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  • × language_ss:"e"
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × year_i:[2010 TO 2020}
  1. Sebastian, Y.: Literature-based discovery by learning heterogeneous bibliographic information networks (2017) 0.00
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    Abstract
    Literature-based discovery (LBD) research aims at finding effective computational methods for predicting previously unknown connections between clusters of research papers from disparate research areas. Existing methods encompass two general approaches. The first approach searches for these unknown connections by examining the textual contents of research papers. In addition to the existing textual features, the second approach incorporates structural features of scientific literatures, such as citation structures. These approaches, however, have not considered research papers' latent bibliographic metadata structures as important features that can be used for predicting previously unknown relationships between them. This thesis investigates a new graph-based LBD method that exploits the latent bibliographic metadata connections between pairs of research papers. The heterogeneous bibliographic information network is proposed as an efficient graph-based data structure for modeling the complex relationships between these metadata. In contrast to previous approaches, this method seamlessly combines textual and citation information in the form of pathbased metadata features for predicting future co-citation links between research papers from disparate research fields. The results reported in this thesis provide evidence that the method is effective for reconstructing the historical literature-based discovery hypotheses. This thesis also investigates the effects of semantic modeling and topic modeling on the performance of the proposed method. For semantic modeling, a general-purpose word sense disambiguation technique is proposed to reduce the lexical ambiguity in the title and abstract of research papers. The experimental results suggest that the reduced lexical ambiguity did not necessarily lead to a better performance of the method. This thesis discusses some of the possible contributing factors to these results. Finally, topic modeling is used for learning the latent topical relations between research papers. The learned topic model is incorporated into the heterogeneous bibliographic information network graph and allows new predictive features to be learned. The results in this thesis suggest that topic modeling improves the performance of the proposed method by increasing the overall accuracy for predicting the future co-citation links between disparate research papers.
    Footnote
    A thesis submitted in ful llment of the requirements for the degree of Doctor of Philosophy Monash University, Faculty of Information Technology.
  2. Oh, K.E.; Joo, S.; Jeong, E.-J.: Online consumer health information organization : users' perspectives on faceted navigation (2015) 0.00
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    Abstract
    We investigate facets of online health information that are preferred, easy-to-use and useful in accessing online consumer health information from a user's perspective. In this study, the existing classification structure of 20 top ranked consumer health information websites in South Korea were analyzed, and nine facets that are used in organizing health information in those websites were identified. Based on the identified facets, an online survey, which asked participants' preferences for as well as perceived ease-of-use and usefulness of each facet in accessing online health information, was conducted. The analysis of the survey results showed that among the nine facets, the "diseases & conditions" and "body part" facets were most preferred, and perceived as easy-to-use and useful in accessing online health information. In contrast, "age," "gender," and "alternative medicine" facets were perceived as relatively less preferred, easy-to-use and useful. This research study has direct implications for organization and design of health information websites in that it suggests facets to include and avoid in organizing and providing access points to online health information.
    Type
    a
  3. Xu, B.; Lin, H.; Lin, Y.: Assessment of learning to rank methods for query expansion (2016) 0.00
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    Abstract
    Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.
    Type
    a
  4. Moreira, W.; Martínez-Ávila, D.: Concept relationships in knowledge organization systems : elements for analysis and common research among fields (2018) 0.00
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    Type
    a

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