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Descripción
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| Adding type information to resources belonging to large knowledge graphs is a challenging task, specially when considering those that are generated collaboratively, such as DBpedia, which usually contain errors and noise produced during the transformation process from di?erent data sources. It is important to assign the correct type(s) to resources in order to e?ciently exploit the information provided by the dataset. In this work we explore how machine learning classi?cation models can be applied to solve this issue, relying on the information de?ned by the ontology class hierarchy. We have applied our approaches to DBpedia and compared to the state of the art, using a per-level analysis. We also de?ne metrics to measure the quality of the results. Our results show that this approach is able to assign 56% more new types with higher precision and recall than the current DBpedia state of the art | |
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Internacional
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Si |
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Nombre congreso
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21st International Conference on Knowledge Engineering and Knowledge Management EKAW 2018 |
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Tipo de participación
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960 |
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Lugar del congreso
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Nancy, Francia |
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Revisores
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Si |
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ISBN o ISSN
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978-3-030-03666-9 |
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DOI
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10.1007/978-3-030-03667-6 |
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Fecha inicio congreso
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12/11/2018 |
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Fecha fin congreso
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16/11/2018 |
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Desde la página
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322 |
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Hasta la página
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337 |
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Título de las actas
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Knowledge Engineering and Knowledge Management. Proceedings of 21st International Conference on Knowledge Engineering and Knowledge Management EKAW 2018. LNCS, volume 11313 |