This chapter provides a brief summary of related research: knowledge management and knowledge support, knowledge flow, knowledge-based planning, ontology, process and process-view.
2.1 Knowledge management and knowledge support
Knowledge is one of the key assets to ensure sustained competitive advantage in the highly technological and global environment of modern organizations [21, 32, 50, 59]. To achieve success in this environment, workers need to effectively apply knowledge to conduct knowledge-intensive operations and management activities [9, 38, 70].
Knowledge management (KM) supplies the principles of creation, organization, transfer and application of the knowledge within organizations [26] and is recognized as a crucial practice for enabling organizations to survive in a knowledge economy era [64].
One purpose of KM is to support workers in fulfilling their knowledge-needs, by bridging the gap between workers’ knowledge and the requirements of tasks [2, 58, 63]. Studies have shown that precise and timely knowledge support is an important mechanism for increasing both productivity and work effectiveness [28, 38].
In a task-based business environment, tasks are conducted in work processes. The effective provision of task-relevant knowledge and context information is crucial to increasing workers’ productivity. To meet this provision, integration solutions of information retrieval (IR) and workflow management systems (WfMS) have been developed. These solutions proactively deliver task-relevant knowledge according to the context of tasks [1, 43]. For example, the KnowMore system derives task profiles from process definitions that facilitate knowledge provision [1]. The Flow-Wiki system was developed by a wiki-based approach for agilely managing workflows and effectively providing relevant information to participators [24]. In this way, process participants can obtain knowledge that pertains to task profiles and/or the execution context of the current process.
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Liu et al. [62-63] proposed a task-based K-support system that provides knowledge to adaptively meet a worker’s dynamic information needs by analyzing his/her access behavior and relevance feedback on documents. Furthermore, because of the nature of teamwork, a collaborative mechanism is essential for establishing knowledge management systems [4, 67].
2.2 Knowledge flow
Knowledge flow research focuses on how knowledge flows transmit, share and accumulate knowledge in a team. In a workflow situation, working knowledge may flow among workers, while process knowledge may flow among various tasks [70, 72-73]. Thus, the knowledge flow reflects the level of knowledge cooperation between workers or processes, and influences the effectiveness of teamwork or workflow.
To fulfill workers’ knowledge-needs, knowledge flows provide links among knowledge sources. Through knowledge flows, workers can effectively obtain knowledge from these sources to execute tasks [25]. Knowledge flows illustrate the sequence of knowledge-needs and/or the order of referring documents when workers perform tasks.
Knowledge flows can facilitate knowledge sharing and reuse in both business and research environments. For example, Zhuge [70] illustrated a knowledge flow within a software development team of a distributed organization. Here, the knowledge flow carried and gathered knowledge from one team member to another for sequential knowledge sharing.
Similar knowledge sharing can take place in a citation chain where knowledge is transferred among scientific researches. In this context, the citation chain of papers is a knowledge flow that disseminates knowledge among scientists and inspires new ideas [71].
Several knowledge flow models have been built in recent researches. Luo et al. [40]
modeled a Textual Knowledge Flow (TKF) from a semantic link network. The purpose of the TKF was to recommend proper browsing paths to users after evaluating their interests and inputs. Lai and Liu [28] constructed a time-ordering knowledge flow model to illustrate the sequence of workers’ knowledge referencing behaviors. In this model,
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workers obtained proper knowledge to fulfill their knowledge-needs through the knowledge flows discovered in document access logs. Kim et al. [25] proposed a knowledge flow model using a process-oriented approach to capture, store and transfer knowledge. Zhang et al. [66] used Petri-Net to model a knowledge flow. In this model, a knowledge node was used to generate, learn, process, understand, synthesize and deliver knowledge based on four types of flow relations: creation, merging, replication and broadcasting. Zhao and Dai [68] integrated business processes and knowledge flows and divided knowledge flows into sequence, distribution, combination and self-reflection patterns based on RAD (role-activity-diagram) model. Finally, Anjewierden et al. [5]
suggested that the referencing sequence in weblogs may be regarded as a knowledge flow and can be described as a sender-message-receiver model.
2.3 Knowledge-based planning
Both knowledge flow and knowledge-based planning prompt similar ideas about embedding knowledge while building models. Knowledge-based planning is a planning methodology used to identify a sequence of tasks executed by one or more agents under given initial conditions and resource constrains to achieve final goals [6]. The methodology involves knowledge acquisition, knowledge validation and knowledge maintenance of planning domains, and adopts appropriate knowledge-based planning tools to build planning models [6]. For example, R-Moreno et al. [46] successfully utilized a planning and scheduling system as well as a workflow modeling tool to plan a telephone installation workflow model. The workflow modeling tool was used to acquire relevant knowledge, such as initial conditions, resource constrains and final goals; then the planning and scheduling system was used to convert the knowledge into planning standard expressions. A knowledge-based planning system can also be employed to manage the result of planned tasks for the purpose of fulfilling other tasks’ preconditions. Chow et al.
[12], for example, proposed a strategic knowledge-based planning system (SKPS) that combined knowledge rules with mathematical models to formulate co-loading shipment plans. Through SKPS, shipment planners could acquire, validate and maintain knowledge
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of the shipment domain, and thus build a co-loading shipment planning model so that executors could utilize the knowledge in the model to perform tasks efficiently.
As the above examples demonstrate, knowledge-based planning focuses on building planning models for problem solving or task execution. Knowledge flow research contributes to the building of knowledge flow models for corresponding task execution plans (or workflow processes) that support knowledge provision, sharing and transferring [28, 70]. Knowledge flows can be either derived by mining workers’ access logs [28] or specified by KF designers according to their experience in executing the corresponding workflow process [69, 72]. Besides these two methods of deriving knowledge flows, knowledge-based planning tools can complement knowledge flow research by helping designers build the appropriate knowledge flows that correspond to task execution plans.
2.4 Ontology
Ontology is a widely accepted approach for capturing and representing knowledge possessed by an organization [44, 54]. It is a conceptualization mechanism that defines knowledge concepts in a specific domain and constructs a hierarchical structure to describe their inter-relationships [18]. Ontology can promote a common understanding throughout a whole organization to facilitate knowledge storage, retrieval and synthesis [45]. For example, the common terminologies and knowledge concepts in ontology can improve the problem-solving capability and efficiency within a supply chain [7]. Another example of ontology pertains to the knowledge concepts derived from Wikipedia articles and categories, which can be used to predict the contents of documents [55].Weng and Chang [60] proposed a research document recommendation system which exploited ontology to construct user profiles, and utilized the profiles to illustrate researchers’ interests. Afacan and Demirkan [3] developed an ontology-based universal design support system to support designers in the conceptual design phase; it adopts ontologies to process and represent required knowledge. As the above examples illustrate, ontology is a versatile paradigm that can be applied in many domains.
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Building ontology is an evolving process and involves many techniques and tools to facilitate the whole process. Obviously, the construction process would include an evaluation and feedback mechanism to gradually improve ontology quality and obtain common understanding in organizations [29, 45, 56]. For example, Uschold and King [57]
proposed a skeletal methodology to build an enterprise ontology; it comprises four phases:
scoping, building, evaluating and documenting. Du et al. [15] designed a six-phase process that includes the preparation, transformation, clustering, recognition, refinement and revision for extracting ontology from unstructured HTML pages. Therefore, involving users in the evaluation or refinement phase is essential for gradually adjusting the quality of ontology. Many ontology-building tools, such as Protégé, OntoEdit and SNet-Builder, can effectively support the ontology construction process to serve predefined purposes and meet users’ requirements [10, 44].
2.5 Process and process-view
Recently, business process modeling has been rapidly applied to streamline business administration and to facilitate cooperation among enterprises. Business process modeling refers to the design, analysis and execution of business processes [20]. Its goals are to describe a set of activities that can be performed in sequence, and to allocate resources and arrange jobs optimally by analyzing the organizational and technical environments [61].
By employing appropriate modeling tools, business process modeling can provide pre-defined templates that allow enterprises to enact their business processes in an effective and efficient manner.
In an industrial environment, processes describe the flows of business operations.
Workflow management systems are definition and execution tools that support these operations [45]. In practice, participants involved in a workflow need a flexible workflow model capable of providing appropriate process information [2, 36]. Because of the increasing complexity of business processes and the variety of participants, it is beneficial for organizations to define virtual processes with different views of the workflow [8, 17, 36, 52]. Liu and Shen [36] presented a novel concept of process abstraction: the process-view.
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A process-view is an abstracted process derived from a base process to provide generalized process information. The process-view is generated by an order-preserving approach, which ensures that the original order of the activities in the base process is preserved.
Under the process-view concept, a WfMS can provide various views of a process for different participants within an organization or cross organizations [37]. Shen and Liu [53]
proposed a role-based approach to discover role-relevant process views for different workflow participants. The role-based approach generates process view automatically, based on the relevance degrees between roles and tasks. This work adopts similar ideas to generate virtual knowledge flows from a base knowledge flow, while retaining the knowledge referencing order.
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