Compartir
Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories (en Inglés)
Heiko Angermann
(Autor)
·
Naeem Ramzan
(Autor)
·
Springer
· Tapa Dura
Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories (en Inglés) - Angermann, Heiko ; Ramzan, Naeem
57,08 €
60,08 €
Ahorras: 3,00 €
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis Listas
Origen: Reino Unido
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Miércoles 05 de Junio y el
Martes 18 de Junio.
Lo recibirás en cualquier lugar de España entre 1 y 5 días hábiles luego del envío.
Reseña del libro "Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories (en Inglés)"
This important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches. Different methods are investigated in accordance with the criteria of the Ontology Alignment Evaluation Initiative (OAEI). The text also highlights promising developments and innovative guidelines, to further motivate researchers and practitioners in the field.Topics and features: discusses the fundamentals and the latest developments in taxonomy matching, including the related fields of ontology matching and schema matching; reviews next-generation matching strategies, matching algorithms, matching systems, and OAEI campaigns, as well as alternative evaluations; examines how the latest techniques make use of different sources of background knowledge to enable precise matching between repositories; describes the theoretical background, state-of-the-art research, and practical real-world applications; covers the fields of dynamic taxonomies, personalized directories, catalog segmentation, and recommender systems.This stimulating book is an essential reference for practitioners engaged in data science and business intelligence, and for researchers specializing in taxonomy matching and semantic similarity assessment. The work is also suitable as a supplementary text for advanced undergraduate and postgraduate courses on information and metadata management.
