Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28058
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Opitz, Bernd
Sztyler, Timo
Jess, Michael
Knip, Florian
Bikar, Christian
Pfister, Bernd
Scherp, Ansgar
Contact Email: ansgar.scherp@stir.ac.uk
Title: On-the-fly entity resolution from distributed social media sources for mobile search and exploration
Editor(s): Roland, Michael
Citation: Opitz B, Sztyler T, Jess M, Knip F, Bikar C, Pfister B & Scherp A (2015) On-the-fly entity resolution from distributed social media sources for mobile search and exploration. In: Roland M (ed.) Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (MUM '15), volume 30-November-2015. 14th International Conference on Mobile and Ubiquitous Multimedia, Linz, Austria, 30.11.2015-02.12.2015. New York: ACM, pp. 14-24. https://doi.org/10.1145/2836041.2836043
Issue Date: 31-Dec-2015
Date Deposited: 22-Oct-2018
Conference Name: 14th International Conference on Mobile and Ubiquitous Multimedia
Conference Dates: 2015-11-30 - 2015-12-02
Conference Location: Linz, Austria
Abstract: We present an approach and mobile application for the interactive exploration and search of geo-located social media entities from different, distributed data providers on the web. When querying the providers, the returned results typically have some overlap. In addition, one has no guarantee that the providers reply within a given time interval. Thus, in order to provide users with geo-located entities in their vicinity in a timely manner, we need to take the asynchronous nature of the data providers' replies into account. Our novel on-the-fly entity resolution engine starts the entity resolution once it retrieves the first responses. It incrementally extends the entity resolution model when more responses arrive. Entities are propagated to the client once the resolution engine has processed them for the first time. Resolution results produced at a later point in time are sent as updates to the client and improve earlier, incomplete results. Our experiments show a matching precision of 95% and scalability of the on-the-fly entity resolution w.r.t. the number of resources being simultaneously processed.
Status: VoR - Version of Record
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