Learning the Common Structure of Data

Published 30 July 2000

Kristina Lerman, Steven Minton

Citations38

TL;DR

An efficient algorithm is presented that learns structural information about data from positive examples alone and automatically identifies data on Web pages so that the wrapper may be reinduced when the source format changes.

Abstract

The proliferation of online information sources has accentuated the need for tools that automatically validate and recognize data. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe two Web wrapper maintenance applications that employ this algorithm. The first application detects when a wrapper is not extracting correct data. The second application automatically identifies data on Web pages so that the wrapper may be reinduced when the source format changes.

Keywords

Computer ScienceDecision Sciences

American Journal of PhysicsHandbook of Mathematical Functions

40,436 Citations1966Donald A. McQuarrie

Machine LearningLearning Logical Definitions from Relations

1,425 Citations1990J. R. Quinlan

FOIL is a system that learns Horn clauses from data expressed as relations, but extends them to a first-order formalism, which has been applied successfully to several tasks taken from the machine learning literature.

View PDF

Wrapper induction for information extraction

1,044 Citations1997Nicholas Kushmerick, Daniel S. Weld

This work introduces wrapper induction, a method for automatically constructing wrappers, and identifies hlrt, a wrapper class that is e(cid:14)ciently learnable, yet expressive enough to handle 48% of a recently surveyed sample of Internet resources.

A hierarchical approach to wrapper induction

376 Citations1999Ion Muslea, Steve Minton +1 more

This work introduces an inductive algorithm, STALKER, that generates high accuracy extraction rules based on user-labeled training examples that can handle information sources that could not be wrapped by existing techniques.

View PDF

Lecture notes in computer scienceLearning stochastic regular grammars by means of a state merging method

352 Citations1994Rafael C. Carrasco, José Oncina

A new algorithm is proposed which allows for the identification of any stochastic deterministic regular language as well as the determination of the probabilities of the strings in the language.

Journal of Computational BiologyApproaches to the Automatic Discovery of Patterns in Biosequences

303 Citations1998Alvis Brāzma, Inge Jonassen +2 more

A review of a number of existing methods developed to solve the discovery of patterns in biosequences and how these relate to each other, focusing on the algorithms underlying the approaches.

Artificial IntelligenceInductive learning of structural descriptions

194 Citations1981Thomas G. Dietterich, Ryszard S. Michalski

Primary attention is given to methods of learning the simplest form of generalization, namely, the maximally specific conjunctive generalizations (MSC-generalizations) which completely characterize a single set of structural examples.

Modeling Web sources for information integration

172 Citations1998Craig A. Knoblock, Steven Minton +6 more

This work has developed methods for mapping web sources into a simple, uniform representation that makes it efficient to integrate multiple sources and makes it easy to maintain these agents and incorporate new sources as they become available.

Recognizing structure in Web pages using similarity queries

46 Citations1999William W. Cohen

The structure-recognition methods described can be used to learn a wrapper from examples, or for maintaining a wrapper as a Web page changes format, and in these settings, the top-ranked structure is meaningful nearly 85% of the time.

Elsevier eBooksLearning Prototypical Concept Descriptions

30 Citations1995Piew Datta, Dennis Kibler

PL, the algorithm for learning prototypical concept descriptions, is described and it is demonstrated that these descriptions can, in some situations, classify more accurately than standard Machine Learning algorithms.