Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Multiple-goal search algorithms and their application to web crawling
Eighteenth national conference on Artificial intelligence
Analysis of anchor text for web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Interactive Information Retrieval Using Clustering and Spatial Proximity
User Modeling and User-Adapted Interaction
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Web document clustering using hyperlink structures
Computational Statistics & Data Analysis
Toward State Space Island Identification in Multi-process Bidirectional Heuristic Search
AST '09 Proceedings of the 2009 International e-Conference on Advanced Science and Technology
Document clustering with universum
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Identifying aspects for web-search queries
Journal of Artificial Intelligence Research
Hierarchical web-page clustering via in-page and cross-page link structures
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Graph-based term weighting for information retrieval
Information Retrieval
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Effective representation of Web search results remains an open problem in the Information Retrieval community. For ambiguous queries, a traditional approach is to organize search results into groups (clusters), one for each meaning of the query. These groups are usually constructed according to the topical similarity of the retrieved documents, but it is possible for documents to be totally dissimilar and still correspond to the same meaning of the query. To overcome this problem, we exploit the thematic locality of the Web--relevant Web pages are often located close to each other in the Web graph of hyperlinks. We estimate the level of relevance between each pair of retrieved pages by the length of a path between them. The path is constructed using multi-agent beam search: each agent starts with one Web page and attempts to meet as many other agents as possible with some bounded resources. We test the system on two types of queries: ambiguous English words and people names. The Web appears to be tightly connected; about 70% of the agents meet with each other after only three iterations of exhaustive breadth-first search. However, when heuristics are applied, the search becomes more focused and the obtained results are substantially more accurate. Combined with a content-driven Web page clustering technique, our heuristic search system significantly improves the clustering results.