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Information 2019, 10(1), 15;

Semantic Distance Spreading Across Entities in Linked Open Data

Department of Computer Science, King Saud University, Riyadh 11451, Saudi Arabia
Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Author to whom correspondence should be addressed.
This manuscript is an extended version of our paper “PLDSD: Propagated Linked Data Semantic Distance” published in the proceedings of Knowledge Engineering and Semantic Web, Szczecin, Poland, 8–10 November 2017.
Received: 27 November 2018 / Revised: 21 December 2018 / Accepted: 24 December 2018 / Published: 2 January 2019
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
PDF [996 KB, uploaded 2 January 2019]


Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommended by calculating a semantic distance between resources. The LDSD approach, however, has some drawbacks such as its inability to measure the semantic distance resources that are not directly linked to each other. In this paper, we first propose another variation of the LDSD approach, called wtLDSD, by extending indirect distance calculations to include the effect of multiple links of differing properties within LOD, while prioritizing link properties. Next, we introduce an approach that broadens the coverage of LDSD-based approaches beyond resources that are more than two links apart. Our experimental results show that approaches we propose improve the accuracy of the LOD-based recommendations over our baselines. Furthermore, the results show that the propagation of semantic distance calculation to reflect resources further away in the LOD graph extends the coverage of LOD-based recommender systems. View Full-Text
Keywords: linked data; semantic distance; recommender system linked data; semantic distance; recommender system

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Alfarhood, S.; Gauch, S.; Labille, K. Semantic Distance Spreading Across Entities in Linked Open Data. Information 2019, 10, 15.

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