Intelligent Information Processing IV

1. Front Matter

   Pages i-xi

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2. Keynote Presentations

   1. Semantic Computing

      • Phillip C-Y. Sheu

      Pages 1-2

   2. Towards Brain-inspired Web Intelligence

      • Ning Zhong

      Pages 3-4

   3. Data Mining Technologies Inspired from Visual Principle

      • Zongben Xu

      Pages 5-6

3. Semantic Web Services

   1. A Context Model for Service Composition Based on Dynamic Description Logic

      • Wenjia Niu, Zhongzhi Shi, Liang Chang

      Pages 7-16

   2. Evaluation of Ontologies and DL Reasoners

      • Muhammad Fahad, Muhammad Abdul Qadir, Syed Adnan Hussain Shah

      Pages 17-27

   3. ER2OWL: Generating OWL Ontology from ER Diagram

      • Muhammad Fahad

      Pages 28-37

4. Knowledge Acquisition and Management

   1. Voice knowledge acquisition system for the management of cultural heritage

      • Stefan du Château, Danielle Boulanger, Eunika Mercier-Laurent

      Pages 38-49

   2. Granularity of Knowledge from Different Sources

      • Maria A. Mach, Mieczyslaw L. Owoc

      Pages 50-57

   3. Fuzzy Ontology Models Based on Fuzzy Linguistic Variable for Knowledge Management and Information Retrieval

      • Jun Zhai, Yiduo Liang, Jiatao Jiang, Yi Yu

      Pages 58-67

5. Data Mining

   1. Blog Classification: Adding Linguistic Knowledge to Improve the K-NN Algorithm

      • Ines Bayoudh, Nicolas Bechet, Mathieu Roche

      Pages 68-77

   2. A Modified Clustering Method with Fuzzy Ants

      • Jianbin Chen, Deying Fang, Yun Xue

      Pages 78-85

   3. An New Algorithm for Modeling Regression Curve

      • JiSheng Hao, LeRong Ma, WenDong Wang

      Pages 86-91

6. Web Search

   1. Enhancing Web Search with Heterogeneous Semantic Knowledge

      • Rui Huang, Zhongzhi Shi

      Pages 92-101

   2. Exploring Words with Semantic Correlations from Chinese Wikipedia

      • Yun Li, Kaiyan Huang, Seiji Tsuchiya, Fuji Ren, Yixin Zhong

      Pages 103-108

   3. A Heuristic Knowledge Reduction Algorithm Based on Partition Subdivision and Consistent Degree

      • Wen Huo, Xiaoguang Hong

      Pages 109-117

7. Cognition-based Intelligent Information Processing

   1. Object-based Image Retrieval with Attention Analysis and Spatial Re-ranking

      • Ke Gao, Shouxun Lin, Yongdong Zhang, Sheng Tang

      Pages 118-128

   2. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

      • Erkam Güreşen, Gülgün Kayakutlu

      Pages 129-137

   3. A Robot Emotion Generation Mechanism Based on PAD Emotion Space

      • Gao Qingji, Wang Kai, Liu Haijuan

      Pages 138-147

   4. Study of Personalized Network Tutoring System Based on Emotional-cognitive Interaction

      • Manfei Qi, Ding Ma, Wansen Wang

      Pages 148-154

Knowledge existing in modern information systems usually comes from many sources and is mapped in many ways. There is a real need for representing “knowledge pieces” as rather universal objects that should fit to multi-purpose a- ing systems. According to great number of information system’s tasks, knowledge representation is more or less detailed (e.g. some level of its granularity is - sumed). The main goal of this paper is to present chosen aspects of expressing granularity of knowledge implemented in intelligent systems. One of the main r- sons of granularity phenomena is diversification of knowledge sources, therefore the next section is devoted to this issue. 2. Heterogeneous Knowledge as a Source for Intelligent Systems Knowledge, the main element of so-called intelligent applications and systems, is very often heterogeneous. This heterogeneity concerns the origin of knowledge, its sources as well as its final forms of presentation. In this section the selected c- teria of knowledge differentiation will be presented, in the context of potential sources of knowledge acquisition. In Fig. 1 an environment of intelligent systems is shown, divided into different knowledge sources for the system. Fig. 1. Potential knowledge sources for intelligent information/reasoning system. Source: own elaboration based on (Mach, 2007) p. 24.

• Beijing
• Data Mining
• IFIP TC12
• IIP 2008
• K-NN Algorithm
• Virtual Organization
• artificial intelligence
• cognition
• image processing
• image segmentation
• knowledge
• linguistics
• modeling
• neural network
• semantic web

• Institute of Computing Technology, Chinese Academy of Sciences, China

  Zhongzhi Shi

• MODEME, IAE Research Center, Lyon University, France

  E. Mercier-Laurent

• Indiana University, USA

  D. Leake

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