Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Enriching web taxonomies through subject categorization of query terms from search engine logs
Decision Support Systems - Web retrieval and mining
Finding Similar Queries to Satisfy Searches Based on Query Traces
OOIS '02 Proceedings of the Workshops on Advances in Object-Oriented Information Systems
Towards Automatic Generation of Query Taxonomy: A Hierarchical Query Clustering Approach
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Clique Analysis of Query Log Graphs
SPIRE '08 Proceedings of the 15th International Symposium on String Processing and Information Retrieval
Detecting Overlapping Community Structures in Networks
World Wide Web
Mining web query hierarchies from clickthrough data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Applications of web query mining
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Mining query log graphs towards a query folksonomy
Concurrency and Computation: Practice & Experience
| Hi-index | 0.00 |
The human interaction through the web generates both implicit and explicit knowledge. An example of an implicit contribution is searching, as people contribute with their knowledge by clicking on retrieved documents. Thus, an important and interesting challenge is to extract semantic relations among queries and their terms from query logs. In this paper we present and discuss results on mining large query log induced graphs, and how they contribute to query classification and to understand user intent and interest. Our approach consists on efficiently obtaining a hierarchical clustering for such graphs and, then, a hierarchical query folksonomy. Results obtained with real data provide interesting insights on semantic relations among queries and are compared with conventional taxonomies, namely the ODP categorization.