Knowledge Graphs and Llms in Action
暫譯: 知識圖譜與大型語言模型實戰
Negro, Alessandro, Kus, Vlastimil, Futia, Giuseppe
- 出版商: Manning
- 出版日期: 2025-11-18
- 售價: $2,050
- 貴賓價: 9.5 折 $1,948
- 語言: 英文
- 頁數: 472
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633439895
- ISBN-13: 9781633439894
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相關分類:
Machine Learning
尚未上市，無法訂購
相關主題
商品描述
Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights. In Knowledge Graphs and LLMs in Action you will learn how to: - Model knowledge graphs with an iterative top-down approach based in business needs
- Create a knowledge graph starting from ontologies, taxonomies, and structured data
- Use machine learning algorithms to hone and complete your graphs
- Build knowledge graphs from unstructured text data sources
- Reason on the knowledge graph and apply machine learning algorithms Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs and LLMs in Action, you'll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You'll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Knowledge graphs represent a network of real-world entities--from people and places to genes and proteins--and model the relationships between them. KGs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information that's vital for human knowledge. They're poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more. About the book Knowledge Graphs and LLMs in Action is a practical guide to putting knowledge graphs into action. It's full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets. Throughout the book, you'll find extensive examples and use-cases taken from healthcare, biomedicine, document archive management systems, and even law enforcement. You'll learn methodologies based on the very latest KG approaches, as well as deep learning graph techniques such as Graph Neural Networks and NLP-based tools like BERT. About the reader For readers who know the basics of machine learning. Examples in Python. About the author Dr. Alessandro Negro is the Chief Scientist at GraphAware. Alessandro has been a speaker at many prominent conferences and is the author of the Manning book Graph-Powered Machine Learning and several scientific publications. 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 has 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 and a Fellow at the Nexa Center for Internet & Society. He holds a Ph.D. in computer engineering from the Politecnico di Torino (Italy), where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs. Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. He holds a master's degree in software engineering from Unisalento (Italy). As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.
- Create a knowledge graph starting from ontologies, taxonomies, and structured data
- Use machine learning algorithms to hone and complete your graphs
- Build knowledge graphs from unstructured text data sources
- Reason on the knowledge graph and apply machine learning algorithms Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs and LLMs in Action, you'll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You'll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Knowledge graphs represent a network of real-world entities--from people and places to genes and proteins--and model the relationships between them. KGs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information that's vital for human knowledge. They're poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more. About the book Knowledge Graphs and LLMs in Action is a practical guide to putting knowledge graphs into action. It's full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets. Throughout the book, you'll find extensive examples and use-cases taken from healthcare, biomedicine, document archive management systems, and even law enforcement. You'll learn methodologies based on the very latest KG approaches, as well as deep learning graph techniques such as Graph Neural Networks and NLP-based tools like BERT. About the reader For readers who know the basics of machine learning. Examples in Python. About the author Dr. Alessandro Negro is the Chief Scientist at GraphAware. Alessandro has been a speaker at many prominent conferences and is the author of the Manning book Graph-Powered Machine Learning and several scientific publications. 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 has 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 and a Fellow at the Nexa Center for Internet & Society. He holds a Ph.D. in computer engineering from the Politecnico di Torino (Italy), where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs. Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. He holds a master's degree in software engineering from Unisalento (Italy). As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.
商品描述(中文翻譯)
知識圖譜有助於理解數據中對象、事件、情境和概念之間的關係，讓您能夠輕鬆識別重要的模式並做出更好的決策。本書提供了有效標記數據、建模知識圖譜以及利用其獲取有用見解的工具和技術。
在知識圖譜與大型語言模型實踐中，您將學習如何：- 根據業務需求採用自上而下的迭代方法建模知識圖譜- 從本體論、分類法和結構化數據開始創建知識圖譜
- 使用機器學習算法來完善和補全您的圖譜
- 從非結構化文本數據源構建知識圖譜
- 在知識圖譜上進行推理並應用機器學習算法 超越數據分析，開始基於有用的上下文知識做出決策。前沿的知識圖譜（KG）方法將這種能力掌握在您手中。在知識圖譜與大型語言模型實踐中，您將發現知識圖譜的理論，並學習如何構建能夠展示智能行為的服務。您將學會從基本原則創建KG，並親自開發針對醫療保健和金融等現實領域的顧問應用程序。 購買印刷版書籍可獲得Manning Publications提供的免費PDF和ePub格式電子書。 關於技術 知識圖譜代表了一個現實世界實體的網絡——從人和地點到基因和蛋白質——並建模它們之間的關係。KG代表了一種真正的範式轉變，改變了機器理解數據的方式，通過有效建模對人類知識至關重要的上下文信息。它們有望幫助徹底改變數據分析和機器學習，應用範圍從搜索引擎到電子商務等。 關於本書 知識圖譜與大型語言模型實踐是一本將知識圖譜付諸實踐的實用指南。書中充滿了構建和分析知識圖譜的技術和代碼範例，所有示範均使用真實的全尺寸數據集。在整本書中，您將找到來自醫療保健、生物醫學、文檔檔案管理系統甚至執法的廣泛示例和用例。您將學習基於最新KG方法的研究方法，以及深度學習圖技術，如圖神經網絡和基於NLP的工具，如BERT。 關於讀者 適合了解機器學習基礎的讀者。範例使用Python。 關於作者 阿萊桑德羅·內格羅博士是GraphAware的首席科學家。阿萊桑德羅曾在許多知名會議上發表演講，並且是Manning出版的《圖形驅動的機器學習》一書的作者，以及多篇科學出版物的作者。他是GraphAware Hume的創建者之一，這是一個關鍵的知識圖譜平台。 弗拉斯蒂米爾·庫斯博士是GraphAware的首席數據科學家，參與Hume的開發。多年來，他在使用NLP和ML技術從非結構化數據構建和利用知識圖譜方面積累了豐富的經驗。他目前的重點是NLP和圖形機器學習。 朱塞佩·富提亞博士是GraphAware的高級數據科學家，也是Nexa互聯網與社會中心的研究員。他擁有意大利都靈理工大學的計算機工程博士學位，研究了支持自動構建知識圖譜的圖形表示學習技術。 法比奧·蒙塔尼亞是GraphAware的首席機器學習工程師。他擁有意大利Unisalento的軟體工程碩士學位。作為科學與產業之間的橋樑，他協助快速將科學推理轉化為產品價值。
作者簡介
Dr. Alessandro Negro is the Chief Scientist at GraphAware. Alessandro has been a speaker at many prominent conferences and is the author of the Manning book Graph-Powered Machine Learning and several scientific publications. 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 and a Fellow at the Nexa Center for Internet & Society. He holds a Ph.D. in computer engineering from the Politecnico di Torino (Italy), where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs. Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. He holds a master's degree in software engineering from Unisalento (Italy). As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.
作者簡介(中文翻譯)
阿萊桑德羅·內格羅博士是GraphAware的首席科學家。阿萊桑德羅曾在許多知名會議上發表演講，並且是Manning出版的書籍Graph-Powered Machine Learning及多篇科學出版物的作者。他是GraphAware Hume的創建者之一，這是一個關鍵任務的知識圖譜平台。
弗拉斯蒂米爾·庫斯博士是GraphAware的首席數據科學家，負責Hume的開發。多年來，他在使用自然語言處理（NLP）和機器學習（ML）技術從非結構化數據中構建和利用知識圖譜方面積累了豐富的經驗。他目前的重點是NLP和圖形機器學習。朱塞佩·富提亞博士是GraphAware的高級數據科學家，也是Nexa互聯網與社會中心的研究員。他擁有意大利都靈理工大學的計算機工程博士學位，研究了圖形表示學習技術，以支持知識圖譜的自動構建。法比奧·蒙塔尼亞是GraphAware的首席機器學習工程師。他擁有意大利Unisalento的軟體工程碩士學位。作為科學與產業之間的橋樑，他協助快速將科學推理轉化為產品價值。
