Research on pervasive knowledge service model
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Abstract:
Knowledge service is the integration of knowledge and services. Introducing the concept of "people-oriented" in pervasive computing to knowledge service, this paper constructs a kind of pervasive knowledge service model by means of multidisciplinary research achievements such as intelligence computing, system science, ecology, system science, knowledge science and service science, especially generalized computing and generalized learning proposed by us. At the same time, the key technologies of pervasive knowledge service are discussed.Keywords:
Context-aware pervasive systems
A framework for knowledge based knowledge acquisition is presented. The ACKnowledge project addresses the problem of supporting the knowledge engineer in the phases of KBS development involving knowledge acquisition. The goal of ACKnowledge is to provide an integrated and principled system or knowledge engineering workbench (KEW). Within this project the author has been working on the problem of providing a coherent framework for the integration of knowledge acquisition (KA) methods and techniques. He has taken the view that KEW will act as a KBS for KA; this view has a number of consequences for the development of the next generation of knowledge acquisition tools.
Knowledge Acquisition
Workbench
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Based on the research of knowledge modeling and the practice of engineering design, the knowledge expression system for knowledge-based cooperative design system was put forward, which includes the engineering language knowledge, the engineering data table knowledge, the engineering instance knowledge and the engineering graphic knowledge. According to the requirement of the collaborative development of enterprise group, using the resources of Chengdu-Deyang-Mianyang networked manufacturing and ASP platform, a knowledge-based cooperative design system has been developed and integrated with ASP platform. A reasoning system based on knowledge expression system has been developed and applied in the cooperative design system. The collaboration among enterprises and knowledge-based design have been implemented. The system has been put into practice in many enterprises.
Table (database)
Design knowledge
Collaborative engineering
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Product design which is one of the most important stages for manufacturing is a complex activity involving a great amount of knowledge. In this paper, knowledge is divided into three types: formula knowledge, diagram knowledge, table knowledge. Based on these requirements, knowledge base is constructed. This is used to store knowledge and to support the decision-making product design. First, the knowledge representation is formally described. Then, the knowledge navigation and search strategy is detailed. A specific section is dedicated to demonstrating the knowledge application techniques according to the different knowledge types that are proposed. The proposed methods are illustrated with examples. Finally, we conclude with the presentation of application performed with the knowledge application techniques.
Design knowledge
Table (database)
Decision table
Descriptive knowledge
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Tacit Knowledge
Knowledge modeling
Mathematical knowledge management
Explicit knowledge
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The robust knowledge base needs a reasonable design pattern to represent knowledge model. On the foundation of the research about relationship of knowledge model with knowledge ontology, the knowledge model defined by three categories of domain knowledge, reasoning knowledge and task knowledge is set up. Furthermore, the matching relationship mechanism of knowledge model with knowledge base is put forward. At last, according to design pattern of knowledge model, the example of knowledge base for project risk management based on ontology is implemented.
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Knowledge Acquisition
Subject-matter expert
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Aiming at deficiencies of existing knowledge resources management systems, we designed a new collaborative knowledge construction system for massive knowledge resources. By collaborative knowledge building in the following three phases: acquisition of knowledge factors, generation of the local extended topic maps and integration of global extended topic map, we realized a conjunction of concept level and knowledge element level in different knowledge granularity, as well as an integration of topic map in distributed nodes. Through those methods we achieved to establish the global extended topic map of a specific domain from knowledge resources.
Granularity
Knowledge Acquisition
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In order to promote intuition and accuracy of researcher's knowledge representation, this paper is focused on the visualization of knowledge structure based on knowledge network. According to the system science theory, the composition of researcher's knowledge structure is analyzed. A model of researcher's knowledge network which represents researcher's knowledge structure is proposed. This model includes knowledge points, knowledge stocks and relationship between knowledge points. After modeling process of knowledge network which includes modeling of knowledge points, calculating of knowledge stocks, modeling of relationship and hierarchical processing of knowledge network, researcher's knowledge fields and knowledge sub fields can be discovered. A researcher's knowledge management prototype system is developed based on the above method. The method makes researcher's knowledge structure visualized intuitively, objectively and quantitatively, which is validated by a case in the end.
Mathematical knowledge management
Intuition
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In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.
Knowledge Acquisition
Subject-matter expert
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