RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues


  • M. Uma Maheswari
  • J.G.R. Sathiaseelan






Travelogues, Semantic, Ranking, Travel Tags, Visualization


The travelogues contain prosperous geo-referenced information such as tours, weather, and expenses etc. Reading travelogues and finding useful trip information is a tedious task for many people. So as to correlate the georeferenced text meaningfully the semantic ranking is taken in account on the user contributing travelogues. In this way there is a requirement for creating programmed travelogue mining methods to pass on valuable data in a travelogue to its peruses in the more successful way. Visualizing a geo-referenced data with location tags and descriptions makes it helpful for readers to understand the fundamental substance of the travelogue. For user expedient, this paper proposes a Travelogue RankTagViz approach that semantically ranks and visualizes the tags of user travelogues. The RankTagViz contains two phases. In the first phase, the user travelogues are ranked based on semantic. In the next phase, the travelogues are visualized based on the tag and images. During semantic ranking, a semantic dimension reduction method is proposed to pre-process and mine useful information from the travelogues. After that a semantic rank mechanism is proposed to rank travelogues based on tags and POI (Point Of Interest). For tag visualization, location based tags and images are extracted from the travelogues and a novel UI is intended to give a superior client encounter, by arranging both the literary and visual data produced by committed voyagers in an alluring way. Test comes about on an arrangement of gathered travelogues show the proposed techniques' capacity to rank and imagine travelogues.


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How to Cite

Maheswari, M. U., & Sathiaseelan, J. (2018). RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues. International Journal of Engineering & Technology, 7(2.22), 53–56. https://doi.org/10.14419/ijet.v7i2.22.12304
Received 2018-05-01
Accepted 2018-05-01
Published 2018-05-01