3 research outputs found
A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems
Personalisation, adaptation and recommendation are central features
of TEL environments. In this context, information retrieval techniques are applied
as part of TEL recommender systems to filter and recommend learning resources
or peer learners according to user preferences and requirements. However,
the suitability and scope of possible recommendations is fundamentally
dependent on the quality and quantity of available data, for instance, metadata
about TEL resources as well as users. On the other hand, throughout the last
years, the Linked Data (LD) movement has succeeded to provide a vast body of
well-interlinked and publicly accessible Web data. This in particular includes
Linked Data of explicit or implicit educational nature. The potential of LD to
facilitate TEL recommender systems research and practice is discussed in this
paper. In particular, an overview of most relevant LD sources and techniques is
provided, together with a discussion of their potential for the TEL domain in
general and TEL recommender systems in particular. Results from highly related
European projects are presented and discussed together with an analysis of
prevailing challenges and preliminary solutions.LinkedU
An ontology-based recommender system using scholar's background knowledge
Scholarās recommender systems recommend scientific articles based on the similarity of articles to scholarsā profiles, which are a collection of keywords that scholars are interested in. Recent profiling approaches extract keywords from the scholarsā information such as publications, searching keywords, and homepages, and train a reference ontology, which is often a general-purpose ontology, in order to profile the scholarsā interests. However, such approaches do not consider the scholarsā knowledge because the recommender system only recommends articles which are syntactically similar to articles that scholars have already visited, while scholars are interested in articles which contain comparatively new knowledge. In addition, the systems do not support multi-area property of scholarsā knowledge as researchers usually do research in multiple topics simultaneously and are expected to receive focused-topic articles in each recommendation. To address these problems, this study develops a domain-specific reference ontology by merging six Web taxonomies and exploits Wikipedia as a conflict resolver of ontologies. Then, the knowledge items from the scholarsā information are extracted, transformed by DBpedia, and clustered into relevant topics in order to model the multi-area property of scholarsā knowledge. Finally, the clustered knowledge items are mapped to the reference ontology by using DBpedia to create clustered profiles. In addition a semantic similarity algorithm is adapted to the clustered profiles, which enables recommendation of focused-topic articles that contain new knowledge. To evaluate performance of the proposed approach, three different data sets from scholarsā information in Computer Science domain are created, and the precisions in different cases are measured. The proposed method, in comparison with the baseline methods, improves the average precision by 6% when the new reference ontology along with the full scholarsā knowledge is utilized, by an extra 7.2% when scholarsā knowledge is transformed by DBpedia, and further 8.9% when clustered profile is applied. Experimental results certify that using knowledge items instead of keywords for profiling as well as transforming the knowledge items by DBpedia can significantly improve the recommendation performance. Besides, the domain-specific reference ontology can effectively capture the full scholarsā knowledge which results to more accurate profiling