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Ontologies are increasingly utilized for integrating and organizing data and knowledge in research and industry. This book focuses on semantic data mining, leveraging domain ontologies as background knowledge to mine insights from domain ontologies and knowledge graphs, beyond just empirical data. The introductory chapters lay the theoretical groundwork for data mining and ontology representation. It presents various methods for semantic data mining, tackling tasks like pattern mining, classification, and similarity-based approaches. The book addresses specific challenges in using ontologies for data mining, such as managing knowledge incompleteness and defining a truly “semantic” similarity measure. Several chapters illustrate applications of semantic data mining, ranging from scenarios that employ lightweight ontologies for knowledge graph enrichment to advanced cases involving intelligent knowledge discovery assistants that utilize complex domain ontologies for meta-mining. This ontology-based meta-learning approach enhances full data mining processes. The book is aimed at researchers in semantic technologies, knowledge engineering, data science, and data mining, as well as developers of knowledge-based systems and applications.
Achat du livre
Semantic data mining, Agnieszka Ławrynowicz
- Langue
- Année de publication
- 2017
Modes de paiement
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- Titre
- Semantic data mining
- Sous-titre
- An Ontology-Based Approach
- Langue
- Anglais
- Auteurs
- Agnieszka Ławrynowicz
- Éditeur
- AKA
- Publié
- 2017
- Pages
- 194
- ISBN10
- 3898387240
- ISBN13
- 9783898387248
- Séries
- Description
- Ontologies are increasingly utilized for integrating and organizing data and knowledge in research and industry. This book focuses on semantic data mining, leveraging domain ontologies as background knowledge to mine insights from domain ontologies and knowledge graphs, beyond just empirical data. The introductory chapters lay the theoretical groundwork for data mining and ontology representation. It presents various methods for semantic data mining, tackling tasks like pattern mining, classification, and similarity-based approaches. The book addresses specific challenges in using ontologies for data mining, such as managing knowledge incompleteness and defining a truly “semantic” similarity measure. Several chapters illustrate applications of semantic data mining, ranging from scenarios that employ lightweight ontologies for knowledge graph enrichment to advanced cases involving intelligent knowledge discovery assistants that utilize complex domain ontologies for meta-mining. This ontology-based meta-learning approach enhances full data mining processes. The book is aimed at researchers in semantic technologies, knowledge engineering, data science, and data mining, as well as developers of knowledge-based systems and applications.