A Linked Data browser with recommendations

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Durao, Frederico
Bridge, Derek G.
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Institute of Electrical and Electronics Engineers (IEEE)
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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.
Graph theory , Iterative methods , Linked Data , Online front-ends , Pattern classification , Recommender systems , Semantic Web , User profile , LDRec , Linked Data graph , Recommendation technique , User trial , Linked Data browser , Linked Data principles , Recommendation functionality , Off-line experiment , Iterative classification , Browsers , Resource description framework , Motion pictures , Data models , Tools , Browsing , Recommending , Collective , Classification , Iterative
Durao, F. and Bridge, D. (2018) 'A Linked Data browser with recommendations', 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, 5-7 November, pp. 189-196. doi:10.1109/ICTAI.2018.00038
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