Piao, Guangyuan and Breslin, John G. (2016) Measuring semantic distance for linked open data-enabled recommender systems. SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing. pp. 315-320.

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Abstract

The Linked Open Data (LOD) initiative has been quite successful in terms of publishing and interlinking data on the Web. On top of the huge amount of interconnected data, measuring relatedness between resources and identifying their relatedness could be used for various applications such as LOD-enabled recommender systems. In this paper, we propose various distance measures, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating Linked Data semantic distance between resources that can be used in a LOD-enabled recommender system. We evaluated the distance measures in the context of a recommender system that provides the top-N recommendations with baseline methods such as LDSD. Results show that the performance is significantly improved by our proposed distance measures incorporating normalizations that use both of the resources and global appearances of paths in a graph.

Item Type:	Article
Keywords:	Measuring; semantic; distance; linked; open data-enabled; recommender systems;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15640
Identification Number:	https://doi.org/10.1145/2851613.2851839
Depositing User:	Guangyuan Piao
Date Deposited:	08 Mar 2022 16:03
Journal or Publication Title:	SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
Publisher:	ACM Digital Library
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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