Knowledge Graphs and LLMs in Action (Paperback)
Language: English
Published by Manning Publications, New York, 2025
Series: Book 159 of 182 - In Action
- Softcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
£ 55.67
Quantity: 1 available
Add to basketItem description from seller
Paperback. Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way that gives it meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results. Iterative top-down modeling: Aligns every graph decision with clear business questions. Ontology and taxonomy starters: Jump-start graph design from your existing structured data. Python code walk-throughs: Let you replicate techniques on day one, no guesswork. GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions. Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets. Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice. Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets. You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact. Knowledge graphs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information thats vital for human knowledge. Theyre poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …
Seller Inventory # 9781633439894
- Title
- Knowledge Graphs and LLMs in Action (Paperback)
- Author
- Alessandro Negro
- Publisher
- Manning Publications, New York
- Publication year
- 2025
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1633439895
- ISBN 13
- 9781633439894
- Series
- Book 159 of 182: In Action
Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way that gives it meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results.
- Iterative top-down modeling: Aligns every graph decision with clear business questions.
- Ontology and taxonomy starters: Jump-start graph design from your existing structured data.
- Python code walk-throughs: Let you replicate techniques on day one, no guesswork.
- GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions.
- Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets.
Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice.
Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets.
You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Alessandro Negro is the Chief Scientist at GraphAware. He is one of the creators of GraphAware Hume, a mission critical knowledge graph platform.
Dr. Vlastimil Kus is the Lead Data Scientist at GraphAware where he contributes to the development of Hume. Over the years he gained significant experience in building and utilizing Knowledge Graphs from unstructured data using NLP and ML techniques in various domains. His current focus is NLP and Graph Machine Learning.
Dr. Giuseppe Futia is Senior Data Scientist at GraphAware. He studied Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.
Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.
"About the title" may belong to another edition of this title.
Grand Eagle Retail
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