39 research outputs found
Schema-Guided Induction of Monadic Queries
International audienceThe induction of monadic node selecting queries from partially annotated XML-trees is a key task in Web information extraction. We show how to integrate schema guidance into an RPNI-based learning algorithm, in which monadic queries are represented by pruning node selecting tree transducers. We present experimental results on schema guidance by the DTD of HTML
Query Induction with Schema-Guided Pruning Strategies
International audienceInference algorithms for tree automata that define node selecting queries in unranked trees rely on tree pruning strategies. These impose additional assumptions on node selection that are needed to compensate for small numbers of annotated examples. Pruning-based heuristics in query learning algorithms for Web information extraction often boost the learning quality and speed up the learning process. We will distinguish the class of regular queries that are stable under a given schema-guided pruning strategy, and show that this class is learnable with polynomial time and data. Our learning algorithm is obtained by adding pruning heuristics to the traditional learning algorithm for tree automata from positive and negative examples. While justified by a formal learning model, our learning algorithm for stable queries also performs very well in practice of XML information extraction
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
Logics for Unranked Trees: An Overview
Labeled unranked trees are used as a model of XML documents, and logical
languages for them have been studied actively over the past several years. Such
logics have different purposes: some are better suited for extracting data,
some for expressing navigational properties, and some make it easy to relate
complex properties of trees to the existence of tree automata for those
properties. Furthermore, logics differ significantly in their model-checking
properties, their automata models, and their behavior on ordered and unordered
trees. In this paper we present a survey of logics for unranked trees
Conditional Random Fields for XML Applications
XML tree labeling is the problem of classifying elements in XML documents. It is a fundamental task for applications like XML transformation, schema matching, and information extraction. In this paper we propose XCRFs, conditional random fields for XML tree labeling. Dealing with trees often raises complexity problems. We describe optimization methods by means of constraints and combination techniques that allow XCRFs to be used in real tasks and in interactive machine learning programs. We show that domain knowledge in XML applications easily transfers in XCRFs thanks to constraints and combination of XCRFs. We describe an approach based on XCRF to learn tree transformations. The approach allows to solve xml data integration tasks and restructuration tasks. We have developed an open source toolbox for XCRFs. We use it to propose a Web service for the generation of personalized RSS feeds from HTML pages
Learning Multipicity Tree Automata
International audienceIn this paper, we present a theoretical approach for the problem of learning multiplicity tree automata. These automata allows one to define functions which compute a number for each tree. They can be seen as a strict generalization of stochastic tree automata since they allow to define functions over any field K. A multiplicity automaton admits a support which is a non deterministic automaton. From a grammatical inference point of view, this paper presents a contribution which is original due to the combination of two important aspects. This is the first time, as far as we now, that a learning method focuses on non deterministic tree automata which computes functions over a field. The algorithm proposed in this paper stands in Angluin's exact model where a learner is allowed to use membership and equivalence queries. We show that this algorithm is polynomial in time in function of the size of the representation
Detecting Irrelevant subtrees to improve probabilistic learning from tree-structured data
International audienceIn front of the large increase of the available amount of structured data (such as XML documents), many algorithms have emerged for dealing with tree-structured data. In this article, we present a probabilistic approach which aims at a posteriori pruning noisy or irrelevant subtrees in a set of trees. The originality of this approach, in comparison with classic data reduction techniques, comes from the fact that only a part of a tree (i.e. a subtree) can be deleted, rather than the whole tree itself. Our method is based on the use of confidence intervals, on a partition of subtrees, computed according to a given probability distribution. We propose an original approach to assess these intervals on tree-structured data and we experimentally show its interest in the presence of noise
Conditional Random Fields for XML Applications
XML tree labeling is the problem of classifying elements in XML documents. It is a fundamental task for applications like XML transformation, schema matching, and information extraction. In this paper we propose XCRFs, conditional random fields for XML tree labeling. Dealing with trees often raises complexity problems. We describe optimization methods by means of constraints and combination techniques that allow XCRFs to be used in real tasks and in interactive machine learning programs. We show that domain knowledge in XML applications easily transfers in XCRFs thanks to constraints and combination of XCRFs. We describe an approach based on XCRF to learn tree transformations. The approach allows to solve xml data integration tasks and restructuration tasks. We have developed an open source toolbox for XCRFs. We use it to propose a Web service for the generation of personalized RSS feeds from HTML pages
Regular Rooted Graph Grammars
In dieser Arbeit wir ein pragmatischer Ansatz zur Typisierung, statischen Analyse und Optimierung von Web-Anfragespachen, speziell Xcerpt, untersucht. Pragmatisch ist der Ansatz in dem Sinne, dass dem Benutzer keinerlei Einschränkungen aus Entscheidbarkeits- oder Effizienzgründen auf modellierbare Typen gestellt werden. Effizienz und Entscheidbarkeit werden stattdessen, falls nötig, durch Vergröberungen bei der Typprüfung erkauft.
Eine Typsprache zur Typisierung von Graph-strukturierten Daten im Web wird eingeführt. Modellierbare Graphen sind so genannte gewurzelte Graphen, welche aus einem Spannbaum und Querreferenzen aufgebaut sind. Die Typsprache basiert auf
reguläre Baum Grammatiken, welche um typisierte Referenzen erweitert wurde. Neben wie im Web mit XML üblichen geordneten strukturierten Daten, sind auch ungeordnete Daten, wie etwa in Xcerpt oder RDF üblich, modellierbar. Der dazu verwendete Ansatz---ungeordnete Interpretation Regulärer Ausdrücke---ist neu. Eine operationale Semantik für geordnete wie ungeordnete Typen wird auf Basis spezialisierter Baumautomaten und sog. Counting Constraints (welche wiederum auf presburgerarithmetische Ausdrücke) basieren. Es wird ferner statische Typ-Prüfung und -Inferenz von Xcerpt Anfrage- und Konstrukttermen, wie auch Optimierung von Xcerpt Anfragen auf Basis von Typinformation eingeführt.This thesis investigates a pragmatic approach to typing, static analysis and static
optimization of Web query languages, in special the Web query language Xcerpt. The
approach is pragmatic in the sense, that no restriction on the types are made for
decidability or efficiency reasons, instead precision is given up if necessary.
Pragmatics on the dynamic side means to use types not only to ensure validity of objects
operating on, but also influencing query selection based on types.
A typing language for typing of graph structured data on the Web is introduced.
The Graphs in mind are based on spanning trees with references, the typing languages
is based on regular tree grammars with typed reference extensions. Beside ordered data
in the spirit of XML, unordered data (i.e. in the spirit of the Xcerpt data model or
RDF) can be modelled using regular expressions under unordered interpretation – this
approach is new. An operational semantics for ordered and unordered types is given
based on specialized regular tree automata and counting constraints (them again based
on Presburger arithmetic formulae). Static type checking of Xcerpt query and construct
terms is introduced, as well as optimization of Xcerpt query terms based on schema
information