ABSTRACT. Designing knowledge base capable of knowledge extraction, creation, storage, management and sharing is a challenging task for the knowledge ...
International Journal of Information Technology and Engineering Vol. 2, No. 1, January-June 2011, pp. 9-12 © International Science Press,
ISSN: 2229-7367
Designing Knowledge Base Towards PDMS Zeeshan Ahmed University of Wuerzburg, Germany, Vienna University of Technology, Austria.
ABSTRACT Designing knowledge base capable of knowledge extraction, creation, storage, management and sharing is a challenging task for the knowledge engineers and managers. Initially this discusses the concept, contributions and problems to the field of Product Data Management (PDM), then targeting some of existing PDM problems; a semantic based approach is discussed as solution. The main idea behind this Knowledge Base is to develop a Meta data based system to extract and use knowledge out of data to capture, manage, improve and deliver knowledge by providing information collection, retrieval, analysis, management and organization in PDM Systems. Keywords: PDM, Knowledge Base, Semantic Web.
1. INTRODUCTION Product Data Management (PDM) is a digital way of organizational data management to maintain and improve the quality of products and followed processes. PDM products maintain the organizational information including the information about personal involved in managerial and technical operations, running projects and manufacturing products [1]. Where PDM products are heavily benefiting industry there it is also facing some serious unresolved issues .i.e., successful secure platform independent PDM system implementation, PDM system deployment and reinstallation, static and unfriendly machine interface, unintelligent search and scalable standardized framework. Targeting these PDM problems many approaches and solutions are proposed including Meta-phase, SherpaWorks, Enovia, CMS, Windchill, and Smarteam, but still there is no such promising approach exists which can claim of providing all solutions [2]. Targeting two of above mentioned PDM problems i.e., unfriendly graphical machine interface and static search I have proposed an approach i.e. Intelligent Semantic Oriented Agent based Search (I-SOAS) [3]. I-SOAS is an agent, information engineering & modelling, data warehousing and knowledge base approach, proposed to provide solution in implementing a semantic based intelligent PDM System capable of handling user’s structured and unstructured requests by processing, modelling and managing the into database [9]. To meet aforementioned goals this research, the proposed conceptual architecture of I-SOAS is mainly divided into four sequential iterative components .i.e., Intelligent User Interface, Information Processing, Data Management and Data Representation (See Figure 1).
Figure 1: I-SOAS Conceptual Architecture [3]
Intelligent User Interface is proposed to design intelligent human machine interface for system user communication [5]. Information Processing is proposed to process and model user’s unstructured and structured inputted request by reading, lexing, parsing, and semantic modelling [7, 11]. Data Management is proposed to manage user requests and system performance based information [6] and Data Representation is proposed to represent system output in user’s understandable format [4]. The overall concept of I-SOAS is to first provide a flexible graphical machine interface capable of taking input from user in natural language, then tokenize, parse and analyze natural language based input to semantically model the input, then using semantically modelled information dynamically generate a semantic based SQL query which will run in to the connected or used relational data base management system to extract needed information and then convert extracted information to natural language and represent to the user.
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To implement I-SOAS I have designed a four tier implementation architecture consisting of four main modules .i.e., Graphical Interface, Repository, Knowledge Base, Processing & Modelling and three communication layers .i.e., Process Presentation Layer, Process Database Layer and Process Knowledge Layer. Graphical Interface is to implement the intelligent human machine interface, Repository is to load and manage the organizational technical and managerial data, Knowledge Base is proposed to capture, manage, improve and deliver knowledge, and Processing & Modelling is to read, organized, tokenize, parse, semantically evaluate and generate a semantic based queries to extract desired results from Repository and Knowledge base. Three communication layers .i.e., Process Presentation Layer, Process Knowledge Layer and Process Database layers are to transfer data between above mentioned four major modules of implementation architecture (See Figure 2).
Figure 2: I-SOAS Implementation Architecture [5]
requirements of the third component of I-SOAS’s conceptual iterative architecture Data Management (see Figure 1). Knowledge Base is supposed to contain qualitative information depending upon efficient information retrieval system and content classification structure. Moreover Knowledge Base is designed in way so then it can intelligently provide solutions to unsolved problems and behave like an expert system. The Knowledge Base is designed keeping three major requirements in mind .i.e., Knowledge Extraction and Creation, Knowledge Store and Management and Knowledge Sharing and Usage. 2.1 Knowledge Base Categorization The required Knowledge Base component must be capable of extracting information from repository and indentifying knowledge out of it, then store and manage identified knowledge and then in the end provide knowledge in a way then it can be easily shared and used. To meet these aforementioned goals of knowledge base design I have followed the standardized categorization of Knowledge Base [6]. Following standardized categorization of Knowledge base, as shown in Figure 3, that I-SOAS’s Knowledge Base design is basically categorized into two categories .i.e., Machine readable knowledge bases and Human readable knowledge bases. Machine readable knowledge bases are designed to store knowledge in machine read and understandable format like logical set of instructions forming rules to describe knowledge. Whereas Human readable knowledge bases are designed to extract knowledge from relevant information providing sources like white pages, manuals, organizational documents etc, (see Figure 3).
In this research paper, I am not going in to the details of every component of I-SOAS but Knowledge Base. Initially in this research paper I briefly present the main idea to develop Knowledge Base for I-SOAS, then designed and followed Knowledge Base Life Cycle, designed Knowledge Base System Sequence for I-SOAS and then present internal work flow of designed Knowledge Base for I-SOAS in Section 2 of this research paper. Moreover I also briefly present the information about involved tools and technologies in the development of Knowledge Base in Section 3, concluding the discussion in Section 4 will also present some future recommendation in Section 5 of this research paper. 2. I-SOAS KNOWLEDGE BASE The main idea behind the I-SOAS Knowledge Base is to develop a Meta data based system to extract and use knowledge out of data. Knowledge Base is designed to capture, manage, improve and deliver knowledge by providing information collection, retrieval, analysis, management and organization. The designed Knowledge Base system for I-SOAS is conceptually based on the
Figure 3: Knowledge Base Categories
2.2 Knowledge Base Life Cycle To obtain desired results from I-SOAS knowledge base the whole functionality is basically divided into three cyclic steps .i.e., Knowledge Engineering, Knowledge Management and Knowledge Transfer (see Figure 4).
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Designing Knowledge Base Towards PDMS
component of system sequence design Manage Knowledge to manage knowledge using Tacit/Explicit knowledge management methodologies (See Figure 5). 2.4 Knowledge Base Internal Work Flow
Figure 4: Knowledge Base Life Cycle
Following the above discussed system requirements and designed designs; I have designed the internal work flow of I-SOAS Knowledge Based. I-SOAS Knowledge Base internal workflow is supposed to start with the knowledge gathering by either capturing the knowledge or by creating the knowledge (Knowledge Engineering). Resultant captured or created knowledge is first supposed to be stored, organized and then categorized. Then resultant knowledge is further transferred to use by sharing and distributing it, (See Figure 6).
In the first step knowledge engineering will be performed with application of some knowledge engineering mythologies like Common KADS etc to and extract and gather required knowledge based information. Then the very next cyclic task is to manage obtained knowledge from the first step using Tacit/Explicit knowledge management [8] and then at last transfer knowledge to share and use. 2.3 Knowledge Base System Sequence To meet afore mentioned goals and targets the internal system sequence of I-SOAS knowledge base in designed. The I-SOAS Knowledge Base system sequence design is consists of four main components .i.e., Process Knowledge Layer, Data Input, Capture or Create Knowledge and Manage Knowledge. These four components are supposed to perform certain jobs; the job of Process Knowledge Layer is to bring data from Processing & Modelling component (See Figure 2) and forward to Data Input component. Data Input will first analyze the input to verify the input structure and if the format of input is according to designed standard then will forward input data to Capture/Create Knowledge component.
Figure 6: Knowledge Base Internal Work Flow
3. KNOWLEDGE BASE INVOLVED TECHNOLOGIES At the moment to implement the I-SOAS Knowledge base I am considering following technologies .i.e., Java and Ontology based technologies XML, RDF and OWL [11] (See Figure 7).
Figure 5: Knowledge Base System Sequence Design
As the name shows the task of Capture/Create Knowledge is to Capture or Create Knowledge from Data Input using knowledge engineering methodologies. Then engineered knowledge will be forwarded to the last
Figure 7: Knowledge Base Involved Technologies
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International Journal of Information Technology and Engineering [2]
Huang, M.Y., Lin, Y.J., Xu, H., “A Framework for WebBased Product Data Management using J2EE”, The International Journal of Advanced Manufacturing Technology, Pages 847-852, 24, Issue: 11, December 2004.
[3]
A. Zeeshan., “Proposing Semantic Oriented Agent and Knowledge base Product Data Management”, Information Management and Computer Security Journal, 17, Issue: 5, pp. 360-371, 2009.
[4]
A. Zeeshan, D. Gerhard:, “Semantic Oriented Agent based Approach towards Engineering Data Management, Web Information Retrieval and User System Communication Problems”, In the proceedings of 3rd International Conference for Internet Technology and Secured Transactions, Dublin Institute of Technology, Dublin Ireland, pp. 23-28, June 2008.
5. FUTURE RECOMMENDATIONS
[5]
As this short research paper is about an ongoing in process research project, right now I am developing Knowledge Base as a real time software application by implementing above discussed proposed and designed designs. The prototype version of I-SOAS consisting of Intelligent Graphical User Interface and Processing and Modelling is developed [5, 6]. Now I am focusing the development activities of I-SOAS Knowledge Base. In future I am pretty hopeful of presenting I-SOAS Knowledge Base with the presentation of implemented version of I-SOAS.
A. Zeeshan., “Designing Flexible GUI to Increase the Acceptance Rate of Product Data Management Systems in Industry”, International Journal of Computer Science & Emerging Technologies, 2, Issue 1, pp. 100-109, ISSN: 20446004, February 2011.
[6]
A. Zeeshan., “PDM Based I-SOAS Data Warehouse Design”, In the Proceedings of FIFTH International Conference on Statistical Sciences: Mathematics, Paper ID 125, ISBN 978-969-8858-04-9, 17, 23-25 January 2009.
[7]
A. Zeeshan., “Proposing LT Based Search in PDM Systems for Better Information Retrieval”, International Journal of Computer Science & Emerging Technologies, 1, Issue 4, pp. 86-100, December 2010.
[8]
Kane H, Ragsdell G and Oppenheim C., ”Knowledge Management Methodologies”, In The Electronic Journal of Knowledge Management, 4 Issue 2, pp. 141-152, 2006
[9]
A. Zeeshan Ahmed:, “Importance of I-SOAS In PDM Community”, International Journal of Web Applications, 3 , Issue: 1, pp. 12-16, March 2011.
4. CONCLUSION In this short research paper I have briefly described Product Data Management and its some major existing challenges. Targeting PDM challenges I presented our proposed solution by describing its conceptual and implementation architectures (see Section 2). Furthermore focusing on the scope of this research paper and going in to the details of our research, I have briefly presented ISOAS Knowledge Base concept and designed implementation designs. The main motivation of writing this short research paper is to present the presence of I-SOAS and the methodologies followed by I-SOAS to resolve the problems of knowledge engineering and management in PDM systems.
ACKNOWLEDGEMENT I am thankful to University of Wuerzburg Germany and Vienna University of Technology Austria for giving me the opportunity to keep working on this research project. I am thankful to Prof. Dr. Detlef Gerhard for his supervision during this research and pay my gratitude to Prof. Dr. Thomas Dandekar for his generous support. I also thanks to my beloved wife and colleague Mrs. Saman Majeed (Doctoral Researcher) for her support during this research, development and technical documentation. REFERENCES [1]
A. Zeeshan, D. Gerhard., “Contributions of PDM Systems in Organizational Technical Data Management”, In the Proceedings of The First IEEE International Conference on Computer, Control & Communication, 12-13 November, Karachi Pakistan 2007.
[10] A. Zeeshan, M. Saman, D. Thomas., “Towards Design and Implementation of a Language Technology based Information Processor for PDM Systems”, International Science & Technology Transactions of Information TechnologyTheory and Applications, 1, Issue 1, pp. 1-7, July 2010. [11] A. Zeeshan, T. Ina, “Integration of Natural Language Processing towards Semantic Oriented Search”, In International Journal of Computer Science and Software Technology, 3, Issue 2, pp. 69-76, July-December 2010.
