty attached to the evidence-combination process. This is a powerful and coherent way of representing aspects of a combination such as the quality of evidential sources, the evidence of the user’s assessment and the reliability of evidence. It does not require any a priori probability distributions. Context fusion can also be viewed as an application of data fusion [35], which is a well-proven area that has been studied for many years. Therefore, many techniques, methods and theories of data fusion can be directly applied to the area of context fusion, such as those in the work of Kleine-Ostmann [36] and Hall [37].

4.6. Information/knowledge modelling There are two main tasks in this process. One is knowledge abstraction and the other is knowledge codification. The integrated information from the integration process hides hardware details and provides some kind of elementary abstraction. However, in most cases, information output from the context fusion should still be refined, summarized and abstracted to generate new knowledge. Moreover, the explicit knowledge from the collection process may also need to be reorganized and restructured. Therefore, a process of ‘information/ knowledge modelling,’ which produces a higher level of knowledge abstraction, is necessary. The other task in this process is to codify the tacit knowledge that comes directly from the collection process. Before tacit knowledge can be used in a computer-based system, it needs to be converted into rule-based explicit knowledge for use in decision making. The knowledge abstraction procedure in this process organizes the context information and explicit knowledge into desirable forms and sends it to other specific processes. Furthermore, organizing context information and explicit knowledge in a hierarchical, multilevel structure will help meet various requirements of different applications. The following scenario illustrates the ideas in the information/knowledge modelling portion of the framework. The CTNET has other evidence to help determine Mike’s present status: the knowledge that ‘Mike’s class attendance rate is 100%’, and the information that ‘Mike is now in the academic building’. Furthermore, his schedule indicates that he should be attending a class at this time. Combining all available information/explicit knowledge, a conclusion about Mike’s current status can be drawn. Then, a


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piece of information like ‘Mike is in class’ can be further abstracted to a higher level such as ‘Mike is busy’.

Two approaches are adopted in our framework to construct higher levels of knowledge abstraction. The first one is the generalization/specification technique used in the object-oriented approach. This technique allows the user to generate a higher level of abstraction from the knowledge at a lower level of abstraction. Its construction requires people’s knowledge, experiences and judgment. Another technique is proposed by the authors in a separate article [38]. In that approach, knowledge abstraction is represented by a concept hierarchy that is directly created, without people’s intervention, from existing data stored in a relational database using a Semantics Proximity (SP)-based data mining technique proposed by the authors. Another major task in this process is to codify tacit knowledge (as its input) into explicit knowledge (as its output). Knowledge codification involves organizing and representing knowledge to make it ready for computer-based decision making. The techniques used for codification include decision tree, decision table and decision frame [25]. After codification, tacit knowledge will become application-specific knowledge; it will be visible, explicit and easy to access. Back to our CTNET example again. Based on two pieces of explicit knowledge, ‘Mike likes action movies in general’ and ‘People who like action movies also like FPS games’, and a piece of tacit knowledge, ‘Mike looks relaxed and in a good mood now’, a new piece of explicit knowledge like ‘Mike would like to buy an FPS game now’ could be generated. Furthermore, this explicit knowledge can be codified to a new context-aware-specific rule: ‘Send a promotion message about FPS games to Mike if he is within 50 metres of the store now.’

In conclusion, the main function of this process is to combine information and knowledge, then generate new explicit knowledge and send it to the knowledge base for presentation, storage and future use. Moreover, the knowledge stored in the base can sometimes be one

of the sources of this modelling process. Thus, knowledge is generalized and combined iteratively so as to construct a hierarchical knowledge structure.

4.7. Knowledge representation After being modelled, context knowledge still needs to be represented in an appropriate format for its storage in the knowledge base and the use of the knowledgematching engine. A standard specification language is needed to represent the semantics of the context knowledge, and it should satisfy the following requirements: this language should be capable of expressing context about relationships between all kinds of objects, and should be independent of any types of sensors and services currently available. The language should also be capable of describing context in a structured way for the retrieval of semantic information, and it should be expressed in a general format. From a more practical point of view, context information transfer is normally performed in the forms of publishing, subscribing, or querying procedures in a context-aware m-commerce application. Hence, the difficulty of building a standard specification language to represent context knowledge is one of its essential problems. An Extensible Mark-up Language (XML)based specification language will be used for knowledge representation in the proposed framework. The Context Specification Language proposed in the Context Fabric System [28] provides a flexible way of stating needs regarding context information. It is a simple XML-based language that supports subscribing to context events and querying for context states. Making use of the powerful representation ability of XML, this language can address context specification that contains very complex semantics. It is fairly generic and applicable to a wide range of context-aware applications. The proposed framework uses an XML-based language, Resource Description Framework (RDF), to

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create a unified representation format for context knowledge. For example, a piece of information regarding user activity, ‘Mike (the user ID) has an annual meeting in the conference room at 2003–01–15 14:00’, can be described as follows in RDF format. 4.8. Knowledge storage and utilization (matching engine) After the context knowledge is stored in the context knowledge base and is ready for use (we assume that both the user knowledge base and the content knowledge base are also ready for use), the knowledgematching engine can start finding matching users for a given product/service from a merchant. It will take all three parts of knowledge into consideration and recommend a list of users who are most likely to be interested in the product/service being promoted. The recommendation can be derived via Bayesian belief network and rule-based reasoning. The Bayesian network allows a combination of declarative knowledge about structure and empirical knowledge about probabilities. There are vigorous mathematical proofs behind the model and the network can link observations to explanations. The Bayesian network provides prior probability distributions and is a good way to deal with reasoning with uncertainties. One of the more famous applications of the Bayesian network is in Microsoft Office Assistant (the Lumière project). In Lumière, it models the whole chain, from observation to adaptation. The ruled-based approach relies on known matching patterns. Thus, as soon as a known match is detected, the system will trigger an alarm. The rule-based approach is generally supported by means of expert systems that are equipped with knowledge about matching. It provides a very good support to multifactor (e.g., content, context and user) decision-making activities. However, a system with this approach cannot cope with unknown matching, nor can it reason with uncertainties. Therefore, we combine both the Bayesian network approach and the rule-based approach to construct the knowledge-matching engine of our framework. The focus of constructing the matching engine is to build a Bayesian user model to predict the user’s behaviour with support from the three knowledge bases (content, context and user). The construction of a Bayesian network usually relies on the modeller’s experiences and is quite time-consuming. In our framework, a new method, ‘Mining Bayesian Networks from a relational database’, proposed by the authors [30] for

constructing Bayesian networks from a relational database, will be used. In that approach, historical data of the user’s preferences and behaviour are stored in a relational database and will be used to automatically construct the Bayesian network. A Bayesian network generated by that method will include both the nodes together with their dependencies and the nodes’ prior probabilities. The historical data of the user’s preferences and behaviour will be stored in and managed by a relational database and will be used to build the user knowledge base when necessary. A Bayesian network software sheet will also be used to calculate the post probabilities after some external events such as a piece of context knowledge or a piece of content knowledge e.g. a customer message is received. This kind of post probability will be used to predict the user’s behaviour. The engine’s decision making will be based on Bayesian probabilistic and rule-based reasoning capabilities. The knowledge from both the content knowledge base (message contents) and context knowledge base (location, time, weather, activity) will be used as external events for the user Bayesian network (user knowledge base). The probabilities of different users’ interests in a particular event, e.g. a merchant’s message, will be calculated. Higher probabilities indicate the users who are more interested. The matching will then be done. Let us use the example to further illustrate these ideas. Based on data, information and knowledge concerning Mike’s preferences, a Bayesian network has been constructed using the method proposed by the authors. The network indicates that Mike’s interest in buying a computer game at a particular time is dependent on the type of game, the price and the location (Mike’s distance from the shop at that time). The network also shows the prior probability for Mike to buy a computer game is 35%. Assume that an ad, ‘The best-selling computer game, Win-Win of the Star Trek series, is on sale at SuperGames!’ is received, the context knowledge base indicates that Mike is within 50 metres of SuperGames, and he is moving toward the shop. Furthermore, it is known that there is no class for Mike to attend at that time, according to his timetable. The Bayesian network will then work out that the post probability for Mike to buy that game is now 75%. According to the predefined rules, Mike is identified by the knowledgematching engine as a matching case with a high level of confidence.

5. Conclusion A context-aware mobile application requires context information to support its decision-making activities


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related to delivering relevant information to the right customers. The context information is made up of very diverse elements from various sources with different levels of knowledge abstraction. The process view of KM is employed in this paper to propose a framework for effective management of this context information. The framework covers several major knowledge-related processes and takes into consideration the diversity and variety of the context information. The processes include collection, preprocessing, integration, modelling and representation, which enable the transition from data, information and knowledge to new knowledge and the procedure of upgrading the level of knowledge abstraction. The framework also indicates that the newly generated knowledge will be stored in a context knowledge base and used by a context knowledge-matching engine to support decision-making activities. The Bayesian network and the rule-based approach is then used in the matching engine to find matching users for a given product/service in a contextaware m-commerce application. A context-aware mobile application is used throughout the paper to illustrate the use of the proposed framework. However, this framework can also be applied in the management of any context-aware system.

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Acknowledgement This research is fully supported by City University of Hong Kong’s strategic research grant project 7001640.

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