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공공누리This item is licensed Korea Open Government License

dc.contributor.author
Nawi, Rosmamalmi Mat
dc.contributor.author
Noah, Shahrul Azman Mohd
dc.contributor.author
Zakaria, Lailatul Qadri
dc.date.accessioned
2022-02-18T08:02:44Z
dc.date.available
2022-02-18T08:02:44Z
dc.date.issued
2021-06-30
dc.identifier.issn
2287-4577
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/16284
dc.description.abstract
Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Korea Institute of Science and Technology Information
dc.relation.ispartofseries
Journal of Information Science Theory and Practice;Volume 9 Issue 2
dc.title
Issues and Challenges in the Extraction and Mapping of Linked Open Data Resources with Recommender Systems Datasets
dc.type
Serial
dc.identifier.doi
https://doi.org/10.1633/JISTaP.2021.9.2.5
dc.contributor.approver
KOAR, ADMIN
dc.date.dateaccepted
2022-02-18T08:02:44Z
dc.date.datesubmitted
2022-02-18T08:02:44Z
dc.subject.keyword
recommender system
dc.subject.keyword
linked open data
dc.subject.keyword
DBpedia
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8. KISTI 간행물 > JISTaP > Vol. 9 - No. 2
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