Personalized Concept-based Search on the Linked Open Data

Melike Sah, Vincent Wade

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 novelfuzzy 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-basedcategorization approach was evaluated on a particular benchmark (~10,000 mappings). The evaluations showed that we can achieve highly acceptable categorization accuracyand 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 ourpersonalization approach is scalable.(iv) Finally, we compared our system with the existing LOD search engines, which showed that our approach is unique.

Full Text: Untitled
Type of Paper: Research Paper
Keywords: Categorization, concept-based search,fuzzyretrieval model, linked open data, personalized search/exploration, query expansion, results re-ranking, semantic indexing, UMBEL
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