Classification of news by topic using location data

Zolzaya Dashdorj, Muhammad Tahir Khan, Loris Bozzato, Sang-Geun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


In this work, we will consider news articles to determine geolocalization of their information and classify their topics on the basis of an available open data source: OpenStreetMap (OSM). We propose a knowledge-based conceptual and computational approach that disambiguates place names (i.e., geo-objects and regions) mentioned in news articles in terms of geographic coordinates. The geo-located news articles are analyzed to identify local topics: we found that the mentioned geo-objects are a good proxy to classify news topics.

Original languageEnglish
Title of host publicationSemantic Technology - 6th Joint International Conference, JIST 2016, Revised Selected Papers
EditorsJun Sun, Yang Liu, Jin Song Dong, Grigoris Antoniou, Zhe Wang, Wei Hu, Yuan-Fang Li
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319501116
Publication statusPublished - 2016
Event6th Joint International Conference on Semantic Technology, JIST 2016 - Singapore, Singapore
Duration: 2016 Nov 22016 Nov 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10055 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th Joint International Conference on Semantic Technology, JIST 2016

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (number 2015R1A2A1A10052665).

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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