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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