Achieving Efficient Access to Large Integrated Sets of Semantic Data in Web Applications

Bellekens, Pieter; Sluijs, Kees van der; Woensel, William Van; Casteleyn, Sven; Houben, Geert‐Jan

doi:10.1109/icwe.2008.18

Cited by 4 publications

(3 citation statements)

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“…The most common role of ontology in RS design consists in providing a taxonomic classification of items [9][10][11]. In this approach, user profiles are indexed by the entities from the ontology and each dimension is weighted according to the preferences of the explicit (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, user profiles are indexed by the entities from the ontology and each dimension is weighted according to the preferences of the explicit (e.g. ratings, likes) [9,10] and/or implicit users (e.g. reads, views) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Most of these approaches either use light-weight ontologies or use ontologies as a source for a controlled vocabulary, resulting in a shallow or null use of the ontological hierarchical structure. The exception are the approaches that use inference engines [9,14]. Recent approaches [17,18] manage to handle large ontologies while exploiting relationships along the entire hierarchy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Sheridan¹,

Onsjö

Becerra

et al. 2019

Future Internet

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Collaborative filtering based recommender systems have been extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain when items mostly belong to the same category and are mostly alike (e.g. television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN algorithm. As study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We observed that our proposed method showed to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

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Section: Introductionmentioning

confidence: 99%