Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
SUITOR: an attentive information system
Proceedings of the 5th international conference on Intelligent user interfaces
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
WordSieve: A Method for Real-Time Context Extraction
CONTEXT '01 Proceedings of the Third International and Interdisciplinary Conference on Modeling and Using Context
Aiding knowledge capture by searching for extensions of knowledge models
Proceedings of the 2nd international conference on Knowledge capture
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Suggesting novel but related topics: towards context-based support for knowledge model extension
Proceedings of the 10th international conference on Intelligent user interfaces
Intelligent support for knowledge capture and construction
Intelligent support for knowledge capture and construction
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Emerging semantic communities in peer web search
P2PIR '06 Proceedings of the international workshop on Information retrieval in peer-to-peer networks
User profiling with hierarchical context: an e-Retailer case study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
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Proactive retrieval systems monitor a user's task context and automatically provide the user with related resources. The effectiveness of such systems depends on their ability to perform context-based retrieval, generating queries which return context-relevant results. Two factors make this task especially challenging for Web-based retrieval. First, the quality of Web retrieval can be strongly affected by the vocabulary used to generate the queries. If the system's vocabulary for describing the context differs from the vocabulary used in the resources themselves, relevant resources may be missed. Second, search engine restrictions on query length may make it difficult to include sufficient contextual information in a single query. This paper presents an algorithm, IACS (Incremental Algorithm for Context-Based Search), which addresses these problems by building up, applying, and refining partial context descriptions incrementally. In IACS, an initial term-based context description is the starting point for a cycle of mining search engines, performing context-based filtering of results, and refining context descriptions to generate new rounds of queries in an expanded vocabulary. IACS has been applied in a system for proactively supporting concept-map-based knowledge modeling, by retrieving resources relevant to target concepts in the context of the rich information provided by “in progress” concept maps. An evaluation of the system shows that it provides significant improvements over a baseline for retrieving context-relevant resources. We expect the algorithm to have broad applicability to context-based Web retrieval for rich contexts.