Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A TeXQuery-based XML full-text search engine
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
NaLIX: an interactive natural language interface for querying XML
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
EntityRank: searching entities directly and holistically
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Scalable semantic web data management using vertical partitioning
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A relational approach to incrementally extracting and querying structure in unstructured data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Structured annotations of web queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Understanding the semantic structure of noun phrase queries
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Keyword++: a framework to improve keyword search over entity databases
Proceedings of the VLDB Endowment
Facet discovery for structured web search: a query-log mining approach
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Coreference aware web object retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Entwining structure into web search
Proceedings of the 7th International Workshop on Ranking in Databases
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In web search today, a user types a few keywords which are then matched against a large collection of unstructured web pages. This leaves a lot to be desired for when the best answer to a query is contained in structured data stores and/or when the user includes some structural semantics in the query. In our work, we include information from structured data sources into web results. Such sources can vary from fully relational DBs, to flat tables and XML files. In addition, we take advantage of information in such sources to automatically extract corresponding semantics from the query and use them appropriately in improving the overall relevance of results. For this demonstration, we show how we effectively capture, annotate and translate web user queries such as 'popular digital camera around $425' returning results from a shopping-like DB.