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    [返回] 中文XML论坛 - 专业的XML技术讨论区休息区『 最新动态 & 业界新闻 』 → 《拥抱Web 3.0》- 由语义网技术权威Ora Lassila和James Hendler撰文 查看新帖用户列表

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     * 贴子主题: 《拥抱Web 3.0》- 由语义网技术权威Ora Lassila和James Hendler撰文 举报  打印  推荐  IE收藏夹 
       本主题类别: Ajax | Web架构 | Web Services | Semantic Web    
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    发贴心情 《拥抱Web 3.0》- 由语义网技术权威Ora Lassila和James Hendler撰文


    下载地址: http://www.mindswap.org/papers/2007/90-93.pdf


    部分摘要:


    In an article published in The New York Times
    this past November, reporter John Markoff stated
    that “commercial interest in Web 3.0 — or
    the ‘Semantic Web,’ for the idea of adding meaning
    — is only now emerging.”1 This characterization
    caused great confusion with respect to the
    relationships between the Semantic Web and the
    Web itself, as well as between the Semantic Web
    and some aspects of the so-called Web 2.0. Some
    wanted to reject the term “Web 3.0” as too
    business-oriented; others felt that the vision in the
    article was only part of the larger Semantic Web
    vision, and still others felt that, whatever it was
    called, the Semantic Web’s arrival in the Business
    section of The New York Times reflected an important
    coming of age.
    With the Resource Description Framework
    (RDF) and Web Ontology Language (OWL) — the
    languages that power the Semantic Web — becoming
    standards and new technologies reaching
    maturity for embedding semantics in existing Web
    pages and querying RDF knowledge stores, something
    exciting is clearly happening in this area.
    Semantic Web Background
    With more than 10 years’ work on the Semantic
    Web’s foundations and more than five years since
    the phrase became popular, it’s an opportune
    moment to look at the field’s current state and
    future opportunities. From a humble beginning as
    a methodology for machine-interpretable metadata
    and through a “world-embracing” vision of a
    new era of software (often — erroneously, in our
    opinion — attributed as science fiction), the
    Semantic Web has matured into a set of standards
    that support “open” data and a view of information
    processing that emphasizes information rather
    than processing.
    From one viewpoint, the Semantic Web is the
    symbiosis of Web technologies and knowledge
    representation (KR), which is a subfield of artificial
    intelligence (AI) concerned with constructing
    and maintaining (potentially complex) models of
    the world that enable reasoning about themselves
    and their associated information. As such, we can
    understand the Semantic Web through the lessons
    learned from the Web’s development and adoption,
    as well as (perhaps somewhat painfully) from the
    deployment of AI technologies.
    On the Web, we’ve seen the emergence of some
    completely new business models that do indeed
    work, despite initially seeming infeasible. These
    include the models introduced or perfected by
    Netscape (creating a community by giving stuff
    away), Amazon and eBay (marketplaces), and
    Yahoo! and Google (advertising-supported sites).
    Sharing data (or content, as it’s often called when
    discussing the Web) has unexpected and serendipitous
    outcomes — once you make something available,
    you have no idea how some people will use
    it. The long-tail phenomenon — for example,
    aggregate sales of low-selling items, such as specialized
    books, surpassing the total number of bestsellers
    sold — defies traditional thinking about
    business models, but it’s important to the new Webbased
    economy. Web sites don’t really exist in isolation
    — linking is what makes search engines work
    and gives the “blogosphere” its power.
    From the euphoria surrounding AI in the 1980s
    through the hangover of the “AI winter” in the
    1990s, we’ve learned what doesn’t work: you can’t
    sell a stand-alone “AI application.” These technologies
    make sense only when embedded within
    other systems. Tools are hard to sell and often fail
    to make good business sense (they certainly don’t
    make sense according to venture capitalists). Finally,
    thinking of AI itself, we observe that reasoning
    engines are a means to an end, rather than the end
    itself; how you use them is more important than
    the mere fact that you use them.


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    做个勤奋者,思考者,更要做个偏执狂!

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