Knowledge representation and inference techniques to improve the management of gas and oil facilities

Abstract

This paper describes an experimental work carried out in the framework of an important European project to create and make use of a wide-ranging knowledge base in the gas/oil domain. In the context of this work, “knowledge base” means a collection of formal statement relating, with a negligible loss of information, the inner content (the ‘meaning’) of “complex events” included in two different “storyboards”. These events – originally presented under the form of unstructured natural language information – concern some general activities proper to the management of gas/oil facilities, like recognizing and monitoring gas leakage alarms in a gas processing plant or triggering the different steps needed to activate a gas turbine. To express this sort of information and to set up the knowledge base, the NKRL (Narrative Knowledge Representation Language) formalism has been used. NKRL is a conceptual meta-model and Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich ‘narrative’ data sources. The final knowledge base has been firstly tested in depth using the standard NKRL querying and information retrieval tools. High-level inference procedures have then been used, both “transformation rules” – unsuccessful queries are ‘transformed’ to produce results that are ‘semantically similar’ to those searched for initially – and “hypothesis rules” – information in the knowledge base is automatically aggregated to supply a sort of ‘causal’ explanation of some retrieved events.