TY - JOUR
T1 - Allied
T2 - A framework for executing linked data- based recommendation algorithms
AU - Figueroa, Cristhian
AU - Vagliano, Iacopo
AU - Rodriguez Rocha, Oscar
AU - Torchiano, Marco
AU - Zucker, Catherine Faron
AU - Corrales, Juan Carlos
AU - Morisio, Maurizio
N1 - Publisher Copyright: Copyright © 2017, IGI Global.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - The increase in the amount of structured data published on the Web using the principles of Linked Data means that now it is more likely to find resources on the Web of Data that represent real life concepts. Discovering and recommending resources on the Web of Data related to a given resource is still an open research area. This work presents a framework to deploy and execute Linked Data based recommendation algorithms to measure their accuracy and performance in different contexts. Moreover, application developers can use this framework as the main component for recommendation in various domains. Finally, this paper describes a new recommendation algorithm that adapts its behavior dynamically based on the features of the Linked Data dataset used. The results of a user study show that the algorithm proposed in this paper has better accuracy and novelty than other stateof- the-art algorithms for Linked Data.
AB - The increase in the amount of structured data published on the Web using the principles of Linked Data means that now it is more likely to find resources on the Web of Data that represent real life concepts. Discovering and recommending resources on the Web of Data related to a given resource is still an open research area. This work presents a framework to deploy and execute Linked Data based recommendation algorithms to measure their accuracy and performance in different contexts. Moreover, application developers can use this framework as the main component for recommendation in various domains. Finally, this paper describes a new recommendation algorithm that adapts its behavior dynamically based on the features of the Linked Data dataset used. The results of a user study show that the algorithm proposed in this paper has better accuracy and novelty than other stateof- the-art algorithms for Linked Data.
KW - DBpedia
KW - Evaluation Framework
KW - Interlinked Data
KW - Linked Data
KW - Recommender Algorithm
KW - Recommender System Semantic Recommender
KW - Web of Data
UR - http://www.scopus.com/inward/record.url?scp=85029738607&partnerID=8YFLogxK
U2 - https://doi.org/10.4018/IJSWIS.2017100107
DO - https://doi.org/10.4018/IJSWIS.2017100107
M3 - Article
SN - 1552-6283
VL - 13
SP - 134
EP - 154
JO - International Journal on Semantic Web and Information Systems
JF - International Journal on Semantic Web and Information Systems
IS - 4
ER -