Web search users complain of inaccurate results of the current search engines. Most of inaccurate results are from failing to understand user's search goal. This paper proposes a method to mine user's intentions and to build an intention map representing their information needs. It selects intention features from search logs obtained from previous search sessions on a given query and extracts user's intentions by using clustering and labeling algorithms. The mined user's intentions on the query are represented in an intention map. For the efficiency analysis of intention maps, we extracted user intentions using 2,600 search log data of a current domestic commercial web search engine. The experimental results using a web search engine with the intention maps show statistically significant improvements in user satisfaction scores.