Query-Based Learning of XPath Expressions

Carme, Julien; Ceresna, Michal; Goebel, Max

doi:10.1007/11872436_29

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…The results of [1,2,4,6,8,9] are, once again, completely different from (and complementary to) those of ours. Most significantly, we do not ask the user questions of any type, nor do we attempt to precisely learn the user's query of interest.…”

Section: Related Workcontrasting

confidence: 82%

“…Equivalence questions are considered more natural for a user, as membership questions often provide the user with a document that is completely different from the document of current interest, and ask questions about this different document. Many negative results have been shown, most notably, that tree automata cannot be learned in polynomial time with equivalence questions [4], as well as a similar negative result for learning even simple fragments of XPath [8,9]. Several of these works [19,21] have been experimentally tested against wrapper induction benchmarks, and have been shown to work extremely well.…”

Section: Related Workmentioning

confidence: 95%

“…Note that we neither arbitrarily choose some consistent XPath query to return to the user, 2 nor do we burden the user with questions. When the precise user's query is required for some purpose, our approach is not sufficient (and instead, those of [8,9] can be used). However, in many contexts, the user is simply interested in seeing enough nodes of interest (e.g., for imprecise querying of XML).…”

Section: Related Workmentioning

confidence: 98%

“…Slightly more related are the works of [1,2,4,6,8,9,19,21]. The goal of these papers is to learn a query (e.g., in the classes XPath, tree automata, tree patterns, (k, l)-contextual tree languages), usually using equivalence or membership questions.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Certain and possible XPath answers

Cohen

Weiss

2013

Proceedings of the 16th International Conference on Database Theory

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Formulating an XPath query over an XML document is a difficult chore for a non-expert user. This paper introduces a novel approach to ease the querying process. Instead of specifying a query, the user simply marks positive examples X + of nodes that fit her information need. She may also mark negative examples X − of undesirable nodes. A deductive method, to suggest additional nodes that will interest the user, is developed in this paper.To be precise, a node y is a certain answer if every query returning all positive examples X + , and not returning any negative example from X − , must also return y. Similarly, y is a possible answer if there exists a query returning X + and y, while not returning any node in X − . Thus, y is likely to be of interest to the user if y is a certain answer, and unlikely to be of interest if y is not even a possible answer. The complexity of finding certain and possible answers, with respect to various classes of XPath, is studied.It is shown that for a wide variety of XPath queries (including child and descendant axes, wildcards, branching and attribute constraints), certain and possible answers can be found efficiently, provided that X + and X − are of bounded size. To prove this result a novel algorithm is developed.

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Section: Related Workcontrasting

confidence: 82%

Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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Certain and possible XPath answers

Cohen

Weiss

2013

Proceedings of the 16th International Conference on Database Theory

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“…Given an XPath expression p and a tree t, we denote by p(t) the set of nodes in t that are selected by p. Carme et al discuss how active learning could be used to infer a target expression p from a MAT oracle [32]. In their interpretation of the learning model, answering equivalence query for a hypotheses expression q means verifying whether p(t) = q(t) for all trees t. If q and p are not equivalent, then the teacher returns all nodes in the symmetric difference of p(t) and q(t), i.e., the set of nodes of t that belong to exactly one of the two sets.…”

Section: Wrapper Inductionmentioning

confidence: 99%

Learning Tree Languages

Björklund

Fernau

2016

Topics in Grammatical Inference

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Tree languages have proved to be a versatile and rewarding extension of the classical notion of string languages. Many nice applications have been established over the years, in areas such as Natural Language Processing, Information Extraction, and Computational Biology. Although some properties of string languages transfer easily to the tree case, in particular for regular languages, several computational aspects turn out to be harder. It is therefore both of theoretical and of practical interest to investigate how far and in what ways Grammatical Inference algorithms developed for the string case are applicable to trees. This chapter surveys known results in this direction. We begin by recalling the basics of tree language theory. Then, the most popular learning scenarios and algorithms are presented. Several applications of Grammatical Inference of tree languages are reviewed in some detail. We conclude by suggesting a number of directions for future research.

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On the hardness of learning queries from tree structured data

Liu

2013

J Comb Optim

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Query-Based Learning of XPath Expressions

Cited by 6 publications

References 6 publications

Certain and possible XPath answers

Certain and possible XPath answers

Learning Tree Languages

On the hardness of learning queries from tree structured data

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