Diane-Michel.com

FOAF – is an ingenious little application that allows you to create an RDF about yourself, and your friends.

You can create your own RDF file simply by visiting the FOAF-a-Matic.
According to the FOAF Website:

FOAF-a-matic is a simple Javascript application that allows you to create a FOAF (“Friend-of-A-Friend”) description of yourself. You can read more about FOAF in Edd Dumbill’s “XML Watch: Finding friends with XML and RDF” article, at the FOAF homepage on RDFWeb, and also the FOAF vocabulary description.

In short though, FOAF is a way to describe yourself — your name, email address, and the people you’re friends with — using XML and RDF. This allows software to process these descriptions, perhaps as part of an automated search engine, to discover information about you and the communities of which you’re a member. FOAF has the potential to drive many new interesting developments in online communities. Ben Hammersely’s “Click to the Clique” article for the Guardian Unlimited website further explores these ideas.

Reference:
FOAF project. (2008). Getting started with FOAF. Retrieved April 19, 2008, from http://www.foaf-project.org/you/index.html

Think New Ideas: A Collaborative Research Community

ThinkNewIdeas.com is a community of forward thinking individuals interested in learning how to facilitate global collaboration. The group will explore and share how Web 2.0 technologies can be successfully used to create and enhance collaborative intelligence.

Mission
We plan to create efficient systems that facilitate knowledge sharing. Our ultimate goal is to enable faster discoveries within mankind’s critical areas of concern. Those areas including medical research, and other fundamental domains. It is our hope by creating more useful knowledge sharing systems, key research data and patterns can be more quickly identified and put to good use.

Research Focus

Our first research phase will focus on the most respected and influential thinkers within the fields of collective intelligence and the semantic Web. We will carefully select and review the highest levels of research possible. Our mission is to discover and distill the essential essence of ideas being worked on at present. We will then provide that information in a clear and highly usable state, and identify those elements of a successful collaborative system. Once the information is distilled we will begin a system prototype for our knowledge sharing environment.

About ThinkNewIdeas.com

Think New Ideas will be powered entirely by Web 2.0 technologies. In order to provide a comfortable and efficient working environment, we will always opt for those applications that are mainstream, and familiar to the majority of users.

Wikipedia: A case study in global collective intelligence

In order to understand collective intelligence more clearly, we need to study one of the strongest and prime examples of collective knowledge building on the Web.That is Wikipedia.Despite earlier questions regarding it’s usability, and overall accuracy, it is indeed an amazing accomplishment. The knowledge base of Wikipedia is growing exponentially.Itis a rich and exciting case study on anenormous scale.Within it lies a trove of information regarding howcollective intelligence is formed and valued by the community that creates it.

According to Wilkinson & Huberman, (2007):

“The online encyclopedia Wikipedia is an impressive example of a global collective intelligence at work. Since its inception in January 2001, Wikipedia has grown to encompass 6.40 million articles (by April 2007) in 250 languages generated from 236 million edits by 5.77 million contributors.

Both Bernardo A. Huberman, and Dennis Wilkinson are with HP and the reknown PARC (Palo Alto Research Center). They havereported substantial findings regarding Wikipedia in theApril 2007 issue of First Monday.

The content of Wikipedia is deemed useful and relevant by the user community at large is confirmed by its current position as11th most visited site on the Internet, serving an average of 16,536 requests per second.

The authors studied a correlation between the number of edits and article quality within 1,211 featured articles. Theresearchers concluded:

“We have shown that although Wikipedia is a complex system in which of millions of diverse editors collaborate in an unscheduled and virtually uncontrolled fashion, editing follows a very simple overall pattern. This pattern implies that a small number of articles, corresponding to topics of high relevance or visibility, accrete a disproportionately large number of edits. And, while large collaborations have been shown to fail in many contexts, Wikipedia article quality continues to increase, on average, as the number of collaborators and the number of edits increases. Thus, topics of high interest or relevance are naturally brought to the forefront of visibility and quality.”

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References

Wilkinson, D. & Huberman, B.A. (April 2007). Assessing the value of cooperation in Wikipedia. Retrieved January 8, 2007, from http://www.firstmonday.org/issues/issue12_4/wilkinson/#w1

Recently I have been researching my own collective intelligencesuite of applications.I have been systematically trying and experimenting numerous solutions. Here I will share with you some of my preliminary conclusions for corporate knowledge sharing applications:

WikiMedia
This is open source software, and is the same thing Wikipedia is built on.Anyone can implement it on an server, their own desktop, or even on a portable memory stick. As reported previously it is being used by manyFortune 500companies. Best of all, it’s free. However it does have some serious drawbacks which must be revealed.

WikiMedia Usability Problems
The look and feel of a WikiMedia product, is familiar to anyone who has used Wikipedia.The familiar interfacemakes for a happy beginning.But when one is face with the daunting task of posting, the comfort level quickly changes. For example,how do you post a page? This simple function is not made clear, and can be confounding to new users. The only way to post a page is to:

1.Search on a topic.
2.Discover the page doesn’t exist
3. Click on a linkonthe “not found” page.
4. Then begin writing.

WikiMedia Formatting Difficulties
Formatting anything other than straight text is difficult.WikiMedia doesn’t use pure HTML formatting, nor does it have a WYSIWYG editor.Individuals used to working within thesimplisticlyrefinedworld of blogs, may find thefunctionality, or lack thereof frustrating. Here is just a small taste of Wikipedia’s help section onformatting:

http://en.wikipedia.org/wiki/Wikipedia:How_to_edit_a_page

From a user’s viewpoint, (which really is the only thing that matters) this is not very good. WikiMedia’s help section is extremely difficult to follow, and too verbose. The first page is filled with promise.However upon digging deeper the writing is confused, and unfocused.

In evaluating collective intelligence systems, remember, the cornerstones of usability according to Nielsen(2003) are systems that are:

a. intuitive
b. easy to learn and remember
c. satisfying
d. efficient to work with
e. low error rate, and easy to recover if you do make a mistake.

I would like torecommend that the WikiMedia edit functions be made more user friendly and not written in such a way to turn off potential valuable contributors.

References:

Nielsen, J. (2003). Usability 101: definition and fundamentals. RetrievedJanuary 6, 2008, from http://www.useit.com/alertbox/20030825.html

Wikipedia.org (2007) Help. Retrieved January 6, 2008, from http://en.wikipedia.org/wiki/Help:Contents

Thomas Malone (2006) has defined collective intelligence as:
“Groups of individuals doing things collectively that seem intelligent”As reported in earlier postings, the MIT Center for Collective Intelligence is working deligently to answer the following:

“How can people and computers be connected so that collectively they act more intelligently than any individual, group, or computer has ever done before? “

Malone (2006) suggests that there are at least three types of research that need to be performed in order to answer the question.

1. Collecting interesting examples, and “systematically describing interesting cases of collective intelligence.”

2. Create new examples of collective intelligence environments.

3. Systematic studies and experiments.

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References
Malone, T. (2006). What is collective intelligence and what will we do about it? Retrieved January 6, 2008, from http://cci.mit.edu/about/MaloneLaunchRemarks.html

MIT’s Center for Collective Intelligence hopes to answer the question: “How can people and computers be connected so that collectively they act more intelligently than any individuals, groups, or computers have ever done before?”

Klein (2007) addressed the inherent problems of large-scale projects involving collective intelligence, inhis paper “Achieving collective intelligence via large-scale on-line argumentation“. He notedthat there are limits within current technologies for “large-scale deliberation.” He stated that while email, instant messaging, open forums, wikis and blogs do allow for individuals to interact globally there are “serious shortcomings from the standpoint of enhancing collective intelligence.”

The content captured by such tools is notorious for often being unsystematic, highly repetitive, and of highly variable quality. At its best (as with Wikipedia) a carefully nutured community process can be effective at capturing short descriptive articles about non-controversial topics, but all those approaches tend to break down when faced with the need to come up with coherent responses to complex problem that involve many competing perspectives. In such cases, discussions can be hijacked by a narrow set of “hot” issues, small voices can be lost, and achieving or identifying consensus becomes almost impossible.”

Klein (2007) stated that tools such as argumentation or rationale capture are a way to help enable brainstorming on controversial topics. They can help organize interaction by having users “structure their interactions into a network consisting of three kinds of entitites:

1. Issues (questions to be answered)
2. Options (alternative answers for a question)
3. Arguments (Claims that support or detract from some other statement.)

Challenges in Creating Large Scale Collective Intelligence
In search of creating large scale collective intelligence banks to solve critical pressing problems such as global warming, reacting to medical epidemics, etc, Klien (2007) has identified numerous design issues.

1. How do we avoid needless duplication?
Explanation: When there are many contributors working concurrently, and the sheer volume of entries grows, it is no longer possible to capture an argument structure within a single screen. It therefore is increasingly likely that someone will introduce an issue, option, or argument aht has already been posted by someone else.

2. How do participants converge on the key issues?
Explanation: Converging on key issues is unlikely to occur prior to posting into the system, as it might in a small-scale setting, especially a facilitated one. This suggests that tools and/or procedures should be made available to enable deliberations about the structure of the argumentation, and not just the content.3. Sheer volume of posters and entries increases exponentially in larger systems.

3. How do we ensure wide participation in entering/editing content?
Explanation: People are reluctant to replace, or modify work by someone else, even if the posting has serious failings. Reluctant to offer diverging opinions if the bulk of the existing arguments all seem to point in another direction.

4. How do we ensure that the argument is structured correctly?
Explanation: In an open system, we can expect that many of the participants will not be experts in how to structure argument maps effectively. This suggests that a large-scale argumentation system needs to support a continuum of formalization, allowing people to enter content in the form that they are comfortable with, be it extended prose or fully-structured argument maps.

5. How do we ensure succinct argumentation about options?
Explanation: We need some tools and/or procedures to summarize and even replace discussion threads with more succinct forms.

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Additional Resources:
Wearesmarterthanme.org: Creating a book of business best practices written collaboratively.

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References
Klein, M. (2007). Achieving collective intelligence via large-scale onl-line argumentation: MIT Center for Collective Intelligence.Retrieved January 1, 2008, from http://cci.mit.edu/publications/CCIwp2007-01.pdf

Collective Intelligence

Collective intelligencecan be defined as an organically grown bank of knowlege, which providesthe group with a totalsum of knowledge that is fargreater than what each individual member could produce or realize on their own. Information within collective intelligenceis organizednaturally according to each member’s interest and intention. Members of the group become smarter by collaboratively definingand organizing information to aid in each other’sunderstanding.Collective intelligence isone of thecornerstone concepts of Web 2.0 technologies.

PARC research is nowresearching and developing concepts related toaugmented social cognition. They define augmented social cognition as being:

Supported by systems, the enhancement of the ability of a group to remember, think, and reason; the system-supported construction of knowledge structures by a group. (Chi, 2007)

When PARC becomes focused on a particular dimension of research it behooves us to take notice. PARC has continually produced revolutionary technologies over the years that have completely changed the way we live and work. They created the ability for us tonetwork computers using the Ethernet, created graphical user interfaces (GUI), object oriented programming, and laser printing to name just a few of their breakthroughs. (PARC, 2007) PARC at this time has over “170 researchers from the physical, computer, biological, and social sciences (80% of whom hold doctoral degrees). “Employees come from 46 different countries, and include native speakers of virtually every major language. This diversity contributes to an environment in which collaboration is multi-dimensional, cutting across cultures, laboratories, and scientific disciplines.

Here is Ed Chi’s presentation on augmented social cognition within Google Talk. The video is entitled: “Social information foraging and collaboratve search: Augmented social cognition from social foraging to social sensemaking.” (Chi, 2007)

Additional Resources:
Social information foraging and collaborative search: PDF

Rememberance of things: Information foraging

References

Chi, Ed. (2007). Augmented social cognition. Retrieved December 31, 2007, from http://asc-parc.blogspot.com/2007/05/augmented-social-cognition.html

Chi, Ed. (2007). Social information foraging and collaborative search: Augmented social cognition from social foraging to social sensemaking. Retrieved December 31, 2007, from http://www.parc.com/research/projects/collaborativesystems/default.html

PARC Research. (2007). Retrieved December 31, 2007, from http://www.parc.com/research/projects/collaborativesystems/default.html

Collaborative Intelligence: Wikis for Business

In today’s top Fortune 500 companies, wikis are becoming thecollaborative tool of choicefor building vast store houses of corporate knowledge. Wikis are relativelyeasy touse and manage,and are surprisingly versatile in their functionality. Innovation and knowledge sharing cut right through typical bureaucratic red tape with amazing speed.Colleagues can communicate as a group much more efficiently without beinghampered by endless strings of meetings, or the literally hundreds ofemails that certain projects seem to breed among all the players involved. Communication can also be more easily facilitated withinon a global scale,utilizing a variety of language translation wikis. The constant creation and evolution of globally shared knowledgecreates a tremendouscatalyst for positive change. Business therefore canbuild and developinnovation at a speedthat simply has never been witnessed before. No longer justlone wolves in an organization, the team comes together taking the power of each member and combining it into a stronger force.

Who are using wikis for business?

According to Business Week’s Rachael King,(2007), one of the early adoptors was Intel.In 2005, Josh Bancroftneeded a way for engineers and staff to easily collaborate on internal projects. His solution was tocreate “Intelpedia”.Intelpedia is nowembraced by and utilized daily by thousands ofengineers and staffwithin the company. “Intelpedia now has amassed 5,000 pages of content and garnered 13.5 million page views. According to King (2007), corporations such as Sony, Xerox, Disney, and Microsoft alsohavebrought in wikis as a new and vitaltool for their business. King cites Andrew Mcafee, a professor at Harvard Business school, “If you did a comprehensive survey of Fortune 1,000 companies, you would probably find some sort of wiki in all of them.”

According to Bancroft (2007), corporate wikis can be used to fill a variety of needs including, “tracking industry news, setting meeting agendas, posting corporate policies, and even creating strategy documents.” Open-source software packages such as MediaWiki, and Twiki allow employees to create their own wikis without having to ask for technical IT help. Bancroft cites Ann Majchrzak and Christian Wagnerin their report for the Society of Information Management, that wikisare used by companies such as Motorola, Yahoo!, Amazon, Google, and Nokia. IBM has successfully implemented their wiki with over 125,000 active users. According to Bancroft (2007), “IBM assembled a worldwide community of 50 IBM experts in the fields of law, academia, economics, government, and technology to collaborate on the wiki. The result of that project is a collaboratively written intellectual-property manifesto that also serves as the foundation of IBM’s new patent policy.”

Wiki Security for Companies

Worried about security, who wouldn’t be? If internal corporate informationwas set free on the Internet, havoc would surely ensue.But, thanks to tighter security features, corporate wikis are less likely to face the pranks or vandalism that Wikipedia faces on a daily basis.Most corporate wikis also utilize intranets, as opposed to the Internet. Enterprise wikis software packages such as Atlassian, Socialtext, CustomerVision, and MindTouch provide the added security and access control features that are needed to keep internal company information private.

What’s next in the evolution of company wikis?

Engaging partners and customers seems to be the next step in the evolution of corporate wikis. For example, according to Bancroft (2007), Microsoft is utilizing a wiki to gather information from it’s partners around the worldwhile developing Visual Studio’s documentation. This not only encourages greater collaboration, but also allows Microsoft to enter into new markets where documentation has been previously hampered by local dialects.

How to handle exponential growth in a wiki

One caveat Bancroft (2007) reveals is that in time wikis can become unwieldy with too much information from too many sources. She cites Majchrzak in stating that there needs to be a “shaper” within the wiki environment, that is someone who helps “synthesize” the information so it is easier to read, more efficient, and therefore more usable by other members of the group.

Let’s finish off with a video by Barry Libert, entitled “7 Rules for Building a Business Community.”

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Corporate Wiki Software Solutions
1. Social Text
2. Confluence
3. Blogtronix
4. Clearspace
5. Mindtouch

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Additional Resources:Marshall School of Business:
Corporate Wiki Survey of Users – PDF ……………………………………………………………………………………………
References: King, R. (March, 2007). No rest for the wiki. Retrieved December 23, 2007, from http://www.businessweek.com/technology/content/mar2007/tc20070312_740461.htm

Rays of Collective Intelligence within the Web 2.0 environment:

Web 2.0 is aboutWeb usersconnecting together and collectively creating and sharing knowledge. It is an extremely powerful and exciting concept which I find particularly compelling.It is also the basic foundationofthe theory of constructivism. Driscoll (2000),stated that “Learners, are not empty vessels waiting to be filled, but rather active organisms seeking meaning. Knowledge is constructed by learners as they attempt to make sense of their experiences. Constructivists are interested in having learners identify and pursue their own learning goals.”

Ok, moving past the theory now. Tagging is an example of how individuals determine how their own content can be organized and found by others of like mind. There is a term for this called “folksonomy”. This term was apparently coined by Thomas Vander Wal (2005).His original definitionfor the term was that “Folksonomy is the result of personal free tagging of information and objects (anything with a URL) for one’s own retrieval. The tagging is done in a social environment (shared and open to others). The act of tagging is done by the person consuming the information.”

However much to the chagrin of the originator of the term… folksonomy seems to have morphed into a new meaning now days and is generally defined as: categorizing things through “collaborative, social tagging”.

Here is another definition from Wikipedia, which I generally try to shy away from as using a solid resource, but nevertheless here it is:Folksonomies are intended to make a body of information increasingly easy to search, discover, and navigate over time. A well-developed folksonomy is ideally accessible as a shared vocabulary that is both originated by, and familiar to, its primary users.

Examples of Tagging & OrganizingCollective Intelligence

43things.com for example, is a rather clever little Website which works by individual’s simply posting their goals they would like to accomplish and then adding their own keyword tags to those goals. Then individuals around the world interested in the same goals can therefore find one another and share their thoughts. This is an intriguing concept to be sure, and a terrific way to find and develop group support and a sense of belonging. Creating a sense of community and defining social presence, is by the way, key for eliminating isolation within online learning environments.

Del.icio.us is immensely popular social bookmarking Website. Basically it is a huge storehouse of all the member’s favorite bookmarks. Once you post your favorite bookmarks obviously they are accessible by any computer. One also utilizes tags to help organize and figure out that favorite bookmark you made last week.

Other examples of tagging sites which provide a new way for people to collectively share, and connect socially include Flickr. With no specific technical knowledge one can share their photographs of family, friends, adventures and projects. In doing so one can organize, search and share stories with others around the world. The best thing it is free and also doesn’t require specific technical knowledge. However there are upgrade packages available for power users.

Now to end our discussion, here is a clever video which helps to describe Web 2.0 by Mike Wesch. Dr. Wesch is a”cultural anthropologist and media ecologist exploring the impacts of new media on human interaction.”

References
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Driscoll. (2000). Psychology for learning for instruction. Allyn and Bacon: New York.

Del.icio.us. (2007) What is Del.icio.us? Retrieved November 25, 2007, from http://del.icio.us/about/43things.com

Flickr.com. (2007). About Flickr. Retrieved November 25, 2007, from
http://flickr.com/about/

43things.com. (2007) What do you want to do with your life? Retrieved November 25, 2007, from http://www.43things.com/about/view/learn_more

Wikipedia.com (2007). Folksonomy. Retrieved November 26, 2007 fromhttp://en.wikipedia.org/wiki/folksonomy.

Vanderwal.net (2005). Folksonomy definition and Wikipedia. Retrieved November 27, 2007 from http://www.vanderwal.net/random/entrysel.php?blog=1750.

Wesch, M. (2007). Web 2.0: The machine is us/ing us. Retrieved December 2, 2007, from http://www.youtube.com/watch?v=6gmP4nk0EOE&feature=related

Today we are awash in key worded blogs, RSS feeds and automated agents which seek out content based on our requirements, and bring it back to us on a regular basis.

But at the beginning of this millenium, the idea of pushing information to us rather than hunting for it was a brand new concept. In May 2001 Scientific American published an article entitled the “Semantic Web” by Tim Berners-Lee, James Hendler and Ora Lassila. At that time, I must confess to reading that particular article over and over, knowing intuitively somehow that it was very important for me to understand its message but not yet knowing how to apply it to my work in the Web. The most striking part of the article that left me spell bound was this quote:

Properly designed, the Semantic Web can assist the evolution of human knowledge as a whole.

This statement was quite profound and also foretelling. Read it again, absorb it, and you too will instinctively start to sense how important semantic applications really are.

Semantics according to Merriam-Webster (1986) originates from the Greek word semantikos. Semantics literally means: “significant, to signify, mean, or relating meaning in language. It is the study of meanings.” (Slightly digressing for your amusement, I find it ironic that I gathered this definition from my ancient print dictionary. I purchased it with the first $15.00 I ever made about a thousand years ago, during the age known as BTW = Before the Web.)

Ok back to learning…

The authors of the Semantic Web article; which include the originator of the World Wide Web, Tim Berners-Lee, spoke of how in the beginning Web pages were basically designed for people to read, but had no mechanism for computers to truly comprehend the content (Tim Berners-Lee et al., 2001 ). Of course, computers could scan Web page html to figure out the structure and layout of the page, and make some distinctions reading the header and title, but computer scripts couldn’t really understand or produce meaningful associations about the content on a Web page. In the beginning there was really no ability to actually “process the semantics.” Computers just DIDN’T GET IT yet. The capacity to know what they were processing or how to organize information based on the meaning just wasn’t there.

How does the Semantic Web work?

In order to make Web pages more palatable for machine consumption, we utilize markup languages like XML (eXtensible Markup Language) and RDF (Resource Description Framework) For example when a blog writer annotates their Web pages with tags, they may not realize it, but they are utilizing XML. Their tags are an indication of what the content is about.

XML is the way to exchange general purpose metadata. Therefore content can easily be detected by computer scripts which translate the tags and use them in a variety of ways, including organization. But even though XML can enable a user to give their own content definitions to documents it doesn’t quite get us to what the structures really mean. This is where RDF steps into play. RDF helps to express deeper meaning through a structure similar to the “subject, verb and object of an elementary sentence.” (Tim Berners-Lee et al., 2001)

Thanks a wonderfully written article by Altova (2007) we can gain some moreinsight on how the Semantic Web works.

In the Semantic Web data itself becomes part of the Web and is able to be processed independently of application, platform, or domain. This is in contrast to the World Wide Web as we know it today, which contains virtually boundless information in the form of documents. We can use computers to search for these documents, but they still have to be read and interpreted by humans before any useful information can be extrapolated. Computers can present you with information but can’t understand what the information is well enough to display the data that is most relevant in a given circumstance. The Semantic Web, on the other hand, is about having data as well as documents on the Web so that machines can process, transform, assemble, and even act on the data in useful ways.

Implementing the Semantic Web requires adding semantic metadata, or data that describes data, to information resources. This will allow machines to effectively process the data based on the semantic information that describes it. When there is enough semantic information associated with data, computers can make inferences about the data, i.e., understand what a data resource is and how it relates to other data.

The first step required for machines to understand data is to get that data into a uniform format, where, for instance, a field labeled “street” always has the same format and contains the same type of information, and so on. This type of functionality can be found today on Web sites that use forms that allow users to enter information and run a query, such as airline Web sites that allow visitors to search for and book flights based on a variety of criteria. However, considering the amount and variety of data available from different sources today, this method of data typing does not scale beyond very specific applications.

The next step towards the Semantic Web requires that data from multiple domains is classified based on its properties and its relationship with other data. This is where Semantic Web technologies such as RDF, RDFS, and OWL come in.

Resource Description Framework (RDF)
An official W3C recommendation, RDF is an XML-based standard for describing resources that exist on the Web, intranets, and extranets. RDF builds on existing XML and URI (Uniform Resource Identifier) technologies, using a URI to identify every resource, and using URIs to make statements about resources. RDF statements describe a resource (identified by a URI), the resource’s properties, and the values of those properties. RDF statements are often referred to as “triples” that consist of a subject, predicate, and object, which correspond to a resource (subject) a property (predicate), and a property value (object). Below is an example of an RDF statement in plain English:
[resource] [property] [value]
The secret agent is Niki Devgood
[subject] [predicate] [object]

Overall, RDFS is a simple vocabulary language for expressing the relationships between resources. Building upon RFDS is OWL, which is a much richer, more expressive vocabulary for defining Semantic Web ontologies.

Web Ontology Language (OWL)
OWL is a third W3C specification for creating Semantic Web applications. Building upon RDF and RDFS, OWL defines the types of relationships that can be expressed in RDF using an XML vocabulary to indicate the hierarchies and relationships between different resources. In fact, this is the very definition of “ontology” in the context of the Semantic Web: a schema that formally defines the hierarchies and relationships between different resources. Semantic Web ontologies consist of a taxonomy and a set of inference rules from which machines can make logical conclusions.

A taxonomy in this context is system of classification, such as the scientific kingdom/phylum/class/order/etc. system for classifying plants and animals that groups resources into classes and sub-classes based on their relationships and shared properties.

Since taxonomies (systems of classification) express the hierarchical relationships that exist between resources, we can use OWL to assign properties to classes of resources and allow their subclasses to inherit the same properties. OWL also utilizes the XML Schema datatypes and supports class axioms such as subClassOf, disjointWith, etc., and class descriptions such as unionOf, intersectionOf, etc. Many other advanced concepts are included in OWL, making it the richest standard ontology description language available today.

Semantic Web Present and Future
It’s important to note that implementation of RDF, OWL, and the Semantic Web as a whole will be a gradual process. Questions about what the Semantic Web is and how it can benefit businesses and individuals are similar to initial confusion about why we needed HTTP and the Web before “WWW” was a staple of our daily vocabulary. But considering how those technologies have proliferated, it’s likely that the Semantic Web vision is one that will be realized, even if it’s on a small scale initially.

Let’s close with a video of Tim Berners-Lee, Director of the World Wide Web Consortium describing his vision of the Semantic Web:

References
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Berners-Lee, T. Hendler, J., & Lassila, O. (May 2001). The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities.Retrieved December 2, 2007, from http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21&catID=2

Altova.com (2007). What is the Semantic Web? Retrieved December 2, 2007, from http://www.altova.com/semantic_web.html

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Want to learn more?

W3C Semantic Web
Semantic Web Case Studies
W3C Semantic Webon XML-Tim Berners-Lee
The Semantic Web Road Map – Tim Berners-Lee
Info Mesh: Semantic Web
HP – Intro to Semantic Web Technologies
RDF Tutorial
Semantic Web in Breadth
W3C Semantic Web Presentation 2002
The Semantic Web is closer than you think- O’Reilly
Ontologies Come of Age – Stanford University – Deborah L. McGuinness