Personalized Concept-Based Search and Exploration on the Web of Data Using Results Categorization

As the size of the Linked Open Data (LOD) increases, searching and exploring LOD becomes more challenging. To overcome this issue, we propose a novel personalized search and exploration mechanism for the Web of Data (WoD) based on concept-based results categorization. In our approach, search results (LOD resources) are conceptually categorized into UMBEL concepts to form concept lenses, which assist exploratory search and browsing. 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 concept lens using a similarity measure. 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 is an innovative feature offered by our approach since it 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 categories and results are included using results re-ranking and query expansion. Our personalization approach is non-intrusive, privacy preserving and scalable since it does not require login and implemented at the client-side. To evaluate efficacy of the proposed personalized search, a benchmark was created on a tourism domain. The results showed that the proposed approach performs significantly better than a non-adaptive baseline concept-based search and traditional ranked list presentation.