DYNAMO-MAS: a multi-agent system for ontology evolution from text

International audienceManual ontology development and evolution are complex and time-consuming tasks, even when textual documents are used as knowledge sources in addition to human expertise or existing ontologies. Processing natural language in text produces huge amounts of linguistic data that need to be filtered out and structured. To support both of these tasks, we have developed DYNAMO-MAS, an interactive tool based on an adaptive multi-agent system (adaptive MAS or AMAS) that builds and evolves ontologies from text. DYNA-MO-MAS is a partner system to build ontologies; the ontologist interacts with the system to validate or modify its outputs. This paper presents the architecture of DYNAMO-MAS, its operating principles and its evaluation on three case studies

How Reservoir Characterization Can Help to Improve Production Forecasts

Geological models using statistical concepts bring a new horizon for reservoir engineering studies. This article discusses some of the questions raised by the introduction of these geological models, and a methodology is proposed to account for the heterogeneities in the reservoir production, based on a specific software. A link between the detailed reservoir images generated by the probabilistic geological models, and the well-known flow simulators is established through the selection of these images, and the averaging of the petrophysical data. After a description of the main steps of the integrated software, applications of the methodology to major events of a reservoir development, i. e. appraisal phase, and change of development scheme, are presented. Advantages of the stochastic approach are underlined