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作者 Uschold, Michael, author
書名 Demystifying OWL for the enterprise / Michael Uschold
出版項 [San Rafael, California] : Morgan & Claypool, 2018
國際標準書號 9781681731285 ebook
9781681733401 ePub
9781681731278 print
9781681732831 hardcover
國際標準號碼 10.2200/S00824ED1V01Y201801WBE017 doi
book jacket
說明 1 online resource (xxiv, 237 pages) : illustrations
text rdacontent
electronic isbdmedia
online resource rdacarrier
系列 Synthesis lectures on the semantic web, 2160-472X ; # 17
Synthesis digital library of engineering and computer science
Synthesis lectures on the semantic web ; # 17. 2160-472X
附註 Part of: Synthesis digital library of engineering and computer science
Includes bibliographical references (pages 215-218) and index
Part 1. Introducing OWL -- 1. Getting started: what do we need to say? -- 1.1 What is an ontology? What is OWL? -- 1.2 In the beginning there are things -- 1.3 Kinds of things vs. individual things -- 1.4 No thing is an island -- 1.4.1 Healthcare -- 1.4.2 Finance -- 1.4.3 Corporate registrations -- 1.5 Things can have a variety of attributes -- 1.6 More general things and more specific things -- 1.7 Drawing conclusions -- 1.8 Data and metadata -- 1.9 Summary learning -- 2. How do we say it in OWL? -- 2.1 Introduction -- 2.2 Saying things -- 2.2.1 An ontology is a set of triples -- 2.2.2 Namespaces, resource identifiers, and OWL syntax -- 2.2.3 Summary: informal to formal -- 2.2.4 Notational conventions -- 2.3 A simple ontology in healthcare -- 2.3.1 Healthcare data -- 2.3.2 Healthcare metadata -- 2.3.3 Individuals and their types -- 2.3.4 Richer semantics and automatic categorization -- 2.3.5 Other ways to specify meaning -- 2.3.6 Pause and reflect -- 2.4 Summary of key OWL concepts and assertions -- 2.4.1 Vocabularies and namespaces -- 2.4.2 Individuals and classes -- 2.4.3 Properties -- 2.4.4 Class expressions and restrictions -- 2.4.5 Drawing conclusions -- 2.5 Summary learning -- 3. Fundamentals: meaning, semantics, and sets -- 3.1 Logic -- 3.1.1 Reasoning and arguments -- 3.1.2 Formal semantics and sets -- 3.1.3 The open world -- 3.1.4 Resource identifiers -- 3.1.5 Literals and datatypes -- 3.1.6 Metaclasses -- 3.1.7 Expressions -- 3.1.8 Meaning, semantics, and ambiguity -- 3.1.9 Concepts vs. terms -- 3.1.10 The world of triples -- 3.1.11 Reuse and modularity -- 3.1.12 Triple stores, querying, and SPARQL -- 3.1.13 Summary learning -- 3.2 Summary for part 1 --
Part 2. Going into depth: properties and classes -- 4. Properties -- 4.1 Properties, relationships, and sets -- 4.2 Properties are first-class objects -- 4.3 Property hierarchies -- 4.4 Domain and range -- 4.4.1 Use domain and range with care -- 4.5 Inverse properties and property chains -- 4.5.1 Inverse properties -- 4.5.2 Property chains -- 4.6 Property characteristics -- 4.6.1 Functional properties -- 4.6.2 Transitive properties -- 4.6.3 Symmetric and asymmetric properties -- 4.7 Property characteristics of subproperties and inverse properties -- 4.7.1 Subproperties -- 4.7.2 Inverse properties -- 4.8 Data properties -- 4.8.1 Data vs. object properties -- 4.8.2 When to use data properties -- 4.9 Disjointness and equivalence -- 4.10 Annotation properties -- 4.11 Summary learning -- 5. Classes -- 5.1 Review: classes and sets -- 5.2 Class relationships -- 5.2.1 Subclass -- 5.2.2 Class equivalence -- 5.2.3 Disjoint classes -- 5.3 Class expressions -- 5.3.1 Anonymous classes and blank nodes -- 5.3.2 Boolean expressions -- 5.3.3 Enumeration -- 5.3.4 Property restrictions -- 5.3.5 Summary: class expressions -- 5.4 Property restrictions -- 5.4.1 Usage scenarios -- 5.4.2 Anatomy of a property restriction -- 5.4.3 Existential: someValuesFrom -- 5.4.4 Universal: allValuesFrom -- 5.4.5 Minimum cardinality -- 5.4.6 Maximum cardinality -- 5.4.7 Exact cardinality -- 5.4.8 Individual value: hasValue -- 5.4.9 Data property restrictions -- 5.4.10 Summary: property restrictions -- 5.5 Summary learning -- 5.6 Conclusion for Part 2 --
Part 3. Using OWL in practice -- 6. More examples -- 6.1 Patient visit -- 6.2 Collateral -- 6.3 Internal vs. external transactions -- 6.4 Inference -- 6.4.1 Patient visit -- 6.4.2 Inference with partial information -- 6.4.3 Security agreement and collateral -- 6.4.4 Internal organizations and transactions -- 6.4.5 Classification inference -- 6.5 Summary learning -- 7. OWL limitations -- 7.1 Metaclasses -- 7.2 The object of a triple -- 7.3 N-ary relations -- 7.4 Rules -- 7.5 Dates and times -- 7.6 Cardinality restrictions with transitive properties or property chains -- 7.7 Inference at scale -- 7.8 Summary learning -- 8. Go forth and ontologize -- 8.1 Modeling principles and tools -- 8.1.1 Conceptual and operational -- 8.1.2 Concepts, terms, and naming conventions -- 8.1.3 Modeling choice: data or object property? -- 8.1.4 Modeling choice: class or property? -- 8.1.5 Modeling choice: class or individual? -- 8.1.6 Modularity for reusability -- 8.1.7 Ontology editors and inference engines -- 8.2 modeling patterns -- 8.2.1 Genus differentia -- 8.2.2 Orphan classes and high-level disjoints -- 8.2.3 Upper ontologies -- 8.2.4 N-ary relations -- 8.2.5 Buckets, buckets everywhere -- 8.2.6 Roles -- 8.3 Common pitfalls -- 8.3.1 Reading too much into IRIs and labels -- 8.3.2 Unique name assumption -- 8.3.3 Namespace proliferation -- 8.3.4 Domain and range -- 8.3.5 Less is more -- 8.4 Less frequently used OWL constructs -- 8.4.1 Pairwise disjoint and disjoint union -- 8.4.2 Datatypes -- 8.4.3 Different individuals -- 8.4.4 Same individuals -- 8.4.5 Deprecation -- 8.5 The open world revisited -- 8.6 Summary learning -- 8.7 Final remarks --
Appendices -- A.1 Acronyms & abbreviations -- A.2 e6Tools visual OWL syntax -- A.3 Recommended resources for further learning -- A.4 Answers to exercises -- A.4.1 Chapter 1 -- A.4.2 Chapter 2 -- A.4.4 Chapter 4 -- A.4.5 Chapter 5 -- A.4.6 Chapter 6 -- Author biography -- Index
Abstract freely available; full-text restricted to subscribers or individual document purchasers
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After a slow incubation period of nearly 15 years, a large and growing number of organizations now have one or more projects using the Semantic Web stack of technologies. The Web Ontology Language (OWL) is an essential ingredient in this stack, and the need for ontologists is increasing faster than the number and variety of available resources for learning OWL. This is especially true for the primary target audience for this book: modelers who want to build OWL ontologies for practical use in enterprise and government settings. The purpose of this book is to speed up the process of learning and mastering OWL. To that end, the focus is on the 30% of OWL that gets used 90% of the time. Others who may benefit from this book include technically oriented managers, semantic technology developers, undergraduate and post-graduate students, and finally, instructors looking for new ways to explain OWL. The book unfolds in a spiral manner, starting with the core ideas. Each subsequent cycle reinforces and expands on what has been learned in prior cycles and introduces new related ideas. Part 1 is a cook's tour of ontology and OWL, giving an informal overview of what things need to be said to build an ontology, followed by a detailed look at how to say them in OWL. This is illustrated using a healthcare example. Part 1 concludes with an explanation of some foundational ideas about meaning and semantics to prepare the reader for subsequent chapters. Part 2 goes into depth on properties and classes, which are the core of OWL. There are detailed descriptions of the main constructs that you are likely to need in every day modeling, including what inferences are sanctioned. Each is illustrated with real-world examples. Part 3 explains and illustrates how to put OWL into practice, using examples in healthcare, collateral, and financial transactions. A small ontology is described for each, along with some key inferences. Key limitations of OWL are identified, along with possible workarounds. The final chapter gives a variety of practical tips and guidelines to send the reader on their way
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Title from PDF title page (viewed on May 29, 2018)
鏈接 Print version: 9781681731278 9781681732831
主題 Ontologies (Information retrieval)
Semantic Web
Knowledge acquisition (Expert systems)
OWL
ontology engineering
data modeling
conceptual modeling
Semantic Web
knowledge graph
enterprise ontology
semantic technology
semantics
reuse
modularity
metadata
resource description framework (RDF)
RDF Schema
triples
description logic
knowledge representation
web ontology language
Electronic books
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