though they are not good general
search terms. On the other hand, in a
specific social context (such as a particular person’s photos), the same tag
can be useful since it can designate a
particular individual. The use of a tag
as metadata often depends on such a
context, and the “network effect” in
these cites is thus socially organized. 19
A more ambitious use of metadata
involves recent applications of semantic Web technologies7 and represents
an important paradigm shift that is a
significant element of emerging Web
technologies. The semantic Web represents a new level of abstraction from
the underlying network infrastructure,
as the Internet and Web did earlier.
The Internet allowed programmers to
create programs that could communicate without concern for the network
of cables through which the communication had to flow. The Web allows programmers and users to work with a set
of interconnected documents without
concern for the details of the computers storing and exchanging them.
The semantic Web will allow programmers and users alike to refer to
real-world objects—people, chemicals,
agreements, stars, whatever—without
concern for the underlying documents
in which these things, abstract and
concrete, are described. While basic
semantic Web technologies have been
defined and are being deployed more
widely, little work has sought to explain
the effect of these new capabilities on
the connections within the Web of people who use them. 28
The semantic Web arena reflects two
principle nexuses of activity. One tends
to involve data (and the Web), and the
other on the domain (and semantics).
The first, based largely on innovation
in data-integration applications, focuses on developing Web applications that
employ only limited semantics but provide a powerful mechanism for linking
data entities using the URIs that are
the basis of the Web. Powered by the
RDF, these applications focus largely
on querying graph-oriented triple-store
databases using the emerging SPARQL
language, which helps create Web applications and portals that use REST-based models, integrating data from
multiple sources without preexisting
schema. The second, based largely on
the Web Ontology Language, or OWL,
looks to provide models that can be
used to represent expressive semantic
descriptions of application domains
and provide inferencing power for
both Web and non-Web applications
that need a knowledge base.
Current research is exploring how
the databases of the semantic Web
relate to traditional database approaches and to scaling semantic Web
stores to very large scales. 1 In terms of
modeling, one goal is to develop tools
to speed inference in large knowledge bases (without sacrificing performance), including how to exploit
trade-offs between expressivity and
reasoning to provide the capabilities
needed for Web scale. 15 A market is
beginning to emerge for “bottom-up”
tools driven by data and “top-down”
technologies driven by Web ontologies. Creating back-ends for the semantic Web is being transitioned
(bottom-up) from an arcane art into an
emerging Web application programming approach, as new open-source
technologies integrate well with traditional Web servers. At the same time,
new tools support ontology development and deployment (top-down), and
tens of thousands of OWL ontologies
are available for jumpstarting new
domain-modeling efforts. In addition,
approaches using rule-based reasoning modified for the Web have also
gained attention. 4 Engineering the future Web includes the design and use
of these emerging technologies, along
with how they differ from traditional
approaches to databases, in one case
creating back-ends for the semantic
Web, in the other new tools for ontol-ogy-based applications.
The semantic Web is a key emerging technology on the Web, but, also,
as we’ve discussed, there are different
opinions as to what it is best for and,
more important, what the macro effects might be. Our lack of a better understanding of how Web systems develop makes it difficult for us to know
the kinds of effects the technology will
produce at scale. What social consequences might there be from greater
public exposure and the sharing of information hidden away in databases?
A better understanding of how Web
systems move from the micro to the
macro scale would provide a better
understanding of how they could be
developed and what their potential societal effects might be.
conclusion
The Web is different from most previously studied systems in that it is
changing at a rate that may be of the
same order as, or perhaps greater
than, even the most knowledgeable
researcher’s ability to observe it. An
unavoidable fact is that the future
of human society is now inextricably
linked to the future of the Web. We
therefore have a duty to ensure that
future Web development makes the
world a better place. Corporations
have a responsibility to ensure that
the products and services they develop on the Web don’t produce side
effects that harm society, and governments and regulators have a responsibility to understand and anticipate
the consequences of the laws and policies they enact and enforce.
We cannot achieve these aims until we better understand the complex,
cross-disciplinary dynamics driving
development on the Web—the main
aim of Web science. Just as climate-change scientists have had to develop
ways to gather and analyze evidence
to prove or disprove theories about
the effect of human behavior on the
Earth’s climate, Web scientists need
new methodologies for gathering evidence and finding ways to anticipate
how human behavior will affect development of a system that is evolving at
such an amazing rate. We also must
consider what would happen to society if access to the Web was denied
to some or all and to raise awareness
among major corporations and governments that the consequences of
what appear to be relatively small decisions can profoundly affect society
in the future by affecting Web development today.
Computing plays a crucial role in
the Web science vision, and much of
what we know about the Web today
is based on our understanding of it
in a computational way. However, as
we’ve explored here, significant research must still be done to be able
to engineer future successful Web
applications. We must understand
the Web as a dynamic and changing
entity, exploring the emergent behaviors that arise from the “macro”