• Proceedings of the CALSEC Conference
• /
• 2005.03a
• /
• pp.55-61
• /
• 2005

We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

• Journal of Distribution Science
• /
• v.12 no.5
• /
• pp.93-107
• /
• 2014

Purpose - The pool of entrepreneurs with progressive qualities such as creativity and innovation was considered concurrently with such factors as work and capital that stimulate economic development and growth. This study aims to present a model to support the development of a strategic approach for achieving an overall academic entrepreneurship system in Iran. Research design, data, and methodology - The research design of this study is based on applied research because of its objectives, using principles and techniques formulated for basic research to solve operational and real organizational issues. This design also drives the method used, describing and interpreting the findings. Secondary data (library research) was used for this study's data collection. Because of this research's essential characteristics, no hypothesis is launched, and no research setting, questionnaire design, population or population sampling, validity or reliability tests, or statistical analysis are needed. Results and Conclusions - The model is created using a strategic approach acting in an octal setting comprising social, cultural, legal, economic, political, technological, competitive, and natural environments to present a conceptual framework for future studies.

• Journal of Information Processing Systems
• /
• v.9 no.1
• /
• pp.1-30
• /
• 2013

The capitalization of know-how, knowledge management, and the control of the constantly growing information mass has become the new strategic challenge for organizations that aim to capture the entire wealth of knowledge (tacit and explicit). Thus, knowledge mapping is a means of (cognitive) navigation to access the resources of the strategic heritage knowledge of an organization. In this paper, we present a new mapping approach based on the Boolean modeling of critical domain knowledge and on the use of different data sources via the data mining technique in order to improve the process of acquiring knowledge explicitly. To evaluate our approach, we have initiated a process of mapping that is guided by machine learning that is artificially operated in the following two stages: data mining and automatic mapping. Data mining is be initially run from an induction of Boolean case studies (explicit). The mapping rules are then used to automatically improve the Boolean model of the mapping of critical knowledge.

• The Journal of Information Systems
• /
• v.3
• /
• pp.71-82
• /
• 1994

Knowledge acquisition, maintenance of knowledge base, and validation and verification of knowledge are the addressed bottlenecks of building successful knowledge based systems. Along with the increment of interesting in the knowledge based systems, the organization needs to develop a new one although it has a similar one. This causes several serious problems including knowledge redundancy and maintenance of knowledge base. This paper present three models of the reusable knowledge base which might be the solution to the above problem. Three models are : 1) multiple knowledge bases for a single AI application, 2) multiple knowledge bases for multiple AI applications, 3) a single knowledge base for multiple AI applications. A new approach to build such a reusable knowledge base in a homogeneous environment is presented. Our model combines the essential object-oriented techniques with rules in a consistent manner. Important aspects of applying object-oriented techniques to AI are discussed (inheritance, encapsulation, message passing), and some potential problems in building an AI application (decomposition technique of knowledge, search time, and heterogeneous environment) are pointed out. The models of a reusable knowledge base provide several amenities : 1) reduce the knowledge redundancy, 2) reduce the effort of maintenance of the knowledge base, 3) reuse the resource of the multiple domain knowledge bases, 4) reduce the development time.

• Asia pacific journal of information systems
• /
• v.9 no.3
• /
• pp.177-194
• /
• 1999

Identifying knowledge as the single most important asset ultimately defining organizational competitiveness, enterprises are trying to move towards knowledge-oriented practices. Such practices have given rise to learning and agile organization, This paper presents applied information technologies to realize the learning and agile organization, focusing on systems thinking. Firstly, in order to establish a framework for the systems thinking, an information systems development method based on process chain is proposed. Then, an agent-based microworld simulation approach is presented. The approaches provide visible and analytical information to knowledge workers so that they can have systems thinking capabilities eventually. Various microworlds on the top of the information system can be constructed with agents and simulated for possible business events. All decision makings are dynamic in nature. To let knowledge workers look ahead the possible outcomes of the whole relevant processes is the core capability of the approaches. Through watching, the knowledge workers would be able to acquire new insights or problem solving knowledge for the problem in hand.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.328-331
• /
• 1995

A successful, computer-aided design support system can help a process designer focus on making effective design decisions, not merely tedious routine calculations. Such a system is essential to enhance quality of design in terms of economics, environmental benignity, reliability, robustness, and operability. Such a statement is even more accepted when applied to conceptual design problems, where gross design specifications are given while a combinatorial number of design alternatives exists. This paper presents an agent-based approach as a systematic and efficient way to design a design support system for the synthesis of conceptual chemical processes. An agent-based approach allows us to handle design knowledge as an object and thus greatly improve the modularity and reusability of that knowledge. Such modularity and reusability lead to the increased productivity in the development of a design support system and the increased ease in the relaxation of design decisions and the generation of design alternatives, both of which functions are critically important in dealing with the complexity and uncertainty of conceptual design problems.

• Knowledge Management Research
• /
• v.21 no.1
• /
• pp.99-116
• /
• 2020

Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.1-17
• /
• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.

• Journal of Intelligence and Information Systems
• /
• v.9 no.2
• /
• pp.19-38
• /
• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of Information Processing Systems
• /
• v.12 no.4
• /
• pp.688-710
• /
• 2016

Our approach permits to capitalize the expert's knowledge as business rules by using an agent-based platform. The objective of our approach is to allow experts to manage the daily evolutions of business domains without having to use a technician, and to allow them to be implied, and to participate in the development of the application to accomplish the daily tasks of their work. Therefore, the manipulation of an expert's knowledge generates the need for information security and other associated technologies. The notion of cryptography has emerged as a basic concept in business rules modeling. The purpose of this paper is to present a cryptographic algorithm based approach to integrate the security aspect in business rules modeling. We propose integrating an agent-based approach in the framework. This solution utilizes a security agent with domain ontology. This agent applies an encryption/decryption algorithm to allow for the confidentiality, authenticity, and integrity of the most important rules. To increase the security of these rules, we used hybrid cryptography in order to take advantage of symmetric and asymmetric algorithms. We performed some experiments to find the best encryption algorithm, which provides improvement in terms of response time, space memory, and security.