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  • 1
    Online-Ressource
    Online-Ressource
    SAGE Publications ; 2016
    In:  Journal of Information Science Vol. 42, No. 6 ( 2016-12), p. 851-862
    In: Journal of Information Science, SAGE Publications, Vol. 42, No. 6 ( 2016-12), p. 851-862
    Kurzfassung: The semantic Web aims to provide to Web information with a well-defined meaning and make it understandable not only by humans but also by computers, thus allowing the automation, integration and reuse of high-quality information across different applications. However, current information retrieval mechanisms for semantic knowledge bases are intended to be only used by expert users. In this work, we propose a natural language interface that allows non-expert users the access to this kind of information through formulating queries in natural language. The present approach uses a domain-independent ontology model to represent the question’s structure and context. Also, this model allows determination of the answer type expected by the user based on a proposed question classification. To prove the effectiveness of our approach, we have conducted an evaluation in the music domain using LinkedBrainz, an effort to provide the MusicBrainz information as structured data on the Web by means of Semantic Web technologies. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.74 to 0.82 for a corpus of questions generated by a group of real-world end users.
    Materialart: Online-Ressource
    ISSN: 0165-5515 , 1741-6485
    RVK:
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2016
    ZDB Id: 439125-1
    ZDB Id: 2025062-9
    SSG: 24,1
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 2
    Online-Ressource
    Online-Ressource
    SAGE Publications ; 2018
    In:  Journal of Information Science Vol. 44, No. 4 ( 2018-08), p. 464-490
    In: Journal of Information Science, SAGE Publications, Vol. 44, No. 4 ( 2018-08), p. 464-490
    Kurzfassung: In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).
    Materialart: Online-Ressource
    ISSN: 0165-5515 , 1741-6485
    RVK:
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2018
    ZDB Id: 439125-1
    ZDB Id: 2025062-9
    SSG: 24,1
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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