Questo libro esplora l'uso delle ontologie nella rappresentazione dei dati nell'intelligenza artificiale. Descrive metodologie per lo sviluppo di ontologie, da approcci semplici a NeON, e propone un modello semantico per un ambiente universitario, con esempi pratici in Protegé, Java e Apache Jena.
Ontology building and deployment in Java
Focusing on the methodologies for ontology development, the book explores various approaches, from simple to advanced models like Skeletal and NeON, tailored to specific modeling challenges. It proposes a semantic representation model for a university setting, demonstrating the construction of an ontology using Protegé. Additionally, it covers practical applications by utilizing Java and Apache Jena for executing desktop queries and web services, showcasing the versatility of ontologies in artificial intelligence and data representation.
An Introduction to Machine Learning Models - Supervised
Supervised learning is explored as a method where labeled data guides the training process, enabling predictions for unseen test examples. The book delves into various supervised machine learning models, covering theoretical foundations and practical applications. Readers will gain hands-on experience implementing models such as Logistic Regression, Decision Trees, Support Vector Machines, and Random Forest using Jupyter Lab with pandas and scikit-learn. The content is designed to build expertise in machine learning techniques systematically.