實(shí)用語(yǔ)義網(wǎng)

前言

In 2003, when the World Wide Web Consortium was working toward the ratifi-cation of the Recommendations for the Semantic Web languages RDF, RDFS, andOWL, we realized that there was a need for an industrial-level introductorycourse in these technologies. The standards were technically sound, but, as istypically the case with standards documents, they were written with technicalcompleteness in mind rather than education. We realized that for this technol-ogy to take off, people other than mathematicians and logicians would haveto learn the basics of semantic modeling.Toward that end, we started a collaboration to create a series of trainingsaimed not at university students or technologists but at Web developers whowere practitioners in some other field. In short, we needed to get the SemanticWeb out of the hands of the logicians and Web technologists, whose job hadbeen to build a consistent and robust infrastructure, and into the hands of thepractitioners who were to build the Semantic Web. The Web didn't grow tothe size it is today through the efforts of only HTML designers, nor would theSemantic Web grow as a result of only logicians' efforts.After a year or so of offering training to a variety of audiences, we delivered atraining course at the National Agriculture Library of the U.S. Department ofAgriculture. Present for this training were a wide variety of practitioners inmany fields, including health care, finance, engineering, national intelligence,and enterprise architecture. The unique synergy of these varied practitionersresulted in a dynamic four days of investigation into the power and subtlety ofsemantic modeling. Although the practitioners in the room were innovativeand intelligent, we found that even for these early adopters, some of the newways of thinking required for modeling in a World Wide Web context weretoo subtle to master after just a one-week course. One participant had registeredfor the course multiple times, insisting that something else "clicked" each timeshe went through the exercises.This is when we realized that although the course was doing a good job ofdisseminating the information and skills for the Semantic Web, another, morearchival resource was needed. We had to create something that students couldwork with on their own and could consult when they had questions. Thiswas the point at which the idea of a book on modeling in the Semantic Webwas conceived. We realized that the readership needed to include a wide varietyof people from a number of fields, not just programmers or Web application developers but all the people from different fields who were struggling to understand how to use the new Web languages.

內(nèi)容概要

語(yǔ)義網(wǎng)的發(fā)展孕育著萬(wàn)維網(wǎng)及其應(yīng)用的一場(chǎng)革命，作為語(yǔ)義網(wǎng)核心內(nèi)容的語(yǔ)言——RDF和OWL，逐漸得到廣泛的重視和應(yīng)用。本書(shū)是語(yǔ)義網(wǎng)的入門(mén)教程，詳細(xì)講述語(yǔ)義網(wǎng)的核心內(nèi)容的語(yǔ)言，包括語(yǔ)義網(wǎng)的概念、語(yǔ)義建模、RDF、RDF Schema、OWL基礎(chǔ)等。    本書(shū)對(duì)于任何對(duì)語(yǔ)義網(wǎng)感興趣的專(zhuān)業(yè)技術(shù)人員都是十分難得的參考書(shū)。

作者簡(jiǎn)介

Dean　Allemang，世界知名的語(yǔ)義網(wǎng)專(zhuān)家。英國(guó)劍橋大學(xué)數(shù)學(xué)專(zhuān)業(yè)碩士，美國(guó)俄亥俄州立大學(xué)計(jì)算機(jī)專(zhuān)業(yè)博士。有豐富的語(yǔ)義網(wǎng)開(kāi)發(fā)經(jīng)驗(yàn)，曾創(chuàng)辦了最早的一家語(yǔ)義網(wǎng)技術(shù)公司，目前擔(dān)任美國(guó)領(lǐng)先的語(yǔ)義網(wǎng)技術(shù)公司TopQLladrant的首席科學(xué)家。JoumalofWebSemantics編委。世界最大的語(yǔ)義網(wǎng)研究機(jī)構(gòu)DigitalEnterprise研究院的評(píng)審委員會(huì)成員。自2003年起一直擔(dān)任國(guó)際語(yǔ)義網(wǎng)會(huì)議工業(yè)應(yīng)用方向的主席。
　　James　Hendler，語(yǔ)義網(wǎng)的創(chuàng)始人之一，萬(wàn)維網(wǎng)聯(lián)盟語(yǔ)義網(wǎng)協(xié)調(diào)組成員。美國(guó)人工智能協(xié)會(huì)和英國(guó)計(jì)算機(jī)協(xié)會(huì)會(huì)士。曾任美國(guó)國(guó)防部高級(jí)研究計(jì)劃局（DARPA）的信息系統(tǒng)辦公室首席科學(xué)家。目前是Rensselaer理工學(xué)院教授，并兼任麻省理工學(xué)院Web科學(xué)研究項(xiàng)目的副主任。他還是IEEEIntelligentSystems的主編，也是第一位擔(dān)任美國(guó)《科學(xué)》雜志評(píng)審委員的計(jì)算機(jī)科學(xué)家。

書(shū)籍目錄

CHAPTER 1 What Is the Semantic Web?  What Is a Web?  Smart Web, Dumb Web  Smart Web Applications  A Connected Web Is a Smarter Web  Semantic Data  A Distributed Web of Data  Features of a Semantic Web  What about the Round-Worlders?  To Each Their Own  There's Always One More  Summary  Fundamental ConceptsCHAPTER 2 Semantic Modeling  Modeling for Human Communication  Explanation and Prediction  Mediating Variability  Variation and Classes  Variation and Layers  Expressivity in Modeling  Summary  Fundamental ConceptsCHAPTER 3 RDF--The Basis of the Semantic Web  Distributing Data Across the Web  Merging Data from Multiple Sources  Namespaces, URIs, and Identity  Expressing URIs in Print  Standard Namespaces  Identifiers in the RDF Namespace  Challenge- RDF and Tabular Data  Higher-Order Relationships  Alternatives for Serialization  N-Triples  Notation 3 RDF (N3)  RDF/XML  Blank Nodes  Ordered Information in RDF  Summary  Fundamental ConceptsCHAPTER 4 Semantic Web Application Architecture  RDF Parser/Serializer  Other Data Sources--Converters and Scrapers  RDF Store  RDF Data Standards and Interoperability of  RDF Stores  RDF Query Engines and SPARQL  Comparison to Relational Queries  Application Code  RDF-Backed Web Portals  Data Federation  Summary  Fundamental ConceptsCHAPTER 5 RDF and Inferencing  Inference in the Semantic Web  Virtues of hfference-Based Semantics  Where are the Smarts?  Asserted Triples versus Inferred Triples  When Does Inferencing Happen?  Inferencing as Glue  Summary  Fundamental ConceptsCHAPTER 6 RDF Schema  Schema Languages and Their Functions  What Does It Mean? Semantics as Inference  The RDF Schema Language  Relationship Propagation through  rdfs:subPropertyOf  Typing Data by Usage--rdfs:domain  and rdfs:range  Combination of Domain and Range with  rdfs:subClassOf  RDFS Modeling Combinations and Patterns  Set Intersection  Property Intersection  Set Union  Property Union  Property Transfer  Challenges  Term Reconciliation  Instance-Level Data Integration  Readable Labels with rdfs:label  Data Typing Based on Use  Filtering Undefined Data  RDFS and Knowledge Discovery  Modeling with Domains and Ranges  Multiple Domains/Ranges  Nonmodeling Properties in RDFS  Cross-Referencing Files: rdfs:seeAlso  Organizing Vocabularies: rdfs:isDefmedBy  Model Documentation: rdfs:comment  Summary  Fundamental ConceptsCHAPTER  RDFS-Plus  Inverse  Challenge: Integrating Data that Do Not Want  to Be Integrated  Challenge: Using the Modeling Language to  Extend the Modeling Language  Challenge: The Marriage of Shakespeare  Symmetric Properties  Using OWL to Extend OWL  Transitivity  Challenge: Relating Parents to Ancestors  Challenge: Layers of Relationships  Managing Networks of Dependencies  Equivalence  Equivalent Classes  Equivalent Properties  Same Individuals  Challenge: Merging Data from Different Databases  Computing Sameness--Functional Properties  Functional Properties  Inverse Functional Properties  Combining Functional and Inverse  Functional Properties  A Few More Constructs  Summary  Fundamental ConceptsCHAPTER 8 Using RDFS-Plus in the Wild  SKOS  Semantic Relations in SKOS  Meaning of Semantic Relations  Special Purpose Inference  Published Subject Indicators  SKOS in Action  FOAF  People and Agents  Names in FOAF  Nicknames and Online Namds  Online Persona  Groups of People  Things People Make and Do  Identity in FOAF  It's Not What You Know, It's Who You Know  Summary  Fundamental ConceptsCHAPTER 9 Basic OWL  Restrictions  Example: Questions and Answers  Adding "Restrictions"  Kinds of Restrictions  Challenge Problems  Challenge: Local Restriction of Ranges  Challenge: Filtering Data Based on Explicit Type  Challenge: Relationship Transfer in SKOS  Relationship Transfer in FOAF  Alternative Descriptions of Restrictions  Summary  Fundamental ConceptsCHAPTER 10 Counting and Sets in OWL  Unions and Intersections  Closing the World  Enumerating Sets with owL'oneOf  Differentiating Individuals with  owl:differentFrom  Differentiating Multiple Individuals  Cardinality  Small Cardinality Limits  Set Complement  Disjoint Sets  Prerequisites Revisited  No Prerequisites  Counting Prerequisites  Guarantees of Existence  Contradictions  Unsatisfiable Classes  Propagation of Unsatisfiable Classes  Inferring Class Relationships  Reasoning with Individuals and with Classes  Summary  Fundamental ConceptsCHAPTER 11 Using OWL in the Wild  The Federal Enterprise Architecture Reference  Model Ontology  Reference Models and Composability  Resolving Ambiguity in the Model: Sets  versus Individuals  Constraints between Models  OWL and Composition  owl:Ontology  owl:imports  Advantages of the Modeling Approach  The National Cancer Institute Ontology  Requirements of the NCI Ontology  Upper-Level Classes  Describing Classes in the NCI Ontology  Instance-Level Inferencing in the NCI Ontology  Summary  Fundamental ConceptsCHAPTER 12 Good and Bad Modeling Practices  Getting Started  Know What You Want  Inference Is Key  Modeling for Reuse  Insightful Names versus Wishful Names  Keeping Track of Classes and Individuals  Model Testing  Common Modeling Errors  Rampant Classism (Antipattern)  Exclusivity (Antipattern)  Objectification (Antipattern)  Managing Identifiers for Classes (Antipattern)  Creeping Conceptualization (Antipattern)  Summary  Fundamental ConceptsCHAPTER 13 OWL Levels and Logic  OWL Dialects and Modeling Philosophy  Provable Models  Executable Models  OWL Full versus OWL DL  Class/Individual Separation  InverseFunctional Datatypes  OWL Lite  Other Subsets of OWL  Beyond OWL 1.0  Metamodeling  Multipart Properties  Qualified Cardinality  Multiple Inverse Functional Properties  Rules  Summary  Fundamental ConceptsCHAPTER 14 ConclusionsAPPENDIX Frequently Asked Questions  Further Reading  Index

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“本書(shū)正是我這些年一直期待的，它的出版將幫助更多人真正理解語(yǔ)義網(wǎng)。我相信它對(duì)于語(yǔ)義網(wǎng)社區(qū)的作用，就像《Java編程思想》之于Java社區(qū)?！薄　　狧enryStory，Sun公司語(yǔ)義網(wǎng)專(zhuān)家“本書(shū)的兩位作者都是語(yǔ)義網(wǎng)的權(quán)威，一個(gè)來(lái)自學(xué)界，一個(gè)來(lái)自業(yè)界，堪稱(chēng)完美組合。他們使原本晦澀難懂的語(yǔ)義網(wǎng)和相關(guān)的知識(shí)表示標(biāo)準(zhǔn)變得生動(dòng)易懂。強(qiáng)烈推薦?！薄　　狹arkA．Musen，斯坦福大學(xué)教授，著名開(kāi)源語(yǔ)義網(wǎng)平臺(tái)Prot6g6項(xiàng)目負(fù)責(zé)人“Hendler和Allemang的這本書(shū)正是我們一直在尋找的。以前的同類(lèi)圖書(shū)對(duì)做實(shí)際工作的人幫助甚微，而這本書(shū)可讀性很強(qiáng)，例子豐富而且簡(jiǎn)單易懂。我推薦大家都去買(mǎi)這本書(shū)?！薄　　狣avidMcComb

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