A DOMAIN-CENTRIC APPROACH TO DESIGNING USER INTERFACES OF VIDEO RETRIEVAL SYSTEMS

Thesis (PhD) - Indiana University, Information Science, 2007User- and task-centric efforts in video information retrieval (IR) research are needed because current experiments are showing few significant results. It is our belief that unsatisfactory results in video IR can be partially attributed to the overemphasis on technologically-driven approaches to interface development and system evaluation. This study explored variables that have been consistently overlooked in video retrieval efforts, including those related to domain and search tasks. The underlying goal of this study is to promote alternative means for evaluating video retrieval systems, and to make progress toward developing new design principles and a video seeking model. A series of interactive search runs were conducted using a video retrieval system called ViewFinder. ViewFinder was implemented to search and browse the NASA K - 16 Science Education Programs. The system includes new design features that take into account the unique characteristics of the domain and associated tasks. Users with a background in Science Education, including teachers and academic majors, were recruited to perform a number of search tasks. Results from the search experiments were collected and analyzed using both objective and subjective measures. From these results, researchers gained further knowledge about domain-centric video search tasks, including how textual, visual, and hybrid tasks were all deemed important by science educators. Further analysis of experimental results also revealed associations between search tasks, user interaction, interface features and functions, and system effectiveness. The evaluation of individual interface features and functions exhibited that keyword searching was significant for retrieving Science Education video. However, these experiments also produced positive results for various visual search features. Unlike keyword searching, which was consistent and effective across many task types, the use and effectiveness of visual search and browse features were shown to be task dependent. Overall, the results from this study highlight the importance of user- and task-centric methods in video retrieval, as they provided researchers with additional understanding of the influences of domain-specific search tasks on user interaction with video systems. In addition, the experimental methodology employed for this study encourages future foundations for developing and evaluating video search interfaces designed for specific domains and search tasks

Keyword Search on RDF Graphs - A Query Graph Assembly Approach

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is $O(k^{2l} \cdot l^{3l})$, where $l$ is the number of the keywords and $k$ is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both $l$ and $k$ are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency

Contextualised Browsing in a Digital Library's Living Lab

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201

Hybrid Profiling in Information Retrieval

Abstract-One of the main challenges in search engine quality of service is how to satisfy the needs and the interests of individual users. This raises the fundamental issue of how to identify and select the information that is relevant to a specific user. This concern over generic provision and the lack of search precision have provided the impetus for the research into Web Search personalisation. In this paper a hybrid user profiling system is proposed -a combination of explicit and implicit user profiles for improving the web search effectiveness in terms of precision and recall. The proposed system is content-based and implements the Vector Space Model. Experimental results, supported by significance tests, indicate that the system offers better precision and recall in comparison to traditional search engines