857,004 research outputs found

    User experiments with the Eurovision cross-language image retrieval system

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    In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval. The system is evaluated by multilingual users for two search tasks with the system configured in English and five other languages. To our knowledge this is the first published set of user experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual search engine using little knowledge of any language other than English, (2) categorizing images assists the user's search, and (3) there are differences in the way users search between the proposed search tasks. Based on the two search tasks and user feedback, we describe important aspects of any CL image retrieval system

    Hybrid Search: Effectively Combining Keywords and Semantic Searches

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    This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontologybased search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines

    When are abrupt onsets found efficiently in complex visual search? : evidence from multi-element asynchronous dynamic search

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    Previous work has found that search principles derived from simple visual search tasks do not necessarily apply to more complex search tasks. Using a Multielement Asynchronous Dynamic (MAD) visual search task, in which high numbers of stimuli could either be moving, stationary, and/or changing in luminance, Kunar and Watson (M. A Kunar & D. G. Watson, 2011, Visual search in a Multi-element Asynchronous Dynamic (MAD) world, Journal of Experimental Psychology: Human Perception and Performance, Vol 37, pp. 1017-1031) found that, unlike previous work, participants missed a higher number of targets with search for moving items worse than for static items and that there was no benefit for finding targets that showed a luminance onset. In the present research, we investigated why luminance onsets do not capture attention and whether luminance onsets can ever capture attention in MAD search. Experiment 1 investigated whether blinking stimuli, which abruptly offset for 100 ms before reonsetting-conditions known to produce attentional capture in simpler visual search tasks-captured attention in MAD search, and Experiments 2-5 investigated whether giving participants advance knowledge and preexposure to the blinking cues produced efficient search for blinking targets. Experiments 6-9 investigated whether unique luminance onsets, unique motion, or unique stationary items captured attention. The results found that luminance onsets captured attention in MAD search only when they were unique, consistent with a top-down unique feature hypothesis. (PsycINFO Database Record (c) 2013 APA, all rights reserved)

    When are abrupt onsets found efficiently in complex visual search? : evidence from multi-element asynchronous dynamic search

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    Previous work has found that search principles derived from simple visual search tasks do not necessarily apply to more complex search tasks. Using a Multielement Asynchronous Dynamic (MAD) visual search task, in which high numbers of stimuli could either be moving, stationary, and/or changing in luminance, Kunar and Watson (M. A Kunar & D. G. Watson, 2011, Visual search in a Multi-element Asynchronous Dynamic (MAD) world, Journal of Experimental Psychology: Human Perception and Performance, Vol 37, pp. 1017-1031) found that, unlike previous work, participants missed a higher number of targets with search for moving items worse than for static items and that there was no benefit for finding targets that showed a luminance onset. In the present research, we investigated why luminance onsets do not capture attention and whether luminance onsets can ever capture attention in MAD search. Experiment 1 investigated whether blinking stimuli, which abruptly offset for 100 ms before reonsetting-conditions known to produce attentional capture in simpler visual search tasks-captured attention in MAD search, and Experiments 2-5 investigated whether giving participants advance knowledge and preexposure to the blinking cues produced efficient search for blinking targets. Experiments 6-9 investigated whether unique luminance onsets, unique motion, or unique stationary items captured attention. The results found that luminance onsets captured attention in MAD search only when they were unique, consistent with a top-down unique feature hypothesis. (PsycINFO Database Record (c) 2013 APA, all rights reserved)

    SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

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    The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.Comment: 10 pages + 2 pages appendix; 5 figures -- initial preprin
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