Knowledge-based reasoning and recommendation framework for intelligent decision making

ORCID Identifiers

0000-0002-9171-8573

Document Type

Article

Source of Publication

Expert Systems

Publication Date

4-1-2018

Abstract

Copyright © 2018 John Wiley & Sons, Ltd 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.

ISSN

0266-4720

Publisher

Blackwell Publishing Ltd

Volume

35

Issue

2

First Page

e12242

Disciplines

Computer Sciences

Keywords

knowledge-based recommendation, physical activity recommendations, reasoning and recommendation framework, rule-based reasoning, sedentary behaviour

Scopus ID

85041686607

Indexed in Scopus

yes

Open Access

no

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