Ontological Cognitive Map

Chauvin, Lionel; Genest, David; Loiseau, Sylvain

doi:10.1142/s0218213009000366

Cited by 9 publications

(1 citation statement)

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“…In order not avoid miss understated relations, we have built the transitive closure of all causal maps. That is: the set of relations for each causal map is enriched with other relations that can be obtained by transitivity, as described in [Chauvin et al 2008]: for example, if we know that import reduces market price and that market price reduces shellfish stock, we can assert that import rises shellfish stock. Then, we have looked for logical contradiction, that is relations between two similar concepts with the same direction but with an opposite sign.…”

Section: Comparison Between Causal Mapsmentioning

confidence: 99%

Inference reasoning on fishers' knowledge using Bayesian causal maps

Beaufort

Sedki

Fontenelle

2015

Ecological Informatics

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Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individuals judgments, drawing inferences in cognitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity, we derive a decision facilitating tool, enabling scenarios testing for fisheries management.qualitative modelling, cognitive maps, bayesian networks, fisher's knowledge, fisheries management, qualitative decision support 1

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Section: Comparison Between Causal Mapsmentioning

confidence: 99%