Taxonomy Matching Using Background Knowledge

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.

​Dr. Heiko Angermann is an e-commerce, enterprise content management, and omni/multi-channel consultant, and the Head of Project Management at an e-commerce consulting house located in Nuremberg, Germany. Prof. Naeem Ramzan is a full Professor of Computing Engineering in the School of Engineering and Computingat the University of West of Scotland, Paisley, UK. His other publications include the successful Springer title Social Media Retrieval.