An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
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Generalized Queries on Probabilistic Context-Free Grammars
IEEE Transactions on Pattern Analysis and Machine Intelligence
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EquiX---a search and query language for XML
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Holistic twig joins: optimal XML pattern matching
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Computation of the N Best Parse Trees for Weighted and Stochastic Context-Free Grammars
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Statistical properties of probabilistic context-free grammars
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Relating probabilistic grammars and automata
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A parsing: fast exact Viterbi parse selection
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Probabilistic CFG with latent annotations
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The dichotomy of conjunctive queries on probabilistic structures
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ProTDB: probabilistic data in XML
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Matching twigs in probabilistic XML
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Query efficiency in probabilistic XML models
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Incorporating constraints in probabilistic XML
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Recursive Markov chains, stochastic grammars, and monotone systems of nonlinear equations
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Consensus answers for queries over probabilistic databases
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Probabilistic XML via Markov Chains
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The VLDB Journal — The International Journal on Very Large Data Bases
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Stochastic context-free grammars (SCFGs) have long been recognized as useful for a large variety of tasks including natural language processing, morphological parsing, speech recognition, information extraction, Web-page wrapping and even analysis of RNA. A string and an SCFG jointly represent a probabilistic interpretation of the meaning of the string, in the form of a (possibly infinite) probability space of parse trees. The problem of evaluating a query over this probability space is considered under the conventional semantics of querying a probabilistic database. For general SCFGs, extremely simple queries may have results that include irrational probabilities. But, for a large subclass of SCFGs (that includes all the standard studied subclasses of SCFGs) and the language of tree-pattern queries with projection (and child/descendant edges), it is shown that query results have rational probabilities with a polynomial-size bit representation and, more importantly, an efficient query-evaluation algorithm is presented.