Search (4 results, page 1 of 1)

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Abstract

In this paper, we present a novel personalized concept-based search mechanism for the Web of Data based on results categorization. The innovation of the paper comes from combining novel categorization and personalization techniques, and using categorization for providing personalization. In our approach, search results (Linked Open Data resources) are dynamically categorized into Upper Mapping and Binding Exchange Layer (UMBEL) concepts using a novel fuzzy retrieval model. Then, results with the same concepts are grouped together to form categories, which we call conceptlenses. Such categorization enables concept-based browsing of the retrieved results aligned to users' intent or interests. When the user selects a concept lens for exploration, results are immediately personalized. In particular, all concept lenses are personally re-organized according to their similarity to the selected lens. Within the selected concept lens; more relevant results are included using results re-ranking and query expansion, as well as relevant concept lenses are suggested to support results exploration. This allows dynamic adaptation of results to the user's local choices. We also support interactive personalization; when the user clicks on a result, within the interacted lens, relevant lenses and results are included using results re-ranking and query expansion. Extensive evaluations were performed to assess our approach: (i) Performance of our fuzzy-based categorization approach was evaluated on a particular benchmark (~10,000 mappings). The evaluations showed that we can achieve highly acceptable categorization accuracy and perform better than the vector space model. (ii) Personalized search efficacy was assessed using a user study with 32 participants in a tourist domain. The results revealed that our approach performed significantly better than a non-adaptive baseline search. (iii) Dynamic personalization performance was evaluated, which illustrated that our personalization approach is scalable. (iv) Finally, we compared our system with the existing LOD search engines, which showed that our approach is unique.

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Abstract

In the summer of 2008 all University of North Carolina libraries switched from a traditional library catalog interface supporting text-based searching (TextOnly) to a text and facet-based interface (TextFacet) to improve users' search experiences. This study seeks to understand the differences between these two interfaces and how they affect the search experience of the novice user. In this study, 40 participants were asked to search for resources using both interfaces. Their search times and accuracy were measured across three types of search tasks (known, partially known, and exploratory). After completing the searches, they were asked a series of questions about their experiences. The data were analyzed in order to identify strengths and weaknesses in both search interfaces. Thirty-six out of 40 participants preferred the TextFacet interface to the TextOnly interface. Using three dependent variables-time, accuracy, and rating-the two interfaces were compared and interactions were tested with the three task types. Search times for the TextFacet were shorter and participants preferred the TextFacet search interface over the TextOnly search interface. Performances across the three task types were different in terms of search time. The partially known and exploratory task types showed similar distributions for rating and accuracy. These distributions were distinctly different from the known task type. The results of this study may assist libraries in developing improved library catalog search interfaces that utilize facets as well as text searching.

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Date

27.12.2014 18:54:22

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Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly