Map search via a factor graph model

  • Authors:
  • Qi Zhang;Jihua Kang;Yeyun Gong;Huan Chen;Yaqian Zhou;Xuanjing Huang

  • Affiliations:
  • Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Map search has received considerable attention in recent years. With map search, users can specify target locations with textual queries. However, these queries do not always include well-formed addresses or place names. They may contain transpositions, misspellings, fragments and so on. Queries may significantly differ from items stored in the spatial database. In this paper, we propose to connect this task to the semi-structured retrieval problem. A novel factor graph-based semi-structured retrieval framework is introduced to incorporate concept weighting, attribute selection, and word-based similarity metrics together. We randomly sampled a number of queries from logs of a commercial map search engine and manually labeled their categories and relevant results for analysis and evaluation. The results of several experimental comparisons demonstrate that our method outperforms both state-of-the-art semi-structured retrieval methods and some commercial systems in retrieving freeform location queries.