2009年12月9日 星期三

PATTERNS OF SEMANTIC-BASED

PATTERNS OF SEMANTIC-BASED KNOWLEDGE CLASSIFICATION, ORGANIZATIONAND ACQUISITION PROCESSES FOR PRODUCT DESIGN ENTERPRISES
YINGLIN WANG; JIANMEI GUO;
.Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China, { ylwang, guojianmei}@sjtu.edu.cn
XIJUAN LIU
Mechanical School, Shanghai Dianji University, Shanghai 200240
Former knowledge engineering researches aims at boosting automatic reasoning, however recent knowledge management researches focus on promoting the knowledge sharing and reusing among the peoples. Because of the different aims between the two directions, former knowledge representations schemas, such as rule based representation, frame from knowledge engineering researches are not fit for the current knowledge management scenarios. In this paper, for the purpose of building knowledge management systems for product design enterprises, knowledge items are classified into seven types based on the semantics of their usage. Then their representations are discussed respectively. Based on the above classifications, a knowledge representation meta-model and a basic domain ontology reference model for cooperative knowledge management systems are put forward. The reference model is an abstraction that can be reused and extended in knowledge management systems of different enterprises. Finally, the patterns of knowledge acquisition processes in cooperative knowledge management scenarios of product design processes are studied.
1. Introduction
Knowledge representation and acquisition have been studied for many years, and a lot of methods have been put forward. The most common and famous methods are rules, semantic networks, frames and case based methods (Feigenbaum 1984; Bruce 1984; Randall 1986; Bergmann 2002; Bueno 2005). For instance, semantic networks are used to represent knowledge. Each node represents a concept and arcs are used to define relations between the concepts. Frame has its own name and a set of attributes, or slots which contain values. However, the former methods usually aim at supporting automatic reasoning, rather than helping human beings in their design decisions. The purpose is ambitious, but it is beyond the reach, just because knowledge in its nature is so complex that the process may not be fully automated. E.g., fulfilling product design tasks needs very complex cognitive, creative and synthetic activities, which are far beyond what the automatic reasoning can deal with. Up to now, complex product design tasks still mainly rely on the efforts of human beings. Therefore representations of knowledge should be suitable for helping human beings in their routine works, so that the knowledge discovered can be easily represented, well-organized, easily searched when required.
Hence, in order to capture and reuse knowledge in business processes to improve the product, processes and services, the concept of Knowledge Management (KM) was put forward since the 1991 (Nonaka 1991). KM comprises a range of practices used in an organization to identify, create, represent, distribute and enable adoption of insights and experiences, which are either embodied in individuals or embedded in organizational processes or practice. As a consequence, many large companies and non-profit organizations have resources dedicated to internal KM efforts (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organizations.
Relating to knowledge representation, different frameworks for distinguishing between knowledge were studied. One proposed framework distinguishes knowledge between tacit knowledge and explicit knowledge (Polanyi 1966). Tacit knowledge represents internalized knowledge which is hard to be expressed by artificial language and may not be consciously aware of how he or she accomplishes particular tasks, and this is different from explicit knowledge, which represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others (Alavi & Leidner 2001). Early research suggested that a successful KM effort needs to convert internalized tacit knowledge into explicit knowledge in order to share it, but the same effort must also permit individuals to internalized and make personally meaningful any codified knowledge retrieved from the KM effort. Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995). In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is "re-internalized" into implicit knowledge.
At the meantime, researchers put forward frameworks from other different perspectives. Davenport believed that knowledge may or may not be clearly expressed, taught, used during observation; may be detailed or outlined; may be complex or simple, undocumented and documented (Davenport 1993). Specifically, in the product design field. Ropohl divided design knowledge into 4 types from the angle of systems philosophy: technical know-how, functional rules, structural rules, and socio-technological understanding (Ropohl 1997). Bayazit defined 4 types of design knowledge on the basis of design methodology: procedural knowledge, declarative knowledge, design normative knowledge and collaborative design knowledge (Bayazit 1993). He considered collaborative design knowledge as knowledge about team working.
The existing classification frameworks of knowledge are helpful, but some of them fail to clarify knowledge in a clear and systematic way, and the dependency relationships of different kinds of knowledge are missing. Besides, the semantics of the usages, e.g., the connections between knowledge, specialties and tasks in a domain, have not been emphasized. This makes knowledge hard to be maintained and reused. Hence, effective way of knowledge representation and organization for knowledge management activities should be studied further.
Besides the knowledge classification frameworks, the key issue in knowledge management is knowledge acquisition, which has been studied extensively, and many approaches have been proposed. In general sense, knowledge acquisition contains three types: discovery by personal, discovery through cooperation in an organization, and discovery via data mining. From the cognitive view point, Nonaka proposed a paradigm for managing the dynamic aspects of organizational knowledge creating processes (Nonaka 1994). Its central theme is that organizational knowledge is created through a continuous dialogue between tacit and explicit knowledge. He also identifies four patterns of interaction involving tacit and explicit knowledge: socialization, externalization, internalization, and combination. These patterns form a spiral model for organizational knowledge creation. Related to Nonaka's framework, recent years, the researchers studied the patterns of Collaborative Knowledge Management (CKM) and Personal Knowledge Management (PKM) processes respectively. CKM is based on a software environment where people work on-line; they continuously contribute to the collective knowledge, which is then made available to everyone. The key idea is to motivate participation in a collective knowledge creation process by supporting online environments for collaborative work, and to harvest the value of collaborative work using high precision search, alerting, and related KM technologies. Typical researches of CKM are Ramesh 's and Kathryn's. Ramesh identified problems associated with knowledge management in the context of new product development by cross-functional collaborative teams. A prototype system that captures and manage tacit and explicit process knowledge is also discussed (Ramesh 1999). Kathryn introduced the concept of knowledge management for product innovation and presents a collaborative knowledge management tool specifically designed to help manage a portfolio of product innovation projects in a distributed environment (Kathryn 2003). Lihui present a review of existing research, projects, and applications in the domain of collaborative conceptual design, based on the Internet and Web technologies. The purpose of the review is to understand the needs for conceptual engineering design, to clarify the current conceptual design practice, to classify the available technologies, and to study the future trend in this area (Lihui 2002). Compared with CKM, PKM is focused on personal productivity improvement for knowledge workers in their working environments. While the focus is the individual, the goal of PKM is to enable individuals to operate better both within the formal structure of organizations and in looser work groupings. This is as different from KM as traditionally viewed, which appears to be focused on enabling the corporation to be more effective by "recording" and making available what its workers know. Wright studied how individual workers apply knowledge processes to support their day-to-day work activities, and presented an emergent model that links distinctive types of problem solving activities with specific cognitive, information, social and learning competencies, supported by individual, social and organizational enablers (Wright 2005). Jefferson holds that PKM is aimed at helping the individual to overcome the frustrations associated with information overload and allowing them to improve their personal effectiveness (Theresa 2005). The third approach of knowledge acquisition is acquiring knowledge via data mining or using machine learning techniques, such as decision tree, association rules, and neural network. Rubin, S.H. views knowledge mining as an extension of data mining (Rubin 1997). Qingzhang proposed a framework named Intelligent Knowledge Discovery System (IKDS) which help users to select appropriate data mining algorithms to discover useful knowledge (Qingzhang 2007).
Existing researches of knowledge acquisition present the basic schemes of knowledge acquisition process, but patterns and mechanism for integration between them are still rare. Therefore, how different kinds of knowledge acquisition processes and business processes are integrated, and how it is implemented, need to be reconsidered in the complex context of business processes. Besides, in product design domains, knowledge and data might be very complex, much of which can not be represented using structural description. As a result of this, full-automatic knowledge acquisition is hard to be used. So, in which way the automatic mining tools are used need to be discussed further.
Based on the existing researches and the above assumption, we classify the knowledge items used in product design routines into seven abstract types: concepts, relations, rules, methods, processes, know-where, and instances according to the semantics of the roles and usages of them. Based on this classification and the domain ontology, the knowledge items can be organized in a systematic way. Besides, we propose an integration model of knowledge acquisition processes, which combines personal knowledge acquisition processes, collaborative knowledge acquisition processes, and the data mining processes together, forming a seamless lifecycle knowledge management model. The implementation aspects are also discussed in which we use ontology as the basis for building the system.
2. Knowledge Types in Complex Product Knowledge Management Scenarios
Based on the existing results mentioned in section 1, and the empirical experiences, we define seven knowledge types for complex product knowledge management, the semantics of each type are as follows.
Concepts are the basic units of knowledge which are the basics for understanding and communications between different persons and organizations. According to the semantic theory of concepts, concepts are abstract objects (Eric et al, 2007). Many philosophers consider concepts to be a fundamental ontological category of being. From the viewpoint of artificial intelligence domains, concepts are considered as subsets of objects. From the engineering domain, concepts may be physical notations, such as temperature, speed, power etc. They may be special shapes on a component, such as fillet, chamfer etc. They may be special components or mechanical devices, such as motorcycle, gear-box, etc. They may be abstract notations, such as changes, conflicts etc. For represent design knowledge, concepts should be represented and defined first. Important concepts, such as tasks, products of enterprises should be defined. Concepts are the key elements of a domain ontology.
Relations reflect the possible dependencies and associations between a set of variables or objects. Relations can be represented as formula, relational tables, graphs or figures which may represent the qualitative dependencies between variables. Relations can be distinguished into two types: those which always are true for all the instantiation of related variables, and those are true for only specific values of the variables. The former kind can be called formulas in a general sense, while the latter kind can be called individual relations. E.g., the fact "some employee x has participated in a project y" is an individual relation. But the assertion "any person who has been responsible for a job of kind x is able to do the jobs of same kind" should be considered a formula.
Rules are guidelines of what is and what is not allowed in the design decision process. For example, a design taboo is a kind of rules. Some of them may be deduced from corresponding relations. When choosing some parameters when others has been known in a design problem, the related formula relations may be used.
Methods could be directly used to solve certain problems, for instance, the method to increase the rigidity of materials, a way to reduce the energy consumption of a specific machine, or a technique to split the mesh when using the finite element software.
Processes include a set of dependent activities in order to fulfill special tasks. Processes may allow iterations. For example, diagnosis of design problems follows a specific process. The product design is a collaborative process in which variant of resources are involved.
Know-where is the special knowledge of where to find the knowledge or persons to solve problems at hand. For example, knowledge map can be thought as this kind of knowledge.

Figure 1 The knowledge representation meta-model
Instances are specific results or experiences of solving problems, including instances of processes and the results. Instances are the original resources through which the abstract concepts, relations, rules, methods, processes and know-where knowledge can be discovered and generalized. For example, a piece of design case of an artifact, in which a set of design concepts, values and relations between design parameters, the related design methods, and the persons who designed it are described.
The relationship of the seven types of knowledge is shown in Figure 1. In this figure, concepts are the basis for all other kinds of the knowledge representation; relations will be defined on the top of concepts; rules may be derived through relations; the methods, know-where knowledge, and processes are depicted on top of concepts and relations. Those kinds of knowledge can be abstracted from instances; or in the reverse direction, the instances can be instantiated from the more abstracted types. A unified ontology meta-model is used as the framework to describe the domain ontology.

Figure 2 The basic domain ontology reference model
The above kinds of knowledge may be represented in structural forms or represented in natural language forms. Here the guideline is that only when automatic reasoning will have great benefits to the users, should the knowledge be transformed into structural forms. When there is a structural form of a knowledge item, the corresponding natural language form will still be stored in the knowledge base, which is used for human beings to understand it.
Based on the above general classifications of knowledge, we put forward basic domain ontology model as an initialization for building cooperative knowledge management systems (see Figure 2). The model includes the essential classes and relations, such as the classes of organizations, employees, specialties and products, concept descriptions etc. For instance, a unit in an organization includes a group of employees who cooperate in their work to achieve certain common goals of designing, manufacturing, warehousing, and selling the final product. Based on the basic domain ontology model, more specific concepts and relations can be developed.

3. Knowledge Acquisition Process Integration Model
The above classification and the reference knowledge meta-model provide the basis for the acquisition of knowledge. However, knowledge acquisition in cooperative environments is far more than trivial pursuit. Through the analysis of knowledge discovery, we seamlessly integrated three types of knowledge acquisition patterns, i.e., personal discovery, discovering through cooperation, discovering through data mining, into a unified models of the knowledge acquisition processes. In the first pattern, the employee accepts task assigned to he or her; retrieve related knowledge from the enterprise knowledge base (or the knowledge might be pushed to the employee automatically according the task type, etc.); execute the task accordingly and obtain results; through the results, the background knowledge and the former results he or she already knows, the employee independently finds knowledge, record it down and submit it after further modification and edit using the terminology and format of the ontology. Software modules can be embedded in the personal work platforms for editing and submitting knowledge at anytime. In the second pattern, knowledge is discovered by several persons who cooperatively work together towards a final solution for complex tasks. In this kind of knowledge acquisition pattern, a workflow process might be involved and the responsibility of the participants during the process is clearly defined. In Figure 2, for simplicity, we do not distinguish the above two patterns although there are slight differences between them. For both kinds of patterns, after the submission of knowledge, an intermediate state named quasi-knowledge is given to it. A knowledge item of quasi-knowledge will be published on the bulletin board of the enterprise and wait other persons to recommended if they found it valuable and worthy of recommendation. After the recommendation, an evaluation process will follow to judge the correct and the value of the knowledge. Then, the evaluation work will be assigned to related experts, according to some business rules. One of the possible rule might be "IF the employee x work in the specialty y, and the knowledge z to be evaluated involves the specialty y, then x can be assigned the evaluation task of knowledge z".
The third pattern of data mining techniques is integrated in the model in the following ways. When new results are added into the database of the enterprise during execution of a task or several tasks, rules might be generalized through data mining, but as the rules might be nonsense, so they must be checked by the workers before they can be submitted for further evaluation. The automatic content analysis techniques, e.g., text similarity analysis, can also be used to check whether the new findings are similar as the knowledge that already exists in the enterprise's knowledge base to avoid further duplicate efforts of the evaluation for it; or the contradictions might be find through automatic content analysis, so the old knowledge might need to be revised. The automatic content analysis techniques can also be used in the collaborative evaluation process thereafter in the similar ways (see Figure 3). In the evaluation process, the similar knowledge of the evaluated knowledge in the knowledge base will be found and pushed to the reviewers for references to avoid the duplicate efforts of evaluation and the possible redundancy of knowledge in the knowledge base.

Figure 3 Knowledge Acquisition Process Integration Model
4. Case Study
4.1. The organization of knowledge
Domain ontology (which is built on top of the reference model in Figure 2) plays a central role in the above integration model. Each object in the system must be represented via the terminology of concepts and relations of the domain ontology and the standard language, such that it can be understood by others; the representation via ontology will enable some possible automation of the business and knowledge processes. Besides, the system will be more flexible via using domain ontology schema.
For example, as a concept of mechanical engineering, "shaft bearing" may be defined with a general description of its function, and the parameter's (attributes) description as well as some graphics that shows the meaning of them (see Figure 4).
For instance, part of the definition of the "shaft bearing" can be as follows:
Section 1: Basic Definition
Definition of shaft bearing: A device that supports, guides, and reduces the friction of motion between fixed shaft and moving machine parts.
The super-class: Mechanical Connections;
The figure of shaft bearing: see Figure 4.
Section 2: Attributes of a Shaft Bearing;
Attribute (Parameter) 1: Name: Load of the Shaft, W; The semantics: the total force that the bearing is designed to withstand. See also the definition of Mechanical Load; Value types: numerical; Unit: lbf;
Attribute (Parameter) 2: Name: Diameter of the Shaft, d; The semantics: the allowed diameter of the shaft the bearing can support; Value types: numerical; Unit: inch;
… …
Section 3: Relations between the Parameters of a Shaft Bearing;
1) The general description:
The initial step in the selection and sizing of a bearing involves determination of the operation bearing pressure. Bearing pressure is defined as the load divided by the projected area:
2) The formula:
P = 4.4482 x W / (d x L ) where:
P = Bearing pressure, MPa
W = Load, N
d = Shaft diameter, mm
L = Bearing length, mm
3) Variants of the formula;
This formula gives the average pressure in MPa, that the bearing supports. Elevated temperature reduces load capacity; lower temperature generally increases static load capacity.
Section 4: Rules
Title: Rules for selecting bearing proportions;
Content: Optimum performance can be achieved by specifying a length to inside diameter ratio (L/d) ranging from 0.5 to 2.0. Values of L/d less than 1.0 result in easier escape for wear debris and less sensitivity to shaft deflection and misalignment. There may also be some cost advantage in using a bearing with a small L/d ratio. If the L/d is higher than 2.0, distortions or misalignment may cause stress concentrations and excessive localized heating. So make sure don't let the L/d >2.0.
Section 5: Methods
Title: Method when a long bearing is required.
Content: When a long bearing is required, it is advisable to consider using two bearings with a small gap between them or to increase the inside diameter, d, and re-estimate the bearing geometry.
Section 6: Instances
Based on the above definition of "shaft bearing" in the domain ontology, the instances of the concept can be specified thereafter. The graph is helpful to explain the meaning of the relationships of parameters. There may be many formats that can represent the instances of a class (concept). One way is using tables . Each row in the table means a kind of valid combination of these parameters. In product design domain, many of the knowledge take this kind of form.
In this approach, the elements defined in section 1 and section 2 are the basis for all the other parts. For example, when a new bearing instance is available and need to be input to the knowledge base, then the definition in section 1 and section 2 should be used by the system to generate a user interface for the user to input the values of the parameters of the new instance.
4.2. Integrated Knowledge Acquisition and Evolution Processes
Now let's discuss a case in which former recognized knowledge need to be amended. If a designer uses the knowledge of shaft bearing of the knowledge base in a new scenario, and find that the rule or method is invalid or insufficient for this new situation, then the person may need to amend the rule or method of the knowledge base. E.g., if he or she uses a shaft bearing whose L/d ratio < 2.0, but it results in a failure afterwards, then the former rule should be modified to reflect the failure. The contradiction with the former rule may be found by the automatic analysis tools, or some data mining tools may be used, to aid the person to generalize the rules.
Then the person may put forward a new version of the rule, edit and then submit it as a quasi knowledge to be evaluated. For choosing the right experts to evaluate the new findings, the specialty of the knowledge might be indicated and used. E.g., assuming that a piece of knowledge “K_1” mainly relates to a specialty “S_1”, the specialty “S_1” is associated with some employee instances through the “has-Specialty” relation of the class “Employee” in the domain ontology. If these related employees are available, they can be arranged to review the new knowledge; otherwise, the employees from neighboring specialties are selected. Such neighboring specialties can be found in terms of the specialty hierarchy or the similarity of them. If the new knowledge involves different specialties, the employees from the different specialties will be selected.
The knowledge-specialty-employee relationship, used in the above knowledge acquisition process, is defined in ontology meta-model and is instantiated in object-instance layer. Thus, there are three layers: ontology meta-model, domain ontology, and object instances. The rules of employee assignment are business rules, which can be represented on top of the concepts and relations in domain ontology. Similarly, the specialty hierarchy, being a part of domain ontology, can also be used to routine business processes. Take task assignment for example, some task can be assigned to some employees or departments according to the relationship between task types and specialty types.

Acknowledgements
The paper was supported by the National High-tech Research and Development Project of China (863) under the grant No. 2009AA04Z106 and the National Science Foundation of China (NSFC) under the grant No. 60773088.
5. Conclusion
According to the practical requirements of building collaborative knowledge management systems in product design enterprises, we classify the knowledge items into seven types based on the semantics of their usage. In this model, all the elements of knowledge can be specified based on an ontology meta-model. The concept and relation are the basis of others. The rules, methods and processes can be defined using the terminologies of the concept and relation layers. As the dependencies between the elements of knowledge are clearly described, hence knowledge can be systematically maintained. We also propose an integration model that seamlessly integrates three types of knowledge acquisition processes and business processes based on the domain ontology. The pattern of the knowledge representations and acquisition we summarized in this paper are general abstractions from real applications, thus it can be used as a reference model to build the future collaborative knowledge management systems in enterprises, especially for complex product development domains.
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