학술논문
A Linked Data Browser with Recommendations
Document Type
Conference
Author
Source
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) ICTAI Tools with Artificial Intelligence (ICTAI), 2018 IEEE 30th International Conference on. :189-196 Nov, 2018
Subject
Language
ISSN
2375-0197
Abstract
It is becoming more common to publish data in a way that accords with the Linked Data principles. In an effort to improve the human exploitation of this data, we propose a Linked Data browser that is enhanced with recommendation functionality. Based on a user profile, also represented as Linked Data, we propose a technique that we call LDRec that chooses in a personalized way which of the resources that lie within a certain neighbourhood in a Linked Data graph to recommend to the user. The recommendation technique, which is novel, is inspired by a collective classifier known as the Iterative Classification Algorithm. We evaluate LDRec using both an off-line experiment and a user trial. In the off-line experiment, we obtain higher hit rates than we obtain using a simpler classifier. In the user trial, comparing against the same simpler classifier, participants report significantly higher levels of overall satisfaction for LDRec.