Intelligent services: A semantic recommender system for knowledge representation in industry

Intense competition in industrial area pushes companies to increase pace and efficiency of development. During process plant design projects, large amount of information causes a challenge in stakeholders' collaboration in decision making and it breeds lower development speed. An intelligent service is required to improve knowledge and information accessibility by personalizing the knowledge and information based on the stakeholder's situation in their working life which is known as a recommender system. This paper describes the early phases of a PhD project that explores the idea of applying a semantic recommender system in process plant design. To achieve this goal we aim to employ various recommendation approaches, data analysis and ontology engineering. The resource of data is provided by an industrial partner, Aker Solutions. The results of discussion show that similar to the way recommender systems personalize information in web search, it is also feasible to develop an ontology-based recommender system for industry to explore the most relevant explicit and implicit knowledge and information for a given stakeholder.