Integrated knowledge-based content management system

Content Management attempts to follow a given model for effectively creating, editing, managing, and publishing content [68]. Knowledge can be regarded as a kind of content to be managed. Content management can be considered as an infrastructure to amass and distribute knowledge [9].

No single list of the best requirements exists for a content management system. Every organization has its own needs. A classification scheme comprising content creation, content management, and publication and presentation has worked well for museums. The major requirements of content creation include integrated authoring environment, separation of content and presentation, multi-layer content structure, multi-user authoring, content reusing, metadata creation and powerful cross-linking. The key requirements of content management include version control, effective indexing, manage diverse content, integration with coexistent domains, adequate security and pro-active reports. The major requirements of publication and presentation include page templates, exchange support, and effective accessibility.

Considering the unique content management and knowledge management viewpoint of museums, the knowledge-based content management system includes the content creation subsystem, the content management subsystem, and the design of the publishing and presentation subsystem (see Figure 3.4).

Figure 3.4 The architecture knowledge-based content management system (adapted from Rockley’s)

The functionalities of each subsystem are described as follows:

(1) The content creation subsystem

z Editing the global classification hierarchy for various domains z Editing core knowledge content elements with semantic metadata z Organizing advanced knowledge content elements

(2) The content management subsystem

z Managing indexing structures of knowledge content z Managing metadata repositories

z Managing vocabulary repositories

z Managing core and advanced knowledge content repositories

z Managing authoring template databases (3) The publication and presentation subsystem

z Converting knowledge elements into XML-based content structure

z Publishing Web pages from XML-based content and assigned XSL templates z Building the classification hierarchy-based browsing structure

z Creating the metadata-based query interfaces

3.6 Conceptual modeling

Conceptual modeling means identifying relevant concepts of the real world with an abstract model. Conceptual modeling intends to integrate different views of an organization into one global and consistent model where entities and relationships are explicitly defined.

The conceptual modeling of a unified knowledge-based content management system for digital archives in the proposed model is designed to construct both enterprise domain knowledge systems and multi-layer reusable knowledge content structures by adopting a thorough syntax, a semantic tool, and models that concretely express and interpret them.

The ER Model (ERM) can be considered to be the ancestor of all modern modeling methods [14]. Since its original inception ERM has derived many descendents aimed at enhancing the conceptual design of relational databases. Due to the popularity of the object-oriented programming concept, the Object-Oriented Model (OOM) [25] and Extended Entity-Relationship Model (EERM) [71] have been proposed. EERM possesses the features of ERM and OOM, including aggregation abstraction, generalization abstraction, and association abstraction. Aggregation abstraction defines a PART-OF relationship between an entity and its components. Generalization abstraction defines a subset or IS-A relationship between entities, and establishes a hierarchy from a generic entity to its subsets. Association abstraction defines a multi-valued feature of attributes. Due to the many advantages of EERM,

it has been applied in the conceptual modeling of database applications [7, 26]. Huang [42]

also successfully applied EERM to conceptually model the multimedia databases of museum applications. This Study also uses EERM as a conceptual modeling tool in this paper.

3.6.1 Conceptual modeling of enterprise domain knowledge system

The conceptual modeling of a knowledge system (see Figure 3.5) across domains and applications in a museum can be summarized as having the following features.

z A top-down approach is used to construct the global knowledge system across domains.

z The relationships and constraints can be constructed between entities within a domain or across domains.

z The attributes of knowledge content can be specified as metadata annotated by specialists of each domain.

z To support efficient administration and personalized services, profiles of specialists and users must be specified.

After completing the system conceptual design, the knowledge hierarchy and relationships in a domain or between domains can be organized to construct the global knowledge system. The knowledge system can be constructed for application requirements and can also be viewed as the knowledge classification hierarchy system (Guarino, 1995) to represent them. The knowledge classification hierarchy system plays the common view among specialists and users. The knowledge element entities contain a set of core and advanced knowledge elements specified in Section 3.4. The next section reveals their conceptual modeling process.

3.5 The conceptual modeling of enterprise knowledge system

3.6.2 Conceptual modeling of multi-layer reusable knowledge content structures

The multi-layer reusable knowledge content structures shown in Section 3.4 provides a set of formal structures from elementary to complex for specialists to express and organize knowledge content for particular concepts. The knowledge element entity denotes the superclass of all knowledge content, and comprises the core knowledge elements, the advanced knowledge elements and the innovative elements.

The core entity denotes the class of core knowledge elements, each of which is a set of multimedia objects with semantic metadata. The multimedia object entity encompasses the entities of image, audio, video, text and animation. An instance of the advanced entity comprises a set of instances in the core entity. The advanced entity comprises of the

multimedia document subclass, the knowledge unit subclass and the knowledge group subclass. The instance of the latter subclass is organized from a set of instances of the former subclass. Section 3.4 details the usage of the above three subclasses. Any instance in the core entity, advanced entity, or innovative entity may have a set of related link to instances of itself or to others to form a set of knowledge networks. The innovative entity with the same types of instances is inferred from the core and advanced entity. The conceptual modeling of multi-layer reusable knowledge content structures is shown as Figure 3.6.

Figure 3.6 The conceptual modeling of multi-layer reusable knowledge content structures

3.7 A practical implementation

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

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