Knowledge‐based reasoning and recommendation framework for intelligent decision making. Issue 2 (7th February 2018)

• Record Type: Journal Article
• Authors: Ali, Rahman; Afzal, Muhammad; Sadiq, Muhammad; Hussain, Maqbool; Ali, Taqdir; Lee, Sungyoung; Khattak, Asad Masood
• Other Names: GarcÍa‐RodrÍguez José guestEditor.; Escalera Sergio guestEditor.; Psarrou Alexandra guestEditor.; Guyon Isabelle guestEditor.; Lewis Andrew guestEditor.; Leitner Juxi guestEditor.
• Is Part Of: Expert systems. Volume 35:Issue 2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-02-07
• Subjects: knowledge‐based recommendation -- physical activity recommendations -- reasoning and recommendation framework -- rule‐based reasoning -- sedentary behaviour; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12242
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: A physical activity recommendation system promotes active lifestyles for users. Real‐world reasoning and recommendation systems face the issues of data and knowledge integration, knowledge acquisition, and accurate recommendation generation. The knowledge‐based reasoning and recommendation framework (KRF) proposed here, which accurately generates reliable recommendations and educational facts for users, could solve those issues. The KRF methodology focuses on integrating data with knowledge, rule‐based reasoning, and conflict resolution. The integration issue is resolved using a semi‐automatic mapping approach in which rule conditions are mapped to data schema. The rule‐based reasoning methodology uses explicit rules with a maximum‐specificity conflict resolution strategy to ensure the generation of appropriate and correct recommendations. The data used during the reasoning process are generated in real time from users' physical activities and personal profiles in order to personalize recommendations. The proposed KRF is part of a wellness and health care platform, Mining Minds, and has been tested in the Mining Minds integrated environment using a sedentary user behaviour scenario. To evaluate the KRF methodology, a stand‐alone, open‐source application (Version 1.0) was released and tested using a dataset of 10 volunteers with 40 different types of sedentary behaviours. The KRF performance was measured using average execution time and recommendation accuracy.