Using Fuzzy Cognitive Maps for Knowledge Management in a Conflict Environment

Perusich, Karl; McNeese, Michael D.

doi:10.1109/tsmcc.2005.855509

Cited by 35 publications

(9 citation statements)

References 20 publications

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“…E-FCM, a fuzzy cognitive map [13], [22], is composed by element concepts and theme concept with their state values, as well as the relations between concepts with weights. Meanwhile, E-FCM is able to represent Web resources with rich semantics and be understood by machine easily.…”

Section: B Resources Representation and Storage Mechanismmentioning

confidence: 99%

Building Association Link Network for Semantic Link on Web Resources

Luo

et al. 2011

IEEE Trans. Automat. Sci. Eng.

217

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Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World Wide Web to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery, and publishing, etc. Since existing methods for building semantic link on Web resources cannot effectively and automatically organize loose Web resources, effective Web intelligence activities are still challenging. In this paper, a discovery algorithm of associated resources is first proposed to build original ALN for organizing loose Web resources. Second, three schemas for constructing kernel ALN and connection-rich ALN (C-ALN) are developed gradually to optimize the organizing of Web resources. After that, properties of different types of ALN are discussed, which show that C-ALN has good performances to support Web intelligence activities. Moreover, an evaluation method is presented to verify the correctness of C-ALN for semantic link on documents. Finally, an application using C-ALN to organize Web services is presented, which shows that C-ALN is an effective and efficient tool for building semantic link on the resources of Web services. Note to Practitioners-Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World WideWeb to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery and publishing, etc. Since existing methods for building semantic link on Web resources cannot effectively and automatically organize loose Web resources, effective Web intelligence activities are still challenging. A discovery algorithm of associated resources is first proposed by us to build original ALN for organizing loose Web resources. The properties of different types of ALN are discussed, which show that C-ALN has good performances to support Web intelligence activities. An application using C-ALN to organize Web services is presented, which shows that C-ALN is an effective and efficient tool for building semantic link on the resources of Web services.Index Terms-Association link network (ALN), intelligent browsing, interactive computing, knowledge discovery, semantic Web.

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Section: B Resources Representation and Storage Mechanismmentioning

confidence: 99%

Building Association Link Network for Semantic Link on Web Resources

Luo

et al. 2011

IEEE Trans. Automat. Sci. Eng.

217

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“…These efforts have considered situations in which agents interact with machines [38], how to assist agents who are overloaded with information [38], how agents make decisions when under different levels of anxiety [39], and how to incorporate methods from the field of information fusion into group-level decision making [40]. While our decision models are greatly simplified from these efforts, we believe that our focus on micro and macro-level properties of the social network and the flow of information within it allow us to develop new ideas about how conflicting knowledge spreads.…”

Section: Related Workmentioning

confidence: 99%

Simulating Diffusion with Conflicting Knowledge

Joseph

Carley

2012

SSRN Journal

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assistance on the ideas presented in this work.Keywords: Agent-based modeling, Diffusion Processes, Decision-making. AbstractA simulation model is developed and used to better understand the diffusion of conflicting knowledge in a social network. Within the simulation, correct and incorrect knowledge diffuse simultaneously, however, simulation agents are not explicitly made aware that contradictory information exists. Instead, they may become aware of this by obtaining both correct and incorrect knowledge of the same piece of information. The agent must then determine what knowledge to accept. We implement various models of how people react to conflicting knowledge, which we refer to as intrapersonal conflict resolution strategies (ICRS). We analyze these ICRS with respect to the speed and breadth of correct and incorrect knowledge spread and how often agents find themselves holding conflicting knowledge. We also consider the effect of varying the odds of incorrect knowledge existing in the system, the network structure and the differentials in trust between agents. We find evidence that our model is consistent with many real-world notions of diffusion. Consequently, we make tentative conclusions about the effect of our various experimental conditions on the spread of conflicting knowledge in a social network. We note that conflicting knowledge spreads more slowly and persists longer than knowledge which cannot be conflicted.

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“…In keeping our model of how an agent makes a decision and retains information simplistic, we readily note that our work is much more theoretical in nature than other simulations of agent decision-making in uncertain environments. These efforts have considered situations in which agents interact with machines [38], how to assist agents who are overloaded with information [38], how agents make decisions when under different levels of anxiety [39], and how to incorporate methods from the field of information fusion into group-level decision making [40]. While our decision models are greatly simplified from these efforts, we believe that our focus on micro and macro-level properties of the social network and the flow of information within it allow us to develop new ideas about how conflicting knowledge spreads.…”

Section: Related Workmentioning

confidence: 99%

Simulating Diffusion with Conflicting Knowledge

Joseph

Carley

2012

View full text Add to dashboard Cite

assistance on the ideas presented in this work. AbstractA simulation model is developed and used to better understand the diffusion of conflicting knowledge in a social network. Within the simulation, correct and incorrect knowledge diffuse simultaneously, however, simulation agents are not explicitly made aware that contradictory information exists. Instead, they may become aware of this by obtaining both correct and incorrect knowledge of the same piece of information. The agent must then determine what knowledge to accept. We implement various models of how people react to conflicting knowledge, which we refer to as intrapersonal conflict resolution strategies (ICRS). We analyze these ICRS with respect to the speed and breadth of correct and incorrect knowledge spread and how often agents find themselves holding conflicting knowledge. We also consider the effect of varying the odds of incorrect knowledge existing in the system, the network structure and the differentials in trust between agents. We find evidence that our model is consistent with many real-world notions of diffusion. Consequently, we make tentative conclusions about the effect of our various experimental conditions on the spread of conflicting knowledge in a social network. We note that conflicting knowledge spreads more slowly and persists longer than knowledge which cannot be conflicted.

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Using Fuzzy Cognitive Maps for Knowledge Management in a Conflict Environment

Cited by 35 publications

References 20 publications

Building Association Link Network for Semantic Link on Web Resources

Building Association Link Network for Semantic Link on Web Resources

Simulating Diffusion with Conflicting Knowledge

Simulating Diffusion with Conflicting Knowledge

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