Enhancing POI search on maps via online address extraction and associated information segmentation

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/106725

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106725

Title:	Enhancing POI search on maps via online address extraction and associated information segmentation
Authors:	張嘉惠;Chang, Chia-Hui;Chuang, Hsiu-Min;Huang, Chia-Yi;Su, Yueng-Sheng;Li, Shu-Ying
Contributors:	資訊電機學院資訊工程學系
Keywords:	Algorithms;Artificial Intelligence;Computer Science;Crowdsourcing;Extraction;Location based services;Machine learning;Machines;Manufacturing;Mechanical Engineering;Mobile communication systems;Processes;Queries;Query processing;Search engines;Searching;Segmentation;Semantic web;Wireless networks
Date:	2016-04-01
Issue Date:	2026-04-23 13:38:44 (UTC+8)
Publisher:	Springer Netherlands;New York: Springer US
Abstract:	摘要： With the popularity of wireless networks and mobile devices, we have seen rapid growth in mobile applications and services, especially location-based services. However, most existing location-based services like Google Maps and Wikimapia rely on crowd-sourcing or business-data providers to maintain their points-of-interest (POI) databases, which are slow and insufficient. Because most updated information can be found on the Web, the insufficiency of current POI databases can be complemented by automatically extracting POIs and their descriptions from general webpages. In this study, we enhance location-based search on maps via online address extraction and associated information segmentation. Given a POI query that cannot be found on a map, we propose a method for extracting the address from search snippets of the query to exploit information from the Web. We demonstrate the application of sequence labeling to Chinese postal-address extraction and compare the performance with and without Chinese word segmentation. Meanwhile, we also present a novel algorithm for associated information segmentation by making use of a document-object model (DOM) tree structure based on the farthest distinguishable ancestor (FDA) of each address. The FDA algorithm is able to locate associated information for each Chinese address resulting in an improvement from an F-measure of 0.811 to 0.964. 其他題名： Appl Intell 出版者： New York: Springer US 出版日期： 2016-04-01 出處： Applied intelligence (Dordrecht, Netherlands), 2016-04, Vol.44 (3), p.539-556 資源來源： ABI/INFORM Collection 版權： Springer Science+Business Media New York 2015 版權： Springer Science+Business Media New York 2016 識別號： ISSN: 0924-669X 識別號： EISSN: 1573-7497 識別號： DOI: 10.1007/s10489-015-0707-5
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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