10/22/2009
CIWI:
ALBERT,
FLAVELLE
& PINEAU
INTRODUCTION TO THE SEMANTIC WEB
A first look at the Semantic Web | Justin Pineau, Matt Albert, McElroy Flavelle
CIWI: Albert, Flavelle & Pineau
Introduction to the Semantic Web
ATTRIBUTION
All the content in this report, except for the Top Web Links section is from
Wikipedia, licensed under the Creative Commons Share-Alike 3.0 Unported License
(see below for an overview of both Wikipedia and the Creative Commons). The
following picture shows the full license below (it is also set up as a hyperlink to the
original web source for this license).
OUR CONTRIBUTION
We have attempted to add extra value to the content by structuring it in an easy to
read, business report format and to add an informative “Top Web Links” section. We
have also added an index to help you find what you are looking for. We hope you
find it useful and worth the $1 purchase price. We have prepared this report as part
of a MS Word 2007 assignment for BSYS 1000 – Computer Applications that we are
taking at the British Columbia Institute of Technology (BCIT). All proceeds will go to
student clubs within the School of Business at BCIT.
WIKIPEDA
Wikipedia is a multilingual, Web-based, free-content encyclopedia project based
mostly on anonymous contributions. The name “Wikipedia” is a portmanteau of the
words wiki (a type of collaborative Web site) and encyclopedia. Wikipedia’s articles
provide links to guide the user to related pages with additional information.
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Wikipedia is written collaboratively by an international (and mostly anonymous)
group of volunteers. Anyone with internet access can write and make changes to
Wikipedia articles. There are no requirements to provide one’s real name when
contributing; rather, each writer’s privacy is protected unless they choose to reveal
their identity themselves. Since its creation in 2001, Wikipedia has grown rapidly
into one of the largest reference web sites, attracting around 65 million visitors
monthly as of 2009. There are more than 75,000 active contributors working on
more than 14,000,000 articles in more than 260 languages. As of today, there are
3,062,069 articles in English. Every day, hundreds of thousands of visitors from
around the world collectively make tens of thousands of edits and create thousands
of new articles to augment the knowledge held by the Wikipedia encyclopedia. (See
also: Wikipedia:Statistics.)
CREATIVE COMMONS
Creative Commons (CC) is a non-profit organization devoted to expanding the range
of creative works available for others to build upon legally and to share. The
organization has released several copyright-licenses known as Creative Commons
licenses. These licenses allow creators to communicate which rights they reserve,
and which rights they waive for the benefit of recipients or other creators.
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TABLE OF CONTENTS
Our Contribution ............................................................................................................................ 2
Wikipeda ......................................................................................................................................... 2
Creative Commons ......................................................................................................................... 3
The Semantic Web .................................................................................................................................. 6
Purpose ............................................................................................................................................... 6
Relationship to the hypertext web ..................................................................................................... 8
Limitations of HTML........................................................................................................................ 8
Semantic Web solutions ................................................................................................................. 8
Similarities to Object Oriented Programming ................................................................................ 9
Skeptical reactions ............................................................................................................................ 10
Practical feasibility ........................................................................................................................ 10
An unrealized idea ........................................................................................................................ 10
Censorship and privacy ................................................................................................................. 10
Doubling output formats .............................................................................................................. 11
Need ............................................................................................................................................. 11
Components ...................................................................................................................................... 12
Challenges ......................................................................................................................................... 14
Vastness ........................................................................................................................................ 14
Vagueness ..................................................................................................................................... 14
Uncertainty ................................................................................................................................... 14
Inconsistency ................................................................................................................................ 14
Deceit ............................................................................................................................................ 14
Projects ............................................................................................................................................. 16
DBpedia ........................................................................................................................................ 16
FOAF ............................................................................................................................................. 16
GoodRelations for e-commerce ................................................................................................... 16
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SIOC .............................................................................................................................................. 17
Open GUID .................................................................................................................................... 17
SIMILE ........................................................................................................................................... 17
NextBio ......................................................................................................................................... 17
Linking Open Data ........................................................................................................................ 17
Insemtives ..................................................................................................................................... 18
OpenPSI ........................................................................................................................................ 18
Semandeks.................................................................................................................................... 18
Notification Services ......................................................................................................................... 19
Semantic Web Ping Service .......................................................................................................... 19
Piggy Bank..................................................................................................................................... 19
Index ................................................................................................................................................. 20
References ........................................................................................................................................ 22
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THE SEMANTIC WEB
The Semantic Web is an evolving development of the World Wide Web in which the
meaning (semantics) of information and services on the web is defined, making it
possible for the web to understand and satisfy the requests of people and machines
to use the web content. It derives from World Wide Web Consortium director Sir
Tim Berners-Lee's vision of the Web as a universal medium for data, information,
and knowledge exchange.
At its core, the semantic web comprises a set of design principles, collaborative
working groups, and a variety of enabling technologies. Some elements of the
semantic web are expressed as prospective future possibilities that are yet to be
implemented or realized. Other elements of the semantic web are expressed in
formal specifications. Some of these include Resource Description Framework (RDF),
a variety of data interchange formats (e.g. RDF/XML, N3, Turtle, N-Triples), and
notations such as RDF Schema (RDFS) and the Web Ontology Language (OWL), all of
which are intended to provide a formal description of concepts, terms, and
relationships within a given knowledge domain.
PURPOSE
Humans are capable of using the Web to carry out tasks such as finding the Finnish
word for "monkey", reserving a library book, and searching for a low price for a DVD.
However, a computer cannot accomplish the same tasks without human direction
because web pages are designed to be read by people, not machines. The semantic
web is a vision of information that is understandable by computers, so that they can
perform more of the tedious work involved in finding, sharing, and combining
information on the web.
Tim Berners-Lee originally expressed the vision of the semantic web as follows:
I have a dream for the Web [in which computers] become capable of
analyzing all the data on the Web – the content, links, and transactions
between people and computers. A ‘Semantic Web’, which should make this
possible, has yet to emerge, but when it does, the day-to-day mechanisms of
trade, bureaucracy and our daily lives will be handled by machines talking to
machines. The ‘intelligent agents’ people have touted for ages will finally
materialize.
– Tim Berners-Lee, 1999
Semantic publishing will benefit greatly from the semantic web. In particular, the
semantic web is expected to revolutionize scientific publishing, such as real-time
publishing and sharing of experimental data on the Internet. This simple but radical
idea is now being explored by W3C HCLS group's Scientific Publishing Task Force.
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Tim Berners-Lee has described the semantic web as a component of 'Web 3.0'.
“People keep asking what Web 3.0 is. I think maybe when you've got an
overlay of scalable vector graphics - everything rippling and folding and
looking misty - on Web 2.0 and access to a semantic Web integrated across a
huge space of data, you'll have access to an unbelievable data resource."
– Tim Berners-Lee, 2006
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RELATIONSHIP TO THE HYPERTEXT WEB
LIMITATIONS OF HTML
Many files on a typical computer can be loosely divided into documents and data.
Documents like mail messages, reports, and brochures are read by humans. Data,
like calendars, address books, playlists, and spreadsheets are presented using an
application program which lets them be viewed, searched and combined in many
ways.
Currently, the World Wide Web is based mainly on documents written in Hypertext
Markup Language (HTML), a markup convention that is used for coding a body of
text interspersed with multimedia objects such as images and interactive forms.
Metadata tags, for example:
<meta name="keywords" content="computing, computer studies, computer">
<meta name="description" content="Cheap widgets for sale">
<meta name="author" content="John Doe">
provide a method by which computers can categorise the content of web pages.
With HTML and a tool to render it (perhaps web browser software, perhaps another
user agent), one can create and present a page that lists items for sale. The HTML of
this catalog page can make simple, document-level assertions such as "this
document's title is 'Widget Superstore'", but there is no capability within the HTML
itself to assert unambiguously that, for example, item number X586172 is an Acme
Gizmo with a retail price of €199, or that it is a consumer product. Rather, HTML can
only say that the span of text "X586172" is something that should be positioned near
"Acme Gizmo" and "€ 199", etc. There is no way to say "this is a catalog" or even to
establish that "Acme Gizmo" is a kind of title or that "€ 199" is a price. There is also
no way to express that these pieces of information are bound together in describing
a discrete item, distinct from other items perhaps listed on the page.
Semantic HTML refers to the traditional HTML practice of markup following
intention, rather than specifying layout details directly. For example, the use of
<em> denoting "emphasis" rather than <i>, which specifies italics. Layout details are
left up to the browser, in combination with Cascading Style Sheets. But this practice
falls short of specifying the semantics of objects such as items for sale or prices.
Microformats represent unofficial attempts to extend HTML syntax to create
machine-readable semantic markup about objects such as retail stores and items for
sale.
SEMANTIC WEB SOLUTIONS
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The Semantic Web takes the solution further. It involves publishing in languages
specifically designed for data: Resource Description Framework (RDF), Web
Ontology Language (OWL), and Extensible Markup Language (XML). HTML describes
documents and the links between them. RDF, OWL, and XML, by contrast, can
describe arbitrary things such as people, meetings, or airplane parts. Tim BernersLee calls the resulting network of Linked Data the Giant Global Graph, in contrast to
the HTML-based World Wide Web.
These technologies are combined in order to provide descriptions that supplement
or replace the content of Web documents. Thus, content may manifest itself as
descriptive data stored in Web-accessible databases, or as markup within
documents (particularly, in Extensible HTML (XHTML) interspersed with XML, or,
more often, purely in XML, with layout or rendering cues stored separately). The
machine-readable descriptions enable content managers to add meaning to the
content, i.e., to describe the structure of the knowledge we have about that
content. In this way, a machine can process knowledge itself, instead of text, using
processes similar to human deductive reasoning and inference, thereby obtaining
more meaningful results and helping computers to perform automated information
gathering and research.
A number of authors highlight the similarities which the Semantic Web shares with
object-oriented programming (OOP). Both the semantic web and object-oriented
programming have classes with attributes and the concept of instances or objects.
Linked Data uses Dereferenceable Uniform Resource Identifiers in a manner similar
to the common programming concept of pointers or "object identifiers" in OOP.
Dereferenceable URIs can thus be used to access "data by reference". The Unified
Modeling Language is designed to communicate about object-oriented systems, and
can thus be used for both object-oriented programming and semantic web
development.
When the web was first being created in the late 1980s and early 1990s, it was done
using object-oriented programming languages such as Objective-C, Smalltalk and
CORBA. In the mid-1990s this development practice was furthered with the
announcement of the Enterprise Objects Framework, Portable Distributed Objects
and WebObjects all by NeXT, in addition to the Component Object Model released
by Microsoft. XML was then released in 1998, and RDF a year after in 1999.
SIMILARITIES TO OBJECT ORIENTED PROGRAMMING
Similarity to object oriented programming also came from two other routes: the first
was the development of the very knowledge-centric "Hyperdocument" systems by
Douglas Engelbart, and the second comes from the usage and development of the
Hypertext Transfer Protocol.
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SKEPTICAL REACTIONS
PRACTICAL FEASIBILITY
Critics (e.g. Which Semantic Web?) question the basic feasibility of a complete or
even partial fulfillment of the semantic web. Cory Doctorow's critique ("metacrap")
is from the perspective of human behavior and personal preferences. For example,
people lie: they may include spurious metadata into Web pages in an attempt to
mislead Semantic Web engines that naively assume the metadata's veracity. This
phenomenon was well-known with metatags that fooled the AltaVista ranking
algorithm into elevating the ranking of certain Web pages: the Google indexing
engine specifically looks for such attempts at manipulation. Peter Gärdenfors and
Timo Honkela point out that logic-based semantic web technologies cover only a
fraction of the relevant phenomena related to semantics.
Where semantic web technologies have found a greater degree of practical
adoption, it has tended to be among core specialized communities and organizations
for intra-company projects. The practical constraints toward adoption have
appeared less challenging where domain and scope is more limited than that of the
general public and the World-Wide Web.
AN UNREALIZED IDEA
The original 2001 Scientific American article by Berners-Lee described an expected
evolution of the existing Web to a Semantic Web. A complete evolution as
described by Berners-Lee has yet to occur. Indeed, a more recent article from
Berners-Lee and colleagues stated that: "This simple idea, however, remains largely
unrealized."
CENSORSHIP AND PRIVACY
Enthusiasm about the semantic web could be tempered by concerns regarding
censorship and privacy. For instance, text-analyzing techniques can now be easily
bypassed by using other words, metaphors for instance, or by using images in place
of words. An advanced implementation of the semantic web would make it much
easier for governments to control the viewing and creation of online information, as
this information would be much easier for an automated content-blocking machine
to understand. In addition, the issue has also been raised that, with the use of FOAF
files and geo location meta-data, there would be very little anonymity associated
with the authorship of articles on things such as a personal blog.
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DOUBLING OUTPUT FORMATS
Another criticism of the semantic web is that it would be much more timeconsuming to create and publish content because there would need to be two
formats for one piece of data: one for human viewing and one for machines.
However, many web applications in development are addressing this issue by
creating a machine-readable format upon the publishing of data or the request of a
machine for such data. The development of microformats has been one reaction to
this kind of criticism.
Specifications such as eRDF and RDFa allow arbitrary RDF data to be embedded in
HTML pages. The GRDDL (Gleaning Resource Descriptions from Dialects of Language)
mechanism allows existing material (including microformats) to be automatically
interpreted as RDF, so publishers only need to use a single format, such as HTML.
NEED
The idea of a 'semantic web' necessarily coming from some marking code other than
simple HTML is built on the assumption that it is not possible for a machine to
appropriately interpret code based on nothing but the order relationships of letters
and words. If this is not true, then it may be possible to build a 'semantic web' on
HTML alone, making a specially built 'semantic web' coding system unnecessary.
There are latent dynamic network models that can, under certain conditions, be
'trained' to appropriately 'learn' meaning based on order data, in the process
'learning' relationships with order (a kind of rudimentary working grammar). See for
example latent semantic analysis.
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COMPONENTS
The Semantic Web Stack. The semantic web comprises the standards and tools of
XML, XML Schema, RDF, RDF Schema and OWL that are organized in the Semantic
Web Stack. The OWL Web Ontology Language Overview describes the function and
relationship of each of these components of the semantic web:
XML provides an elemental syntax for content structure within documents, yet
associates no semantics with the meaning of the content contained within.
XML Schema is a language for providing and restricting the structure and content of
elements contained within XML documents.
RDF is a simple language for expressing data models, which refer to objects
("resources") and their relationships. An RDF-based model can be represented in
XML syntax.
RDF Schema is a vocabulary for describing properties and classes of RDF-based
resources, with semantics for generalized-hierarchies of such properties and classes.
OWL adds more vocabulary for describing properties and classes: among others,
relations between classes (e.g. disjointness), cardinality (e.g. "exactly one"), equality,
richer typing of properties, characteristics of properties (e.g. symmetry), and
enumerated classes.
SPARQL is a protocol and query language for semantic web data sources.
Current ongoing standardizations include:
Rule Interchange Format (RIF) as the Rule Layer of the Semantic Web Stack
Not yet fully realized layers include:
Unifying Logic and Proof layers are undergoing active research.
The intent is to enhance the usability and usefulness of the Web and its
interconnected resources through:
Servers which expose existing data systems using the RDF and SPARQL standards.
Many converters to RDF exist from different applications. Relational databases are
an important source. The semantic web server attaches to the existing system
without affecting its operation.
Documents "marked up" with semantic information (an extension of the HTML
<meta> tags used in today's Web pages to supply information for Web search
engines using web crawlers). This could be machine-understandable information
about the human-understandable content of the document (such as the creator,
title, description, etc., of the document) or it could be purely metadata representing
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a set of facts (such as resources and services elsewhere in the site). (Note that
anything that can be identified with a Uniform Resource Identifier (URI) can be
described, so the semantic web can reason about animals, people, places, ideas,
etc.) Semantic markup is often generated automatically, rather than manually.
Common metadata vocabularies (ontologies) and maps between vocabularies that
allow document creators to know how to mark up their documents so that agents
can use the information in the supplied metadata (so that Author in the sense of 'the
Author of the page' won't be confused with Author in the sense of a book that is the
subject of a book review).
Automated agents to perform tasks for users of the semantic web using this data
Web-based services (often with agents of their own) to supply information
specifically to agents (for example, a Trust service that an agent could ask if some
online store has a history of poor service or spamming)
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CHALLENGES
Some of the challenges for the Semantic Web include vastness, vagueness,
uncertainty, inconsistency and deceit. Automated reasoning systems will have to
deal with all of these issues in order to deliver on the promise of the Semantic Web.
VASTNESS
The World Wide Web contains at least 48 billion pages as of this writing (August 2,
2009). The SNOMED CT medical terminology ontology contains 370,000 class names,
and existing technology has not yet been able to eliminate all semantically
duplicated terms. Any automated reasoning system will have to deal with truly huge
inputs.
VAGUENESS
These are imprecise concepts like "young" or "tall". This arises from the vagueness
of user queries, of concepts represented by content providers, of matching query
terms to provider terms and of trying to combine different knowledge bases with
overlapping but subtly different concepts. Fuzzy logic is the most common technique
for dealing with vagueness.
UNCERTAINTY
These are precise concepts with uncertain values. For example, a patient might
present a set of symptoms which correspond to a number of different distinct
diagnoses each with a different probability. Probabilistic reasoning techniques are
generally employed to address uncertainty.
INCONSISTENCY
These are logical contradictions which will inevitably arise during the development
of large ontologies, and when ontologies from separate sources are combined.
Deductive reasoning fails catastrophically when faced with inconsistency, because
"anything follows from a contradiction". Defeasible reasoning and paraconsistent
reasoning are two techniques which can be employed to deal with inconsistency.
DECEIT
This is when the producer of the information is intentionally misleading the
consumer of the information. Cryptography techniques are currently utilized to
ameliorate this threat.
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This list of challenges is illustrative rather than exhaustive, and it focuses on the
challenges to the "unifying logic" and "proof" layers of the Semantic Web. The World
Wide Web Consortium (W3C) Incubator Group for Uncertainty Reasoning for the
World Wide Web (URW3-XG) final report lumps these problems together under the
single heading of "uncertainty". Many of the techniques mentioned here will require
extensions to the Web Ontology Language (OWL) for example to annotate
conditional probabilities. This is an area of active research.
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PROJECTS
This section provides some example projects and tools, but is very incomplete. The
choice of projects is somewhat arbitrary but may serve illustrative purposes. It is
also remarkable that in this early stage of the development of semantic web
technology, it is already possible to compile a list of hundreds of components that in
one way or another can be used in building or extending semantic webs.
DBPEDIA
DBpedia is an effort to publish structured data extracted from Wikipedia: the data is
published in RDF and made available on the Web for use under the GNU Free
Documentation License, thus allowing Semantic Web agents to provide inferencing
and advanced querying over the Wikipedia-derived dataset and facilitating
interlinking, re-use and extension in other data-sources.
FOAF
A popular application of the semantic web is Friend of a Friend (or FoaF), which uses
RDF to describe the relationships people have to other people and the "things"
around them. FOAF permits intelligent agents to make sense of the thousands of
connections people have with each other, their jobs and the items important to their
lives; connections that may or may not be enumerated in searches using traditional
web search engines. Because the connections are so vast in number, human
interpretation of the information may not be the best way of analyzing them.
FOAF is an example of how the Semantic Web attempts to make use of the
relationships within a social context.
GOODRELATIONS FOR E-COMMERCE
A huge potential for Semantic Web technologies lies in adding data structure and
typed links to the vast amount of offer data, product model features, and tendering
/ request for quotation data.
The GoodRelations ontology is a popular vocabulary for expressing product
information, prices, payment options, etc. It also allows expressing demand in a
straightforward fashion.
GoodRelations has been adopted by BestBuy, Yahoo, OpenLink Software, the Book
Mashup, and many others. According to Ping the Semantic Web, it is the second
most populated vocabulary on the Semantic Web.
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SIOC
The SIOC Project - Semantically-Interlinked Online Communities provides a
vocabulary of terms and relationships that model web data spaces. Examples of such
data spaces include, among others: discussion forums, weblogs, blogrolls / feed
subscriptions, mailing lists, shared bookmarks, image galleries.
OPEN GUID
Aimed at providing context for the Semantic Web, Open GUID maintains a global
Identifier repository for use in the linked web. Domain-specific Ontologies and
content publishers establish identity relationships with Open GUIDs.
SIMILE
Semantic Interoperability of Metadata and Information in unLike Environments
SIMILE is a joint project, conducted by the MIT Libraries and MIT CSAIL, which seeks
to enhance interoperability among digital assets, schemata/vocabularies/ontologies,
meta data, and services.
NEXTBIO
A database consolidating high-throughput life sciences experimental data tagged
and connected via biomedical ontologies. Nextbio is accessible via a search engine
interface. Researchers can contribute their findings for incorporation to the
database. The database currently supports gene or protein expression data and is
steadily expanding to support other biological data types.
LINKING OPEN DATA
The Linking Open Data project is a community-led effort to create openly accessible,
and interlinked, RDF Data on the Web. The data in question takes the form of RDF
Data Sets drawn from a broad collection of data sources. There is a focus on the
Linked Data style of publishing RDF on the Web. See #Triplify for a small plugin to
expose data from your Web application as Linked Data.
The project is one of several sponsored by the W3C's Semantic Web Education &
Outreach Interest Group (SWEO).
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INSEMTIVES
Insemtives is a European Seventh Framework Program (FP7) -funded project with
the objective to bridge the gap between human and computational intelligence for
the semantic content authoring.
OPENPSI
OpenPSI the (OpenPSI project) is a community effort to create UK government
linked data service that supports research. It is a collaboration between the
University of Southampton and the UK government, lead by OPSI at the National
Archive and is supported by JISC funding.
SEMANDEKS
Semandeksis an attempt to build a semantic index with community effort using a
much user-friendly UI.
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NOTIFICATION SERVICES
SEMANTIC WEB PING SERVICE
The Semantic Web Ping Service is a notification service for the semantic web that
tracks the creation and modification of RDF-based data sources on the Web. It
provides Web Services for loosely coupled monitoring of RDF data. In addition, it
provides a breakdown of RDF data sources tracked by vocabulary that includes:
SIOC, FOAF, DOAP, RDFS, and OWL.
In the latest version, which is 3.0, Semantic Web Ping Service has the ability to
validate RDF resources. There is a simplified pings list export system that was
developed in this version. The pinging infrastructure has gained substantial speed.
One of the reasons for the improvement is that the database was switched from
MySQL to Virtuoso.
In addition, the user interface has been updated. New statistics are available with
this edition as well. The ability to provide all statistics about namespaces and all
statistics about types has been added. A nice feature to provide the list of
namespaces used to describe entities in RDF. All statistics about types gives the
number of typed entities defined in each RDF document known by Ping The
Semantic Web.
PIGGY BANK
Another freely downloadable tool is the Piggy Bank plug-in to FirefoxPiggy Bank
works by extracting or translating web scripts into RDF information and storing this
information on the user’s computer. This information can then be retrieved
independently of the original context and used in other contexts, for example by
using Google Maps to display information. Piggy Bank works with a new service,
Semantic Bank, which combines the idea of tagging information with the new web
languages. Piggy Bank was developed by the Simile Project, which also provides
RDFizers, tools that can be used to translate specific types of information, for
example weather reports for US zip codes, into RDF. Efforts like these could ease a
potentially troublesome transition between the web of today and its semantic
successor.
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INDEX
C
Creative Commons (CC) · 21
M
T
Microformats · 8
The GoodRelations ontology ·
16
The Linking Open Data project
· 17
The Semantic Web Ping
Service · 18
The SIOC Project · 16
Tim Berners-Lee · 5, 6, 8
D
DBpedia · 15
N
Nextbio · 17
F
Friend of a Friend (or FoaF) ·
15
O
Open GUID · 16
OpenPSI · 18
Wikipedia · 21
H
Hypertext Markup Language
(HTML) · 7
I
Insemtives · 17
W
P
Piggy Bank · 19
S
Semandeksis · 18
SIMILE · 17
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Top Web
Source
What is
Semantic
web?
Semantic
Web
Activity
Facts about
Semantic
Web
Programmi
ng the
Semantic
Web
Semantic
Web for
Dummies
Ontologies
Semantic
Web
Services
First
Semantic
Web App
Semantic
Web Apps
Semantic
Web Acid
Test
Source
URL
Semant
ic
web.or
g
W3C
http://semanticweb.org/wiki/Main_Page
Free
Base
http://www.freebase.com/view/en/semantic_web
Free
Base
http://www.freebase.com/view/guid/9202a8c04000641f800000000c64629
1
Luca
http://luca.wordpress.com/2008/02/03/semantic-web-for-dummies/
Obitko
Stanfor
d
www.obitko.com/tutorials/ontologies-semantic-web/
http://www-ksl.stanford.edu/people/sam/ieee01.pdf
Read
Write
Web
CS
http://www.readwriteweb.com/archives/twine_first_mainstream_semantic
_web_app.php
Ian
Davis
http://iandavis.com/blog/2009/03/the-semantic-web-acid-test
http://www.w3.org/2001/sw/
http://www.cs.vu.nl/~mcaklein/papers/NLDB02.pdf
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REFERENCES
1. Berners-Lee, Tim; James Hendler and Ora Lassila (May 17, 2001). "The Semantic
Web". Scientific American Magazine. http://www.sciam.com/article.cfm?id=thesemantic-web&print=true. Retrieved 2008-03-26.
2. ^ a b "W3C Semantic Web Frequently Asked Questions". W3C.
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3. ^ Herman, Ivan (2008-03-07). "Semantic Web Activity Statement". W3C.
http://www.w3.org/2001/sw/Activity.html. Retrieved 2008-03-13.
4. ^ "Design Issues". W3C. http://www.w3.org/DesignIssues/. Retrieved 2008-03-13.
5. ^ Herman, Ivan (2008-03-12). "W3C Semantic Web Activity". W3C.
http://www.w3.org/2001/sw/. Retrieved 2008-03-13.
6. ^ Berners-Lee, Tim; Fischetti, Mark (1999). Weaving the Web. HarperSanFrancisco.
chapter 12. ISBN 9780062515872.
7. ^ Victoria Shannon (2006-06-26). "A 'more revolutionary' Web". International
Herald Tribune. http://www.iht.com/articles/2006/05/23/business/web.php.
Retrieved 2006-05-24.
8. ^ Artem Chebotko and Shiyong Lu, "Querying the Semantic Web: An Efficient
Approach Using Relational Databases", LAP Lambert Academic Publishing, ISBN 9783-8383-0264-5, 2009.
9. ^ Knublauch, Holger; Oberle, Daniel; Tetlow, Phil; Evan (2006-03-09). "A Semantic
Web Primer for Object-Oriented Software Developers". W3C.
http://www.w3.org/2001/sw/BestPractices/SE/ODSD/. Retrieved 2008-07-30.
10. ^ Connolly, Daniel (2002-08-13). "An Evaluation of the World Wide Web with
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11. ^ Engelbart, Douglas (1990). "Knowledge-Domain Interoperability and an Open
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http://www.bootstrap.org/augdocs/augment-132082.htm. Retrieved 2008-07-30.
12. ^ Connolly, Dan. "From the editor... WebApps". W3C.
http://www.w3.org/People/Connolly/9703-web-apps-essay.html. Retrieved 200807-30.
13. ^ Gärdenfors, Peter (2004), "How to make the Semantic Web more semantic",
Formal Ontology in Information Systems: proceedings of the third international
conference (FOIS-2004) (IOS Press): p. 17-34
14. ^ Timo Honkela, Ville Könönen, Tiina Lindh-Knuutila and Mari-Sanna Paukkeri
(2008), "Simulating processes of concept formation and communication", Journal of
Economic Methodology,
http://www.informaworld.com/smpp/content~content=a903999101
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http://www.w3.org/2007/Talks/0424-Stavanger-IH/Slides.pdf. Retrieved 2007-0726.
16. ^ Berners-Lee, Tim (2001-05-01). "The Semantic Web". Scientific American.
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Introduction to the Semantic Web
17. ^ Nigel Shadbolt, Wendy Hall, Tim Berners-Lee (2006). "The Semantic Web
Revisited". IEEE Intelligent Systems.
http://eprints.ecs.soton.ac.uk/12614/1/Semantic_Web_Revisted.pdf. Retrieved
2007-04-13.
18. ^ Lukasiewicz, Thomas; Umberto Straccia. "Managing uncertainty and vagueness in
description logics for the Semantic Web".
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B758F-4SPSPKW1&_user=147018&_coverDate=11%2F30%2F2008&_rdoc=1&_fmt=&_orig=search&
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19. ^ See, for instance: Bergman, Michael K.. "Sweet Tools". AI3; Adaptive Information,
Adaptive Innovation, Adaptive Infrastructure.
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20. ^ "Open GUID". OpenGUID.net. http://openguid.net/. Retrieved 2008-10-19.
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