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View all- Lee HAbdar MYen N(2018)Event-based trend factor analysis based on hashtag correlation and temporal information miningApplied Soft Computing10.1016/j.asoc.2018.02.04471(1204-1215)Online publication date: Oct-2018
Refining imprecise spatio-temporal events: a network-based approach
Authors: 
Andreas Spitz, 
Johanna Geiß, Michael Gertz, Stefan Hagedorn, Kai-Uwe SattlerAuthors Info & Claims
Andreas Spitz, Johanna Geiß, Michael Gertz, Stefan Hagedorn, Kai-Uwe SattlerAuthors Info & ClaimsArticle No.: 5, Pages 1 - 10
Abstract
Events as composites of temporal, spatial and actor information are a central object of interest in many information retrieval (IR) scenarios. There are several challenges to such event-centric IR, which range from the detection and extraction of geographic, temporal and actor mentions in documents to the construction of event descriptions as triples of locations, dates, and actors that can support event query scenarios. For the latter challenge, existing approaches fall short when dealing with imprecise event components. For example, if the exact location or date is unknown, existing IR methods are often unaware of different granularity levels and the conceptual proximity of dates or locations.
To address these problems, we present a framework that efficiently answers imprecise event queries, whose geographic or temporal component is given only at a coarse granularity level. Our approach utilizes a network-based event model that includes location, date, and actor components that are extracted from large document collections. Instances of entity and event mentions in the network are weighted based on both their frequency of occurrence and textual distance to reflect semantic relatedness. We demonstrate the utility and flexibility of our approach for evaluating imprecise event queries based on a large collection of events extracted from the English Wikipedia for a ground truth of news events.
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- Refining imprecise spatio-temporal events: a network-based approach
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Published: 31 October 2016
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SIGSPATIAL'16
SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
October 31, 2016
California, Burlingame
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GIR '16 Paper Acceptance Rate 9 of 12 submissions, 75%;
Overall Acceptance Rate 46 of 61 submissions, 75%
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View all- Lee HAbdar MYen N(2018)Event-based trend factor analysis based on hashtag correlation and temporal information miningApplied Soft Computing10.1016/j.asoc.2018.02.04471(1204-1215)Online publication date: Oct-2018
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