Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology Based Personalized Search
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Privacy-enhancing personalized web search
Proceedings of the 16th international conference on World Wide Web
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Tag clouds for summarizing web search results
Proceedings of the 16th international conference on World Wide Web
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Generating summary keywords for emails using topics
Proceedings of the 13th international conference on Intelligent user interfaces
Personalized Concept-Based Clustering of Search Engine Queries
IEEE Transactions on Knowledge and Data Engineering
Search personalization through query and page topical analysis
User Modeling and User-Adapted Interaction
NAGA: Searching and Ranking Knowledge
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Combining WordNet and ConceptNet for automatic query expansion: a learning approach
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Efficient semantic network construction with application to PubMed search
Knowledge-Based Systems
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Search engines are very effective in finding relevant pages for a query. When a query is ambiguous, the search engine returns a mix of results for different semantic interpretations of the query. This paper proposes a method to extract concepts from the search results of a query, and, treating each retrieved concept as a query, it recursively constructs a network of concepts related to different semantic interpretations of the query. By connecting networks of concepts obtained from different queries, a large integrated network, called Concept Relation Network (CRN), is formed. CRN is a semantic network that can be automatically constructed and maintained using existing search engines (e.g., Google) on the web. Taking advantage of large scale commercial search engines, CRN is able to derive a large number of highly coherent, highly related concepts. We study several ways to weight the connections between the concepts in CRN. By distinguishing between location concepts and content concepts, we analyze the ambiguity of each type of concepts individually. We also propose to extract concept clusters from CRN based on different graph topology. We observe that complete subgraphs in CRN can be used to effectively determine semantically related concepts. Finally, we apply CRN to search engine personalization. Experimental results show that the application of CRN to a concept-based personalization algorithm significantly improves precision comparing to the baseline.