Using ontologies for XML data cleaning
| Autor: | Monica Scannapieco, Diego Milano, Tiziana Catarci |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2005 |
| Předmět: |
Information retrieval
Knowledge representation and reasoning computer.internet_protocol Computer science business.industry database systems algorithms privacy-preserving record Ontology (information science) computer.software_genre Data modeling Knowledge base Data exchange Data quality Schema (psychology) Ontology Domain knowledge Logical data model Data pre-processing Data mining business computer XML |
| Zdroj: | On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops ISBN: 9783540297390 OTM Workshops |
| Popis: | Real data is often affected by errors and inconsistencies. Many of them depend on the fact that schemas cannot represent a sufficiently wide range of constraints. Data cleaning is the process of identifying and possibly correcting data quality problems that affect the data. Cleaning data requires to gather knowledge on the domain to which the data refer. Anyway, existing data cleaning techniques still access this knowledge as a fragmented collection of heterogenous rules and ad hoc data transformations. Furthermore, data cleaning methodologies for an important class of data based on the semistructured XML data model have not yet been proposed. In this paper we introduce the OXC framework, that offers a methodology for XML data cleaning based on a uniform representation of domain knowledge through an ontology We describe how to define XML related data quality metrics based on our domain knowledge representation, and give a definition of various metrics related to the completeness data quality dimension. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|