Building Knowledge Graphs: A Practitioner's Guide (Paperback)

Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by saving interlinked descriptions of entities (objects, events, situations, or abstract concepts) while encoding the semantics underlying the terminology. How do you create a knowledge graph? And how do you move it from theory into practice?

Using hands-on examples, this practical book shows data scientists and data practitioners how to build their own custom knowledge graphs. Authors Jesus Barrasa, Maya Natarajan, and Jim Webber from Neo4j illustrate patterns commonly used for building knowledge graphs that solve many of today's pressing problems. You'll quickly discover how these graphs become exponentially more useful as you add more data.

• Learn the organizing principles necessary to build a knowledge graph
• Explore how graph databases serve as a foundation for knowledge graphs
• Understand how to import structured and unstructured data into your graph
• Follow examples to build integration-and-search knowledge graphs
• Understand what pattern detection knowledge graphs help you accomplish
• Explore dependency knowledge graphs through examples
• Use examples of natural language knowledge graphs and chatbots

知識圖譜非常有用，它可以幫助組織追蹤醫學研究、網絡安全威脅情報、GDPR合規性、網絡用戶參與度等等。它通過保存相互關聯的實體描述（對象、事件、情境或抽象概念），同時編碼底層術語的語義來實現這一目的。如何創建知識圖譜？如何將其從理論轉化為實踐？本實用書籍使用實例向數據科學家和數據從業人員展示如何構建自己的自定義知識圖譜。Neo4j的作者Jesus Barrasa、Maya Natarajan和Jim Webber通過實例演示了常用於構建解決當今許多重要問題的知識圖譜的模式。您將迅速發現，隨著添加更多數據，這些圖譜變得更加有用。本書包括以下內容：學習構建知識圖譜所需的組織原則；探索圖形數據庫作為知識圖譜的基礎；了解如何將結構化和非結構化數據導入圖譜；通過實例構建集成和搜索知識圖譜；了解知識圖譜的模式檢測能力；通過實例探索依賴知識圖譜；使用自然語言知識圖譜和聊天機器人的實例。

Dr. Jesus Barrasa - Jesus leads the Sales Engineering team in EMEA and is Neo4j's resident expert in Semantic technologies. He co-wrote Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly Report) and leads the development of Neosemantics (Neo4j plugin for RDF). Prior to joining Neo4j, Jesus worked for data integration companies like Denodo and Ontology Systems(now EXFO) where he got first-hand experience with many successful large Graph Technology projects for major companies all over the world. Jesus' Ph.D. is in Artificial Intelligence/Knowledge Representation, focused on the automatic repurposing of legacy relational data as Knowledge Graphs.

Dr. Maya Natarajan - Maya is Sr Director, Knowledge Graphs. At Neo4j, Maya is responsible for the 'go-to-market' strategy for knowledge graphs. She is the in-house knowledge graph expert and was a major contributor to Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly Report). Maya has positioned various technologies from blockchain to predictive and user-based analytics to machine learning to deep learning and search in a myriad of industries including Life Sciences, Financial Services, Supply Chain, and Manufacturing at various large and small organizations. Maya has a Ph.D. in Chemical Engineering from Rice University and started her career in biotechnology, where she has five patents to her name.

Dr. Jim Webber - Jim is Neo4j's Chief Scientist and Visiting Professor at Newcastle University, UK. At Neo4j, Jim works on fault-tolerant graph databases and co-wrote Graph Databases (1st and 2nd editions, O'Reilly), Graph Databases for Dummies (Wiley), and Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly Report). Jim has a long history of work on fault-tolerant distributed systems and often advises customers on issues of scale, performance, and fault tolerance for their data-intensive systems.

Dr. Jesus Barrasa - Jesus在EMEA地區領導銷售工程團隊，是Neo4j在語義技術方面的專家。他與他人合著了《Knowledge Graphs: Data in Context for Responsive Businesses》（O'Reilly Report），並領導了Neosemantics（Neo4j的RDF插件）的開發。在加入Neo4j之前，Jesus曾在Denodo和Ontology Systems（現為EXFO）等數據整合公司工作，並在世界各地的大型公司中獲得了許多成功的大型圖形技術項目的第一手經驗。Jesus的博士學位是在人工智能/知識表示方面，專注於將遺留的關聯數據自動轉化為知識圖形。

Dr. Maya Natarajan - Maya是知識圖形的高級總監。在Neo4j，Maya負責知識圖形的市場推廣策略。她是內部的知識圖形專家，也是《Knowledge Graphs: Data in Context for Responsive Businesses》（O'Reilly Report）的主要貢獻者。Maya在多個行業，包括生命科學、金融服務、供應鏈和製造等，為各種技術定位，從區塊鏈到預測和基於用戶的分析，再到機器學習、深度學習和搜索。Maya在Rice University獲得化學工程博士學位，並在生物技術領域開展了職業生涯，擁有五項專利。

Dr. Jim Webber - Jim是Neo4j的首席科學家，也是英國紐卡斯爾大學的客座教授。在Neo4j，Jim致力於容錯圖形數據庫的研究，並與他人合著了《Graph Databases》（第一版和第二版，O'Reilly）、《Graph Databases for Dummies》（Wiley）和《Knowledge Graphs: Data in Context for Responsive Businesses》（O'Reilly Report）。Jim在容錯分佈式系統方面有著悠久的研究歷史，並經常就規模、性能和容錯性等問題為客戶的數據密集型系統提供建議。