On Learning Web Information Extraction Rules with TANGO

The research on Enterprise Systems Integration focuses on proposals to support business processes by re-using existing systems. Wrappers help re-use web ap plications that provide a user interface only. They emulate a human user who interacts with them and extracts the information of interest in a s...

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Detalles Bibliográficos
Autores: Jiménez Aguirre, Patricia, Corchuelo Gil, Rafael
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2016
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/131977
Acceso en línea:https://hdl.handle.net/11441/131977
https://doi.org/10.1016/j.is.2016.05.003
Access Level:acceso abierto
Palabra clave:Web information extraction
Semi-structured documents
Open catalogues of features
Learning rules
Variation points
Configuration method
Descripción
Sumario:The research on Enterprise Systems Integration focuses on proposals to support business processes by re-using existing systems. Wrappers help re-use web ap plications that provide a user interface only. They emulate a human user who interacts with them and extracts the information of interest in a structured for mat. In this article, we present TANGO, which is our proposal to learn rules to extract information from semi-structured web documents with high precision and recall, which is a must in the context of Enterprise Systems Integration. It relies on an open catalogue of features that helps map the input documents into a knowledge base in which every DOM node is represented by means of HTML, DOM, CSS, relational, and user-defined features. Then a procedure with many variation points is used to learn extraction rules from that knowledge base; the variation points include heuristics that range from how to select a condition to how to simplify the resulting rules. We also provide a systematic method to help re-configure our proposal. Our exhaustive experimentation proves that it beats others regarding effectiveness and is efficient enough for practical purposes. Our proposal was devised to be as configurable as possible, which helps adapt it to particular web sites and evolve it when necessary.