34,835 research outputs found
Wrapper Maintenance: A Machine Learning Approach
The proliferation of online information sources has led to an increased use
of wrappers for extracting data from Web sources. While most of the previous
research has focused on quick and efficient generation of wrappers, the
development of tools for wrapper maintenance has received less attention. This
is an important research problem because Web sources often change in ways that
prevent the wrappers from extracting data correctly. We present an efficient
algorithm that learns structural information about data from positive examples
alone. We describe how this information can be used for two wrapper maintenance
applications: wrapper verification and reinduction. The wrapper verification
system detects when a wrapper is not extracting correct data, usually because
the Web source has changed its format. The reinduction algorithm automatically
recovers from changes in the Web source by identifying data on Web pages so
that a new wrapper may be generated for this source. To validate our approach,
we monitored 27 wrappers over a period of a year. The verification algorithm
correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes,
resulting in precision of 0.73 and recall of 0.95. We validated the reinduction
algorithm on ten Web sources. We were able to successfully reinduce the
wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data
extraction task
Four Lessons in Versatility or How Query Languages Adapt to the Web
Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3Câs GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a âWeb of Dataâ
Inference of termination conditions for numerical loops
We present a new approach to termination analysis of numerical computations
in logic programs. Traditional approaches fail to analyse them due to non
well-foundedness of the integers. We present a technique that allows to
overcome these difficulties. Our approach is based on transforming a program in
way that allows integrating and extending techniques originally developed for
analysis of numerical computations in the framework of query-mapping pairs with
the well-known framework of acceptability. Such an integration not only
contributes to the understanding of termination behaviour of numerical
computations, but also allows to perform a correct analysis of such
computations automatically, thus, extending previous work on a
constraints-based approach to termination. In the last section of the paper we
discuss possible extensions of the technique, including incorporating general
term orderings.Comment: Presented at WST200
: Méthodes d'Inférence Symbolique pour les Bases de Données
This dissertation is a summary of a line of research, that I wasactively involved in, on learning in databases from examples. Thisresearch focused on traditional as well as novel database models andlanguages for querying, transforming, and describing the schema of adatabase. In case of schemas our contributions involve proposing anoriginal languages for the emerging data models of Unordered XML andRDF. We have studied learning from examples of schemas for UnorderedXML, schemas for RDF, twig queries for XML, join queries forrelational databases, and XML transformations defined with a novelmodel of tree-to-word transducers.Investigating learnability of the proposed languages required us toexamine closely a number of their fundamental properties, often ofindependent interest, including normal forms, minimization,containment and equivalence, consistency of a set of examples, andfinite characterizability. Good understanding of these propertiesallowed us to devise learning algorithms that explore a possibly largesearch space with the help of a diligently designed set ofgeneralization operations in search of an appropriate solution.Learning (or inference) is a problem that has two parameters: theprecise class of languages we wish to infer and the type of input thatthe user can provide. We focused on the setting where the user inputconsists of positive examples i.e., elements that belong to the goallanguage, and negative examples i.e., elements that do not belong tothe goal language. In general using both negative and positiveexamples allows to learn richer classes of goal languages than usingpositive examples alone. However, using negative examples is oftendifficult because together with positive examples they may cause thesearch space to take a very complex shape and its exploration may turnout to be computationally challenging.Ce mĂ©moire est une courte prĂ©sentation dâune direction de recherche, Ă laquelle jâai activementparticipĂ©, sur lâapprentissage pour les bases de donnĂ©es Ă partir dâexemples. Cette recherchesâest concentrĂ©e sur les modĂšles et les langages, aussi bien traditionnels quâĂ©mergents, pourlâinterrogation, la transformation et la description du schĂ©ma dâune base de donnĂ©es. Concernantles schĂ©mas, nos contributions consistent en plusieurs langages de schĂ©mas pour les nouveaumodĂšles de bases de donnĂ©es que sont XML non-ordonnĂ© et RDF. Nous avons ainsi Ă©tudiĂ©lâapprentissage Ă partir dâexemples des schĂ©mas pour XML non-ordonnĂ©, des schĂ©mas pour RDF,des requĂȘtes twig pour XML, les requĂȘtes de jointure pour bases de donnĂ©es relationnelles et lestransformations XML dĂ©finies par un nouveau modĂšle de transducteurs arbre-Ă -mot.Pour explorer si les langages proposĂ©s peuvent ĂȘtre appris, nous avons Ă©tĂ© obligĂ©s dâexaminerde prĂšs un certain nombre de leurs propriĂ©tĂ©s fondamentales, souvent souvent intĂ©ressantespar elles-mĂȘmes, y compris les formes normales, la minimisation, lâinclusion et lâĂ©quivalence, lacohĂ©rence dâun ensemble dâexemples et la caractĂ©risation finie. Une bonne comprĂ©hension de cespropriĂ©tĂ©s nous a permis de concevoir des algorithmes dâapprentissage qui explorent un espace derecherche potentiellement trĂšs vaste grĂące Ă un ensemble dâopĂ©rations de gĂ©nĂ©ralisation adaptĂ© Ă la recherche dâune solution appropriĂ©e.Lâapprentissage (ou lâinfĂ©rence) est un problĂšme Ă deux paramĂštres : la classe prĂ©cise delangage que nous souhaitons infĂ©rer et le type dâinformations que lâutilisateur peut fournir. Nousnous sommes placĂ©s dans le cas oĂč lâutilisateur fournit des exemples positifs, câest-Ă -dire desĂ©lĂ©ments qui appartiennent au langage cible, ainsi que des exemples nĂ©gatifs, câest-Ă -dire qui nâenfont pas partie. En gĂ©nĂ©ral lâutilisation Ă la fois dâexemples positifs et nĂ©gatifs permet dâapprendredes classes de langages plus riches que lâutilisation uniquement dâexemples positifs. Toutefois,lâutilisation des exemples nĂ©gatifs est souvent difficile parce que les exemples positifs et nĂ©gatifspeuvent rendre la forme de lâespace de recherche trĂšs complexe, et par consĂ©quent, son explorationinfaisable
Differentially Private Publication of Sparse Data
The problem of privately releasing data is to provide a version of a dataset
without revealing sensitive information about the individuals who contribute to
the data. The model of differential privacy allows such private release while
providing strong guarantees on the output. A basic mechanism achieves
differential privacy by adding noise to the frequency counts in the contingency
tables (or, a subset of the count data cube) derived from the dataset. However,
when the dataset is sparse in its underlying space, as is the case for most
multi-attribute relations, then the effect of adding noise is to vastly
increase the size of the published data: it implicitly creates a huge number of
dummy data points to mask the true data, making it almost impossible to work
with.
We present techniques to overcome this roadblock and allow efficient private
release of sparse data, while maintaining the guarantees of differential
privacy. Our approach is to release a compact summary of the noisy data.
Generating the noisy data and then summarizing it would still be very costly,
so we show how to shortcut this step, and instead directly generate the summary
from the input data, without materializing the vast intermediate noisy data. We
instantiate this outline for a variety of sampling and filtering methods, and
show how to use the resulting summary for approximate, private, query
answering. Our experimental study shows that this is an effective, practical
solution, with comparable and occasionally improved utility over the costly
materialization approach
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
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