ARIEX: Automated ranking of information extractors
Information extractors are used to transform the user-friendly information in a web document into structured information that can be used to feed a knowledge-based system. Researchers are interested in ranking them to find out which one performs the best. Unfortunately, many rankings in the literatu...
| Autores: | , , |
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| 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:11441/131917 |
| Acceso en línea: | https://hdl.handle.net/11441/131917 https://doi.org/10.1016/j.knosys.2015.11.004 |
| Access Level: | acceso abierto |
| Palabra clave: | Web documents Information extraction Ranking method Automatisation |
| Sumario: | Information extractors are used to transform the user-friendly information in a web document into structured information that can be used to feed a knowledge-based system. Researchers are interested in ranking them to find out which one performs the best. Unfortunately, many rankings in the literature are deficient. There are a number of formal methods to rank information extractors, but they also have many problems and have not reached widespread popularity. In this article, we present ARIEX, which is an automated method to rank web information extraction proposals. It does not have any of the problems that we have identified in the literature. Our proposal shall definitely help authors make sure that they have advanced the state of the art not only conceptually, but from an empirical point of view; it shall also help practitioners make informed decisions on which proposal is the most adequate for a particular problem. |
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