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Information Systems for Knowledge Management


Information Systems for Knowledge Management


1. Aufl.

von: Inès Saad, Camille Rosenthal-Sabroux, Faiez Gargouri

139,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 06.03.2014
ISBN/EAN: 9781118920619
Sprache: englisch
Anzahl Seiten: 336

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Beschreibungen

More and more organizations are becoming aware of the importance of tacit and explicit knowledge owned by their members which corresponds to their experience and accumulated knowledge about the firm activities. However, considering the large amount of knowledge created and used in the organization, especially with the evolution of information and communications technologies, the firm must first determine the specific knowledge on which it is necessary to focus. Creating activities to enhance identification, preservation, and use of this knowledge is a powerful mean to improve the level of economical performance of the organization. Thus, companies invest on knowledge management programs, in order to develop a knowledge sharing and collaboration culture, to amplify individual and organizational learning, to make easier accessing and transferring knowledge, and to insure knowledge preservation. Several researches can be considered to develop knowledge management programs supported by information and knowledge systems, according to their context, their culture and the stakeholders' viewpoints.
<p><b>Chapter 1. Assessing the Community Maturity from a Knowledge Management Perspective 1<br /> </b><i>Imed BOUGHZALA</i></p> <p>1.1. Introduction 2</p> <p>1.2. Background 4</p> <p>1.2.1. Maturity models 4</p> <p>1.2.2. Knowledge-oriented maturity models 5</p> <p>1.3. Method 9</p> <p>1.4. The CoMM 10</p> <p>1.4.1. The development 10</p> <p>1.4.2. The description 13</p> <p>1.5. Application within a CKO professional association 18</p> <p>1.5.1. Overview of need 18</p> <p>1.5.2. Field application steps 19</p> <p>1.5.3. Findings 20</p> <p>1.5.4. Reflection on the field application of CoMM 23</p> <p>1.6. Discussion and implications 24</p> <p>1.7. Conclusion 25</p> <p>1.8. Bibliography 26</p> <p>1.9. Appendix 31</p> <p><b>Chapter 2. Social Networks: Leveraging User Social Data to Empower Collective Intelligence 33</b><br /> <i>Xuan Truong VU, Marie-Hélène ABEL and Pierre MORIZET-MAHOUDEAUX</i></p> <p>2.1. Introduction 34</p> <p>2.2. Collective intelligence by user-centered social network aggregation 35</p> <p>2.3. Related works 37</p> <p>2.4. Proposed system 40</p> <p>2.4.1. User-centered social network aggregation 41</p> <p>2.4.2. Personalized information filtering 45</p> <p>2.4.3. Collaborative knowledge management 48</p> <p>2.5. Decision support 50</p> <p>2.6. Use scenario 53</p> <p>2.7. Prototype 54</p> <p>2.8. Conclusions and future work 57</p> <p>2.9. Acknowledgment 58</p> <p>2.10. Bibliography 58</p> <p><b>Chapter 3. Sociocultural Knowledge Management toward the Adaptation of a CSCL Environment 61</b><br /> <i>Fadoua OUAMANI, Narjès Bellamine Ben SAOUD and Henda Hajjami Ben GHEZALA</i></p> <p>3.1. Introduction 61</p> <p>3.2. The concept of culture and sociocultural factors 63</p> <p>3.2.1. Culture in ethnology 64</p> <p>3.2.2. Culture in psychology 65</p> <p>3.2.3. Cultural properties 66</p> <p>3.2.4. Models of national culture67</p> <p>3.2.5. Discussion 70</p> <p>3.3. The relation between sociocultural human characteristics, KM and CSCL 71</p> <p>3.3.1. CSCL and knowledge sharing 71</p> <p>3.3.2. Culture, human mind and KM 73</p> <p>3.3.3. Discussion 74</p> <p>3.4. Sociocultural considerations in collaborative environments 75</p> <p>3.4.1. Study of existing culturally sensitive tools 75</p> <p>3.4.2. Limitations and findings 76</p> <p>3.5. The proposed ontology-based sociocultural user profile 78</p> <p>3.6. The conceptual ontology framework based adaptation approach 82</p> <p>3.7. The sociocultural aware KM system for CSCL83</p> <p>3.8. Conclusion and ongoing work 86</p> <p>3.9. Bibliography 87</p> <p><b>Chapter 4. An Argumentation-based Rough Set Theory for Knowledge Management  93</b><br /> <i>Sarra BOUZAYANE, Imène BRIGUI-CHTIOUI, Inès SAAD</i></p> <p>4.1. Introduction 93</p> <p>4.2. Background 95</p> <p>4.2.1. Dominance-based rough set approach (DRSA) 95</p> <p>4.2.2. Argumentation 97</p> <p>4.2.3. Multiagent system 104</p> <p>4.3. Related work 106</p> <p>4.4. Multiagent argumentative approach 116</p> <p>4.4.1. Interaction protocol 116</p> <p>4.4.2. Arguments 117</p> <p>4.4.3. Argument and counter-argument evaluation 120</p> <p>4.4.4. Counter-argument construction 121</p> <p>4.5. Example 123</p> <p>4.6. Conclusion 126</p> <p>4.7. Bibliography 126</p> <p><b>Chapter 5. Considering Tacit Knowledge When Bridging Knowledge Management and Information Systems for Collaborative Decision-Making 131</b><br /> <i>Pierre-Emmanuel ARDUIN, Camille ROSENTHAL-SABROUX, and Michel GRUNDSTEIN.</i></p> <p>5.1. Introduction 132</p> <p>5.2. Background theory 133</p> <p>5.2.1. A vision of knowledge within the organization 133</p> <p>5.2.2. Ethnographic workplace study: participation as a means to observe 134</p> <p>5.2.3. Incommensurability: when communication breaks down 136</p> <p>5.3. Proposition 138</p> <p>5.3.1. Fieldwork through participant observation 139</p> <p>5.3.2. Highlighting evidences and levels with ISO/IEC 15504 141</p> <p>5.3.3. Rating the attributes and assessing tacit knowledge consideration 146</p> <p>5.4. Case study 149</p> <p>5.4.1. Describing the field 149</p> <p>5.4.2. Discussing the collected data and the results 151</p> <p>5.5. Conclusions 154</p> <p>5.6. Acknowledgments 155</p> <p>5.7. Bibliography 156</p> <p><b>Chapter 6. Relevant Information Management in Microblogs 159</b><br /> <i>Soumaya CHERICHI and Rim FAIZ</i></p> <p>6.1. Introduction 160</p> <p>6.2. Twitter IR 161</p> <p>6.3. Features for tweet ranking 163</p> <p>6.3.1. Feature set 164</p> <p>6.3.2. Metric measure of the impact of criteria to improve search results 168</p> <p>6.4. Experimental evaluation 172</p> <p>6.4.1. Description of the collection 172</p> <p>6.4.2. Results 173</p> <p>6.5. Conclusion 176</p> <p>6.6. Bibliography 177</p> <p><b>Chapter 7. A Legal Knowledge Management System Based on Core Ontology 183</b><br /> <i>Karima DHOUIB and Faïez GARGOURI</i></p> <p>7.1. Introduction 183</p> <p>7.2. Legal KM 185</p> <p>7.2.1. Legal portals 186</p> <p>7.2.2. Legal decision support systems and legal expert systems 187</p> <p>7.2.3. Legal case-based reasoning 187</p> <p>7.2.4. Legal ontology 188</p> <p>7.3. Functional architecture of the system 188</p> <p>7.4. Legal ontology construction approach 189</p> <p>7.4.1. Existing ontology construction methodologies 190</p> <p>7.4.2. Our approach 193</p> <p>7.4.3. Our reference ontological framework 196</p> <p>7.4.4. Our building blocks 198</p> <p>7.4.5. Discussion 201</p> <p>7.5. Jurisprudence decision structuring methodology (JDSM) 202</p> <p>7.5.1. Thematic document structuring: some related works 203</p> <p>7.5.2. Our methodology 204</p> <p>7.6. Conclusion 209</p> <p>7.7. Bibliography 210</p> <p><b>Chapter 8. Foundations for a Core Ontology of an Organization's Processes 215</b><br /> <i>Mohamed TURKI, Gilles KASSEL, Inès SAAD and Faïez GARGOURI</i></p> <p>8.1. Introduction 216</p> <p>8.2. Our reference ontological framework 218</p> <p>8.2.1. DOLCE 220</p> <p>8.2.2. Actions, participation roles and participatory capacities 222</p> <p>8.2.3. Artifacts 224</p> <p>8.3. A core ontology of an organization’s processes 224</p> <p>8.3.1. Collective phenomena 226</p> <p>8.3.2. Organizational phenomena 231</p> <p>8.3.3. Process of organization 234</p> <p>8.4. Discussion 240</p> <p>8.5. Conclusion 243</p> <p>8.6. Bibliography 244</p> <p><b>Chapter 9. A Business Process Evaluation Methodology for Knowledge Management Based on Multicriteria Decision-Making Approach 249</b><br /> <i>Mohamed TURKI, Inès SAAD, Faïez GARGOURI and Gilles KASSEL</i></p> <p>9.1. Introduction 249</p> <p>9.2. Related works 252</p> <p>9.3. Dominance-based rough set approach 254</p> <p>9.4. BP evaluation methodology 256</p> <p>9.4.1. Phase 1: preference model construction 257</p> <p>9.4.2. Phase 2: exploitation of the preference model 262</p> <p>9.5. The decision support system for identifying sensitive processes OP-DSS 264</p> <p>9.5.1. Graphical interface 264</p> <p>9.5.2. Model base 265</p> <p>9.5.3. Database 265</p> <p>9.5.4. Knowledge base 267</p> <p>9.5.5. Implementation 268</p> <p>9.6. Case study 271</p> <p>9.7. Conclusion and futures works 272</p> <p>9.8. Bibliography 273</p> <p>9.9. Appendix 1. The set of criteria 275</p> <p>9.10. Appendix 2. Contribution degree computing algorithm 277</p> <p><b>Chapter 10. A Collaborative Approach for Optimizing Continuity between Knowledge Codification with Knowledge Engineering Methods and Knowledge Transfer 279</b><br /> <i>Thierno TOUNKARA</i></p> <p>10.1. Introduction 279</p> <p>10.2. Factors influencing knowledge transfer 280</p> <p>10.2.1. Characteristics of knowledge 281</p> <p>10.2.2. Knowledge transfer channels 283</p> <p>10.2.3. Absorptive capacity of knowledge receivers 284</p> <p>10.2.4. Cultural and organizational contexts 285</p> <p>10.3. Modes of knowledge transfer 286</p> <p>10.3.1. Social exchange versus codification 286</p> <p>10.3.2. Knowledge transfer models 286</p> <p>10.4. Research methodology 290</p> <p>10.4.1. Literature review 290</p> <p>10.4.2. Focus groups for data collection and generation of testable propositions 290</p> <p>10.5. Codifying with knowledge engineering methods: barriers for knowledge transfer 293</p> <p>10.5.1. Multiplicity of formalisms 294</p> <p>10.5.2. Heterogeneity of readers profiles 295</p> <p>10.5.3. Background 295</p> <p>10.5.4. Contexts of use 295</p> <p>10.5.5. Preferences for logical structuring and understanding profile 295</p> <p>10.5.6. Level of description of complex knowledge 296</p> <p>10.5.7. Level of description of specific knowledge 296</p> <p>10.5.8. Exchange channels to increase diffusion/transfer 297</p> <p>10.6. Methodology for knowledge transfer efficiency 298</p> <p>10.6.1. Capturing and codifying tacit knowledge domain 298</p> <p>10.6.2. Defining and formalizing exchanges between groups of actors involved in the knowledge transfer process 298</p> <p>10.7. Hydro Quebec case study 302</p> <p>10.7.1. Approach 303</p> <p>10.7.2. Results and implications 304</p> <p>10.8. Discussion 305</p> <p>10.8.1. About completeness of knowledge 305</p> <p>10.8.2. Exploring ontologies for knowledge transfer 305</p> <p>10.8.3. About costs 306</p> <p>10.9. Conclusion 306</p> <p>10.10. Bibliography 307</p> <p>List of Authors 311</p> <p>Index 313</p>
<p><b>Inès Saad</b> is Associate Professor in Information Systems at France Business School and head of the "Management Education and Digital Technologies" research center. She also heads a Knowledge Management team at the MIS laboratory at the University of Picardie Jules Verne, France. Her research interests include knowledge management, organizational memory, information systems, ontology, group decision-making and decision support systems.</p> <p><b>Camille Rosenthal-Sabroux</b> is Full Professor at Dauphine University, Paris IX in France, as well as an advisor for some large companies on Information Systems, Knowledge Management, and Decision Aid. She was the founder of the SIGECAD Group. Her main research topics are: modeling languages (UML), decision aid, knowledge acquisition, knowledge management, and information systems.</p> <p><b>Faïez Gargouri</b> is Professor in Computer Science at the Higher Institute of Computer Science and Multimedia at Sfax University, Tunisia, where he has been director of the MIRACL (Multimedia, InfoRmation systems and Advanced Computing Laboratory) research laboratory since October 2011. His research interests include business information systems, business intelligence, multimedia information systems, and ontology.</p>

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