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http://dx.doi.org/10.15207/JKCS.2020.11.10.089

A New Semantic Distance Measurement Method using TF-IDF in Linked Open Data  

Cho, Jung-Gil (Department of Computer Engineering, Sungkyul University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.10, 2020 , pp. 89-96 More about this Journal
Abstract
Linked Data allows structured data to be published in a standard way that datasets from various domains can be interlinked. With the rapid evolution of Linked Open Data(LOD), researchers are exploiting it to solve particular problems such as semantic similarity assessment. In this paper, we propose a method, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating the Linked Data semantic distance between resources that can be used in the LOD-based recommender system. The semantic distance measurement model proposed in this paper is based on a similarity measurement that combines the LOD-based semantic distance and a new link weight using TF-IDF, which is well known in the field of information retrieval. In order to verify the effectiveness of this paper's approach, performance was evaluated in the context of an LOD-based recommendation system using mixed data of DBpedia and MovieLens. Experimental results show that the proposed method shows higher accuracy compared to other similar methods. In addition, it contributed to the improvement of the accuracy of the recommender system by expanding the range of semantic distance calculation.
Keywords
Linked Open Data; LOD; Semantic Distance; DBpedia; Resource;
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Times Cited By KSCI : 6  (Citation Analysis)
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1 C. Bizer, T. Heath & T. Berners-Lee. (2009). Linked Data-The Story So Far. International Journal on Semantic Web and Information Systems, 5(3), 1-22. DOI : 10.4018/jswis.2009081901   DOI
2 Google. (2004). RDF vocabulary description language 1.0: RDF schema. W3C[Online]. https://www.w3.org/2001/sw/RDFCore/Schema/200212bwm/
3 V. C. Ostuni, T. D. Noia, E. D. Sciascio & R. Mirizzi. (2013). Top-n recommendations from implicit feedback leveraging linked open data. In Proceedings of the 7th ACM conference on Recommender systems, 85-92. DOI : 10.1145/2507157.2507172
4 A. Passant. (2010). dbrec: Music Recommendations Using DBpedia. In ISWC 2010 SE-14, 209-224. DOI : 10.1007/978-3-642-17749-1_14
5 S. E. Middleton, D. De Roure & N. R. Shadbolt. (2009). Ontology-based recommender systems. In Handbook on ontologies, 779-796.
6 A. Passant. (2010, March). Measuring Semantic Distance on Linking Data and Using it for Resources Recommendations. In AAAI Spring Symposium: Linked Data Meets Artificial Intelligence (Vol. 77, p. 123).
7 G. Piao, S. S. Ara & J. G. Breslin, (2015). Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes. In 5th Joint International Semantic Technology Conference. (pp. 185-200). Springer, Cham. DOI: 10.1007/978-3-319-31676-5
8 S. Alfarhood, K. Labille & S. Gauch. (2017) PLDSD: Propagated Linked Data Semantic Distance. IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises(WETICE), 278-283. DOI: 10.1109/WETICE.2017.16
9 G. O. Silva, F. A. Durao & M. Capretz, (2019). PLDSD: Personalized Linked Data Semantic Distance for LOD-Based Recommender Systems. iiWAS2019. DOI: 10.1145/3366030.3306041
10 S. Alfarhood, S. Gauch & K. Labille. (2019). Semantic Distance Spreading Accross Entities in Linked Open Data. Information 2019, 10(15), 1-15. DOI: 10.3390/info10010015
11 Google. (2020). Movielens 1M Dataset. grouplens [Online]. https://grouplens.org/datasets/movielens/1m/
12 D. S. Park & H. J. Kim. (2018). A Proposal of Join Vector for Semantic Factor Reflection in TF-IDF Based Keyword Extraction. Journal of KIIT, 16(2), 1-16. DOI : 10.14801/JKIIT.2018.16.2.1
13 J. P. Leal, V. Rodrigues & R. Queiros. (2012). Computing semantic relatedness using dbpedia. Symposium on Languages, Applications and Technologies, 1st (pp. 133-147). Schloss Dagstuhl. DOI: 10.4230/OASIcs.SLATE.2012.133
14 G. Piao & J. G. Breslin. (2016). Measuring Semantic Distance for Linked Open Data-enabled Recommander Systems. SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, 315-320. DOI: 10.1145/2851613.2851839
15 Google. (2020). MappingMovielens2DBpedia. researchGate [Online]. https://www.researchgate.net/publication/297369577_mapping-movielens-dbpedia
16 J. G. Cho. (2020). A location localization method using Smartphone sensor on a subway. Journal of the Korea Convergence Society, 11(3), 37-43. DOI : 10.15207/JKCS.2020.11.3.037   DOI
17 D. Khongorzul, S. M. Lee & M. H. Kim. (2019). OrdinalEncoder based DNN for Natural Gas Leak Prediction. Journal of the Korea Convergence Society, 10(10), 7-13. DOI : 10.15207/JKCS.2019.10.10.007   DOI