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Relational databases (RDBs) have been widely used as back end for information systems. Considering that RDBs have valuable knowledge interwoven in between stored data, how to access, represent and share this knowledge becomes an i...
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Relational databases (RDBs) have been widely used as back end for information systems. Considering that RDBs have valuable knowledge interwoven in between stored data, how to access, represent and share this knowledge becomes an important challenge. Topic maps (TMs) emerge as a good solution for this problem. However, manual development of TMs is a difficult, time-consuming and subjective task if there is no common guideline. The existing TMs building approaches mainly consider the meta-information contained in a RDB, without considering the knowledge residing in the database content (its current state). Other approaches require a predefined configuration for applying a specific data transformation. This paper proposes an automatic method for TM construction based on learning rules. Our method considers the background knowledge of the RDBs during the building process and was implemented and applied on a representative set of 15 RDBs. The resulting TMs were validated syntactically using a standard tool and validated semantically through the inference of information using a formal query language. In addition, an analysis between the relational data (input) and its representation (output) was conducted. The results found in our experiments are encouraging and put in evidence the soundness of the proposed method.
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Intelligent Topic Map (II M) embodies the multilevel, multi-granularity and the inherent relevant characteristics of knowledge. With ITM as infrastructure, this paper presents a visual knowledge structure reasoning method integrat...
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Intelligent Topic Map (II M) embodies the multilevel, multi-granularity and the inherent relevant characteristics of knowledge. With ITM as infrastructure, this paper presents a visual knowledge structure reasoning method integrates the logic-based knowledge reasoning and the structure-based knowledge reasoning. The logic-based knowledge reasoning implements knowledge consistency checking and the implicit associations reasoning between knowledge points, it can help us obtain the optimal description of knowledge. In order to construct the complete knowledge structure, a Knowledge Unit Circle Search strategy for structure-based knowledge reasoning is proposed, by which more detailed semantic association of knowledge is provided and the inherent relevant characteristics of knowledge is obtained. The knowledge reasoning results are visualized by ITM, which provides a visual knowledge map. It is available for users to acquire the knowledge and associations among them. A prototype system has been implemented and applied to the massive knowledge organization, management and service for education.
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This paper presents a visual knowledge structure reasoning method using Intelligent Topic Map which extends the conventional Topic Map in structure and enhances its reasoning functions. Visual knowledge structure reasoning method ...
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This paper presents a visual knowledge structure reasoning method using Intelligent Topic Map which extends the conventional Topic Map in structure and enhances its reasoning functions. Visual knowledge structure reasoning method integrates two types of knowledge reasoning: the knowledge logical relation reasoning and the knowledge structure reasoning. The knowledge logical relation reasoning implements knowledge consistency checking and the implicit associations reasoning between knowledge points. We propose a Knowledge Unit Circle Search strategy for the knowledge structure reasoning. It implements the semantic implication extension, the semantic relevant extension and the semantic class belonging confirmation. Moreover, the knowledge structure reasoning results are visualized using ITM Toolkit. A prototype system of visual knowledge structure reasoning has been implemented and applied to the massive knowledge organization, management and service for education.
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We propose a novel concept of Intelligent Topic Map, which extends the conventional topic map in structure and enhances the reasoning functions. With the Intelligent Topic Map as infrastructure, a mechanism of distributed knowledg...
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We propose a novel concept of Intelligent Topic Map, which extends the conventional topic map in structure and enhances the reasoning functions. With the Intelligent Topic Map as infrastructure, a mechanism of distributed knowledge integration is designed. The structure is divided into three layers: local Intelligent Topic Map layer, similarity measure layer and global Intelligent Topic Map layer. It provides a uniform query interface to a multitude of knowledge sources and lays the foundation for high-quality knowledge services. Moreover, we propose a new similarity measure algorithm based on comprehensive information theory and merging rules for knowledge integration. The experimental results show that our method is feasible and it has the significance of reference and value of further study for the distributed knowledge integration.
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Knowledge management is a critical issue for the next-generation web application, because the next-generation web is becoming a semantic web, a knowledge-intensive network. XML Topic Map (XTM), a new standard, is appearing in this...
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Knowledge management is a critical issue for the next-generation web application, because the next-generation web is becoming a semantic web, a knowledge-intensive network. XML Topic Map (XTM), a new standard, is appearing in this field as one of the structures for the semantic web. It organizes information in a way that can be optimized for navigation. In this paper, a new set of hyper-graph operations on XTM (HyO-XTM) is proposed to manage the distributed knowledge resources. HyO-XTM is based on the XTM hyper-graph model. It is well applied upon XTM to simplify the workload of knowledge management. The application of the XTM hyper-graph operations is demonstrated by the knowledge management system of a consulting firm. HyO-XTM shows the potential to lead the knowledge management to the next-generation web.
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Aiming to help users to effectively acquire and visualize target knowledge from massive, distributed and heterogeneous information sources, we designed a visual knowledge recommendation service system based on intelligent topic ma...
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Aiming to help users to effectively acquire and visualize target knowledge from massive, distributed and heterogeneous information sources, we designed a visual knowledge recommendation service system based on intelligent topic map. It includes knowledge organization, knowledge recommendation and visualization display. Knowledge logical organization model based on intelligent topic map extends the conventional topic map in structure and enhances its reasoning functions. Hot resources mining distinguish the different importance degree of knowledge nodes (topics or knowledge elements), interest trends predict the interested knowledge of users in the future and knowledge navigations provide a personalized navigation path for users. Moreover, recommendation results visualization based on intelligent Topic Map provides users with an intuitive, graphic and pellucid visual interface. Finally, a demonstration is given to elaborate the knowledge recommendation providing process.
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Modern enterprise knowledge management systems typically require distributed approaches and the integration of numerous heterogeneous sources of information. A powerful foundation for these tasks can be Topic Maps, which not only ...
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Modern enterprise knowledge management systems typically require distributed approaches and the integration of numerous heterogeneous sources of information. A powerful foundation for these tasks can be Topic Maps, which not only provide a semantic net-like knowledge representation means and the possibility to use ontologies for modelling knowledge structures, but also offer concepts to link these knowledge structures with unstructured data stored in files, external documents etc. In this paper, we present the architecture and prototypical implementation of a Topic Map application infrastructure, the 'Topic Grid', which enables transparent, node-spanning access to different Topic Maps distributed in a network.
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Purpose - The purpose of this paper is to provide an overview of the current use of topic maps in the library field, how they might be integrated into the ILS structure and some of the inherent challenges in trying to transform MA...
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Purpose - The purpose of this paper is to provide an overview of the current use of topic maps in the library field, how they might be integrated into the ILS structure and some of the inherent challenges in trying to transform MARC data. Design/methodology/approach - A review of available literature was conducted as well as e-mail interviews with researchers and vendors in the field. An introduction to some of the basic concepts quickly leads into a recap of some of the possibilities that have been tried with this technology in the library field. Specific examples of the use of the XML standard XTM are given as well as some theoretical possibilities discussed. Finally some thought is given to where this technology will fit into the ILS. Findings - The paper finds that more work needs to be done by vendors and libraries in structuring data to allow for easier transformation. Research limitations/implications - This study was a limited overview. The lack of training materials and software make topics maps have an unnecessarily high barrier to entry. Practical implications - This paper points a way for further research and a need for basic tools and training geared towards the library community. Originality/value - This paper attempts to address some of the potential and challenges associated with using topic maps in a library environment, especially as part of an ILS.
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In this paper, we carry out a study about the main themes treated by the International Journal of Information Technology & Decision Making during its first 10 years (2002-2011). The themes are detected, quantified and visualized using an approach that combines performance analysis and science mapping. Bibliometric maps based on co-word analysis will help us to visualize the division of the journal into several subfields and their relationships, providing interesting insight into the main topics being discussed in the journal in these years. In addition, the study will show the most productive themes (according to published papers) and the most impacting ones (according to received citations)....
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In this paper, we carry out a study about the main themes treated by the International Journal of Information Technology & Decision Making during its first 10 years (2002-2011). The themes are detected, quantified and visualized using an approach that combines performance analysis and science mapping. Bibliometric maps based on co-word analysis will help us to visualize the division of the journal into several subfields and their relationships, providing interesting insight into the main topics being discussed in the journal in these years. In addition, the study will show the most productive themes (according to published papers) and the most impacting ones (according to received citations).
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摘要 :
In this paper, we carry out a study about the main themes treated by the International Journal of Information Technology & Decision Making during its first 10 years (2002–2011). The themes are detected, quantified and visualized using an approach that combines performance analysis and science mapping. Bibliometric maps based on co-word analysis will help us to visualize the division of the journal into several subfields and their relationships, providing interesting insight into the main topics being discussed in the journal in these years. In addition, the study will show the most productive themes (according to published papers) and the most impacting ones (according to received citations)....
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In this paper, we carry out a study about the main themes treated by the International Journal of Information Technology & Decision Making during its first 10 years (2002–2011). The themes are detected, quantified and visualized using an approach that combines performance analysis and science mapping. Bibliometric maps based on co-word analysis will help us to visualize the division of the journal into several subfields and their relationships, providing interesting insight into the main topics being discussed in the journal in these years. In addition, the study will show the most productive themes (according to published papers) and the most impacting ones (according to received citations).
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