A practical implementationcon

The unified knowledge content management approach for a digital archives project has been implemented in the National Museum of Natural Science (NMNS) in Taiwan

NDAP introduced in Section 1.5. A total of fifteen domains in zoology, botany, geology and anthropology participate in the digital archives project of NMNS. All domains are coordinated and integrated by the information technology integration project to achieve unified processes, content structures, and knowledge-based management system development.

The unified process shown in Section 3.3, including the collecting, digitizing, editing, organizing, publishing, and accessing phases has been specified through discussions. All specialists in each domain must follow the standards and specifications of each stage. A single and integrated knowledge-based content management system (KCMS) was developed, by which the content creation, management, and publication described in Section 3.5 was fulfilled collaboratively among all content specialists and IT specialists. A multi-layer reusable knowledge content structures including core, advanced, and innovative knowledge elements defined in Section 3.4 was designed. These structures are managed and maintained by KCMS. All specialists applied them to edit, organize, and maintain content under a standard and consistent process. All the finished and verified content created by specialists was converted into XML-based content structures for publication. All knowledge content constructed under a single global classification hierarchy-based system and the interchange formats among institutes was also converted into XML-based structures. The XML-based content with assigned XSL templates was transformed into Web pages during accessing.

Users could access content through the integrated knowledge portal through a classification hierarchy-based browsing and metadata search query interface. Figure 3.7 shows the entire implementation system. Figure 3.8 shows the global concept hierarchy and metadata creation.

Figure 3.9 shows the creation of a core knowledge unit. Figure 3.10 shows the creation of an advance knowledge unit. Figure 3.11 shows an example of knowledge unit for interpreting the knowledge content of a species of vascular plant. Figure 3.12 shows an example of organizing a knowledge group for present an exhibition topic of vascular plant by the color of their flower. Figure 3.13 shows an example of organizing a knowledge network for present an

relating knowledge units between insect and vascular plant.

Figure 3.7 Implementation system of UKCM model

Figure 3.9 Creating a core knowledge element

Figure 3.10 Authoring an advanced knowledge element

Figure 3.11 An example of knowledge unit of a species of vascular plant

Figure 3.12 Authoring a knowledge group

Figure 3.13 Authoring a knowledge network

Chapter 4

Knowledge-based ubiquitous learning service model

4.1 Introduction

Using the UKCM model described in Chapter 3, large quantities of knowledge content can be created and managed via a unified approach. Based the unified knowledge bases, numerous applications can be developed through system generation or art design. However, this approach remains inadequate to fulfill the goals of creating a knowledge-based digital museum. This chapter establishes a ubiquitous learning service model that reuses and extends the content of unified knowledge bases to achieve the goals of the knowledge-based digital museum. The ontological knowledge base layer of the KBDM framework can further be constructed by applying ontology to represent the unified knowledge bases in addition to user context and usage. Not only does it cover ubiquitous, proactive, adaptive, and collaborative properties, but this application can also efficiently reuse and diffuse large quantities of knowledge content. Consequently, the adaptive service agent layer and ubiquitous digital museum service layer in the KBDM framework are constructed in a manner that does not include the knowledge discovery module. The details are presented below.

Museums attempt to create a learning environment by using digital technologies to produce and deliver knowledge. The resulting learning environment exists both onsite as digital interactive content, and online on Web sites [33, 35]. The rapid evolution of information and communication technologies encourages museums worldwide to develop mobile learning solutions by creating extra channels for users with mobile handheld devices to supplement conventional docent and audio guides, and current digital technologies [29, 52,

way that museums interact with visitors. Some applications designed for mobile devices can enhance visitor experience in museums [28, 41, 56, 67, 86, 87]. However, most mobile learning projects for museums, particularly in Taiwan, have not successfully developed of onsite tour guide applications for exhibitions. A friendly interface, attractive application [64, 87], multimedia presentation [86], and interactive accessibility [49, 56] are major concerns in such projects. Very few projects combine museum-wide content and services with related domains, applications and projects to create a ubiquitous, proactive and adaptive learning service. Therefore, most relevant knowledge cannot be integrated and reused; the learning content is uniform and constrained to particular domains; the learning environment is restricted in locations of museums, and services cannot be adapted to individual learners.

This study addresses some major factors in addition to these general design factors.

Major factors include the type and subject of a museum (art, history or science), audience types and their requirements (student, teacher, general public or expert), integration of related content resources (collection, exhibition, education and entertainment) and the integration of service and business models with the physical museum (inside and outside the museum). This study proposes a model of ontological knowledge-based mobile learning with ubiquitous, context-aware and personalization services to fulfill these key factors. A ubiquitous learning service [28] enables learners to undertake learning activities covering the pre-visit, onsite-visit and post-visit stages. A context-aware system serves people intelligently and interactively using users’ contextual information, such as temporal and spatial information, in the museum around them [15, 51]. A personalization service provides a new communication strategy based on a continuous process of collaboration, learning and adaptation between a museum and its visitors during all learning stages [45].

Ontology defines the characteristics of a formal, explicit specification for a shared and common understanding of various domains [89]. A unified knowledge base developed in a digital archiving project for the NMNS acts as the kernel component to integrate content from

exhibition, education and collection resources in a museum. Ontology acts as a common, sharable knowledge concept for communication between learners and the unified knowledge base. The learners’ learning records can be represented by an ontological usage profile aggregated from the content profiles at a conceptual rather than titular level. These usage profiles identify useful usage patterns of individuals, groups and global learners. These patterns are used to recommend content for active learners.

A practical mobile learning project based on the proposed model was implemented and opened in July 2005 at the life science hall of the NMNS. This model is likely to be extended to the entire museum before the end of 2006.

This remainder of this chapter is structured as follows. Section 4.2 describes the learning service evolution from e-learning, m-learning, to u-learning. Section 4.3 describes a mobile learning scenario in a user-centered ubiquitous, context-aware and personalized learning environment. Section 4.4 presents a model that is modularized into three layers, ubiquitous learning service layer, adaptive application layer and ontological knowledge base layer, to realize the design issues. Section 4.5 describes an ontology-based model, which is designed to denote and combine learning content for collection, exhibition and education, as well as user context and usage. Section 4.6 describes a personalization service to carry out recommendations adaptively and proactively during the pre-visit, onsite-visit, and post-visit stages for each active learner.

4.2 Learning scenario

The following scenario shows how a student learns in a ubiquitous, context-aware, personalized environment with a knowledge-based mobile learning service. The scenario is

Figure 4.1 Learning scenario during pre-visit, onsite-visit, and post-visit stages 4.2.1 The pre-visit learning stage

Assume that a student wishes to visit a natural science museum for two hours, concentrating on exhibitions about fossils. Before visiting the museum, the student creates a learning plan at home using the Internet by specifying his subjects of interest, visit date, and stay duration. The system recommends all relevant learning content from a global perspective of the museum based on his demography and preference for fossils. The student then determines his final learning plan according to the recommendation, and registers for his planned visit.

4.2.2 The onsite-visit learning stage

When the student visits the museum, he downloads his previously created learning plan and conducts his learning activity by a handheld device. If the student does not wish to learn by planning, then he can learn by following some learning packages prepared by the museum, or learn freely without constraints. All three learning modes are served in a context-aware

environment, and the system automatically pushes relevant guiding maps, learning content and messages based on his location and related context. The student may, while learning, follow the original visiting plan about fossils, or he may be interested in another exhibition about dinosaurs, which is not in the original plan. In this case, some exhibition items and messages of science educational activities about dinosaurs are also delivered to the student.

All learning behavior is tracked and analyzed to provide further intelligent and proactive recommendation services to the student.

4.2.3 The post-visit learning stage

The student can continue learning via the Internet at any place and time after leaving the museum. The learning service recommends additional content to the student based on his preference and learning records tracked by the system during his onsite visit. The recommendation includes extra exhibition content that interests the student, but which he has not yet appreciated during his onsite visit. Other related content in collection and education knowledge bases are also recommended to the student. The system tracks and analyzes the student’s learning behavior from his rating and navigation. Hence, the automatic recommendation service for the student’s next pre-visit plan or post-visit learning is close to his requirements.

4.3 Learning service model

A knowledge-based ubiquitous learning service model in a museum (see Figure 4.2) can be sketched out according to the learning scenario in Section 4.3, and modularized into three layers, namely the ontological knowledge base layer, adaptive application layer and ubiquitous learning service layer.

Figure 4.2 Knowledge-based mobile learning service model

4.3.1 The ubiquitous learning service layer

The ubiquitous learning service layer provides pre-visit, onsite-visit and post-visit learning via a single service portal. The pre-visit learning service provides an ontological interface to determine the learning content chosen by a user or recommended by the system.

The learner can arrange a visiting plan, and register it before visiting. The learner can download a registered learning plan to choose package learning or free learning when visiting the museum. Our implementation discards infrared, ultrasound, WiFi (Wireless Fidelity) and RFID (Radio Frequency Identification) solutions, due to their problems with operating efficiency, roaming and usability in exhibitions. Instead, a context-aware system based on ZigBee/IEEE 802.15.4 technology [48] was developed to serve learners by determining their demography, preferences, interests, locations, stay duration and visiting behavior. The

post-learning service allows learners to continue learning from more recommended content related to past onsite visits.

4.3.2 The adaptive service and content layer

The adaptive service and content layer provides several services for learners in the pre-visit, onsite-visit, and post-visit stages. The learning planning service provides an ontology-based interface allowing a learner to create a learning plan during the pre-visit stage.

The mobile learning service provides plan-learning, package-leaning and free-learning modes for a learner to proceed with his learning activity during the onsite visit. The service collaborates with the context-aware service to transmit requests to the content service during the onsite visit, and reminds the learner of his time spent and his current learning status. The context-aware service senses the learner’s temporal and spatial context, and notifies the content service to deliver the appropriate guide maps, learning content and related activity messages to the learner. The user tracking service tracks the learning behavior of a learner during the onsite visit to capture the preference information for the personalization service.

The learner’s learning activities are dynamically tracked and recorded to refresh his learning preference of each learner. The personalization service adaptively and proactively recommends learning content to the learner in every stage. The content service delivers relevant content from requests of the learning planning service, the mobile learning service and the personalization service.

4.3.3 Ontological knowledge base layer

The ontological knowledge base layer generates and manages learning content, and maintains user context information and learning records for all learners. This layer consists of three components: ontology, unified knowledge base and user context & usage base. The ontology provides common and sharable concepts to denote the unified knowledge base and the user context & usage base. The unified knowledge base comprises a multimedia database

an ontology-based concept hierarchy for content creation, management and publication. The user context and usage base includes user profiles, learning records, usage profiles and usage patterns. Section 4.5 describes in detail the unified knowledge base and the user context &

usage base.

4.4 Ontology-Based content and user context modeling

Ontology has been applied in the past to digital archives, digital museums and museum-related e-learning projects to provide shared and reusable knowledge standards from user and system perspectives. The HowNet approach [23] is adopted herein to build a unified natural and cultural ontology for NMNS (see Figure 4.3). This study adopts the unified classification hierarchy from our previous work, and extends knowledge concepts about exhibition and education topics in natural science to establish the ontology. This ontology plays several significant roles in this study. First, this ontology serves as a sharable thesaurus describing entities, attributes, relationships and events for the unified knowledge base and user context. Second, the ontology maps content onto a simplified and standard specification, and processes it consistently for the learning service. Third, the concept tree of the ontology can be naturally employed to design access interface and calculate the similarity of concept definitions from the user context and the unified knowledge base in the personalization service.

Figure 4.3 A unified natural and cultural ontology

4.4.1 Modeling of Knowledge-based Learning Content

A global knowledge system with an ontology-based concept hierarchy and relationships in a domain or between domains was previously built in the Chapter 3. In order to support a mobile learning service, we extend the conceptual modeling of enterprise domain knowledge system and multi-layer reusable knowledge content structures in section 3.6 by applying ontology to connect the knowledge entity and user context entity. Figure 4.4 illustrates the conceptualization of knowledge elements in the unified knowledge base and the user context

Figure 4.4 Ontological content and user context modeling

The multi-layer reusable content structures defined previously in Section 3.4 can express and organize many classes of content. An entity called knowledge element represents the superclass of all content, and consists of core knowledge elements, advanced knowledge elements and innovative knowledge elements. A core knowledge element is the basis of knowledge content, and consists of a multimedia object and its semantic metadata. An advanced knowledge element is further structured from a set of core knowledge elements, and can be a multimedia document, a knowledge unit, a knowledge group or a knowledge network.

A multimedia document integrates several core knowledge elements to describe an item related to an object of nature or culture (one species of bird or plant, or an artifact), or an exhibition and education topic. A knowledge unit possesses a hierarchical structure, and is

adopted to structure all related multimedia documents to interpret a particular object or topic.

All knowledge units can be categorized into three subclasses, namely the collection knowledge unit, exhibition knowledge unit and education knowledge unit.

A new entity, knowledge package, is defined to support the package-learning mode. This entity comprises a set of knowledge units with the same properties to present a research, education or exhibition topic. The knowledge package consists of two subclasses, the original knowledge group entity and a newly defined entity, knowledge chain. A knowledge group is a set of learning content items, and a knowledge chain denotes a sequence of knowledge units in a learning path. The knowledge chain entity can be applied to design learning packages for the package-learning mode. Specialists in content domains can define the cross-relationships between any pair of the above various elements, whether from within the same domain or different domains, to form a knowledge network.

Each knowledge element is converted to a content profile according to the shared ontology. All knowledge elements can thus be converted to content profiles with a consistent specification and process for the personalization service. Section 4.6 describes the process for creating content profiles.

4.4.2 User context and usage information modeling

Context-awareness is a key issue in the area of mobile and ubiquitous computing. Mobile users often require information depending on their current context, e.g. their location, their environment or available resources. For a mobile information system, several aspects of context can be considered, such as the characteristics of the particular mobile device (storage and screen size) and network (bandwidth and peers), context of the application (requirements in storage, download and display capability), context of the user of the system (e.g., time, location, interests), context of information objects (e.g., location). The handling of the concept

adapt the networking mode (to gain efficiency in the system communication) or to adapt the information display (to gain effectiveness in the user communication) [37].

People interact with a rich and stimulating environment like a museum for intellectual and aesthetic pleasure. This activity is not structured: adapting choices to the contingent situations, visitors move in the physical space guided by their interests or stimulated by the context. In order to avoid breakdowns in the flow of the activity, the boundary between the physical space and the information space should .be seamless

Information on the user context of a learner is vital to provide intelligent and proactive services to support context-aware and personalized mobile learning. Each registered user has a user context, which includes the user profile and learning records. As well as demographic information such as ID, age, education, sex, address and e-mail, a user profile also includes static and dynamic preferences and environment context. The static preference includes interesting domains specified by a user, and the dynamic preference is updated from the historical learning records tracked by the system. The environment context contains a learner’s location, time used and time left, which are identified and measured by the system.

The learning records contain the learning plan and historical learning records, and are denoted by a usage profile with a set of concept definitions aggregated from the content profiles. Usage patterns of individuals, groups and global learners are further discovered from usage profiles and used by the personalization service.

This work models user context and usage information using ontology. This method has two major advantages. First, the recommendation results based on content accessed can be assured to be accurate if the concepts are consistently represented in learners’ usage profiles as knowledge bases. Second, learners can easily inquire about concepts when they do not precisely understand topics or know the titles of all learning content items.

4.5 Ubiquitous personalization service

In this section, we aim to develop a personalization technique to support ubiquitous learning during pre-visit, onsite-visit, and post-visit stages. Several techniques applied in personalization service are the following: content-based filtering, collaborative filtering, rule-based filtering and Web usage mining. More advanced data mining methods and algorithms for use in the Web domain include association rules (a technique for finding frequent patterns, associations and correlations among sets of items), sequential pattern

In this section, we aim to develop a personalization technique to support ubiquitous learning during pre-visit, onsite-visit, and post-visit stages. Several techniques applied in personalization service are the following: content-based filtering, collaborative filtering, rule-based filtering and Web usage mining. More advanced data mining methods and algorithms for use in the Web domain include association rules (a technique for finding frequent patterns, associations and correlations among sets of items), sequential pattern