Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Probability Ranking Principle Revisited
Information Retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Effective keyword search in relational databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Language model information retrieval with document expansion
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Proceedings of the 16th international conference on World Wide Web
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BANKS: browsing and keyword searching in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A general optimization framework for smoothing language models on graph structures
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A generative retrieval model for structured documents
Proceedings of the 17th ACM conference on Information and knowledge management
A Probabilistic Retrieval Model for Semistructured Data
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
A framework for evaluating database keyword search strategies
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
KOIOS: utilizing semantic search for easy-access and visualization of structured environmental data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
CDMW 2012 - city data management workshop: workshop summary
Proceedings of the 21st ACM international conference on Information and knowledge management
A personal perspective on keyword search over data graphs
Proceedings of the 16th International Conference on Database Theory
Searching in the city of knowledge: challenges and recent developments
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Map search via a factor graph model
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Keyword query processing over structured data has gained a lot of interest as keywords have proven to be an intuitive mean for accessing complex results in databases. While there is a large body of work that provides different mechanisms for computing keyword search results efficiently, a recent study has shown that the problem of ranking is much neglected. Existing strategies employ heuristics that perform only in ad-hoc experiments but fail to consistently and repeatedly deliver results across different information needs. We provide a principled approach for ranking that focuses on a well-established notion of what constitutes relevant keyword search results. In particular, we adopt relevance-based language models to consider the structure and semantics of keyword search results, and introduce novel strategies for smoothing probabilities in this structured data setting. Using a standardized evaluation framework, we show that our work largely and consistently outperforms all existing systems across datasets and various information needs.