Escola de Redes

Pierre Lévy: “La inteligencia colectiva, nuestra más grande riqueza”

El 23 de junio el diario francés Le Monde publicó una interesante entrevista al sociólogo y filósofo tunesino Pierre Lévy en la cual expone ideas concisas sobre el desarrollo e implicaciones de la inteligencia colectiva en la sociedad a través de un medio como internet; además comenta a grandes rasgos sobre su actual proyecto de investigación, el IEML (Information Economy Meta Language, una lengua artificial concebida para ser simultáneamente manipulada por los ordenadores y capaz de expresar los matices semánticos y pragmáticos de las lenguas naturales.

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Comentário de Clara Pelaez Alvarez em 25 novembro 2009 às 18:27
Comentário de Clara Pelaez Alvarez em 25 novembro 2009 às 17:39
Andei pesquisando esse assunto há um tempo atrás. Cheguei a baixar um software Zipf 2.0, mas ainda não tive tempo de estudá-lo!

What is the Semantic Web?

Currently the focus of a W3C working group, the Semantic Web vision was conceived by Tim Berners-Lee, the inventor of the World Wide Web. The World Wide Web changed the way we communicate, the way we do business, the way we seek information and entertainment – the very way most of us live our daily lives. Calling it the next step in Web evolution, Berners-Lee defines the Semantic Web as “a web of data that can be processed directly and indirectly by machines.”

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.

Imagine this scenario. You’re a software consultant and have just received a new project. You’re to create a series of SOAP-based Web services for one of your biggest clients. First, you need to learn a bit about SOAP, so you search for the term using your favorite search engine. Unfortunately, the results you’re presented with are hardly helpful. There are listings for dish detergents, facial soaps, and even soap operas mixed into the results. Only after sifting through multiple listings and reading through the linked pages are you able to find information about the W3C’s SOAP specifications.

Because of the different semantic associations of the word “soap,” the results you receive are varied in relevance, and you still have to do a lot of work to find the information you’re looking for. However, in a Semantic Web-enabled environment, you could use a Semantic Web agent to search the Web for “SOAP” where SOAP is a type of technology specification used in Web services. This time, the results of your search will be relevant. Your Semantic Web agent can also search your corporate network for the SOAP specification and discover if your colleagues have completed similar projects or have posted SOAP-related research on the network. Based on the semantic information available for SOAP, your agent also presents you with a list of related technologies. Now you know that WSDL, XML, and URI are all technologies related to SOAP, and that you’ll need to do some research on them, too, before beginning your project. Armed with the information returned by your Semantic Web agent, you read the related technology specifications and send emails to the colleagues who have made SOAP-related materials available on the network to ask for their input before starting your new project.

Now, fast forward a few years. You’re still happily employed as a software consultant, and today you’re taking a working lunch with one of your biggest clients. Her company has an emergency project at its San Francisco branch for which they need you to consult for two weeks, and she asks you to get to San Francisco as soon as possible to begin work. You take out your hand held computer, activate its Semantic Web agent, and instruct it to book a non-stop flight to San Francisco that leaves before 10 AM the next day. You want an aisle seat if it’s available. Once your agent finds an acceptable flight with an available aisle seat, it books it using your American Express card and assigns the charges to your client’s account in your accounting application. It also warns you that you’ll be missing a dentist appointment back home during your trip and adds a note to your calendar reminding you to reschedule. Next, you specify that you want a car service to the client’s site, so your agent scans the availability of limos with “very good” or higher service ratings and books an appointment to have you picked up 30 minutes after your flight lands. Your agent also books you at your favorite hotel in San Francisco, automatically securing the lowest rate using your rewards card number. Finally, the agent updates your calendar and your manager’s calendar with your trip information and prints out your confirmation documents back at your office.

With just a few clicks your Semantic Web agent found and booked your flight, hotel, and car service, then updated your accounting system and calendars automatically. It even compared your itinerary to your calendar and detected the scheduling conflict with your dentist appointment. To do all this, the agent had to find, interpret, combine, and act on information from multiple sources. This example, of course, is a long-term vision for applying the Semantic Web. It’s one that may or may not come to fruition, and only the future will tell. However, the vision itself is important for understanding the potential of Semantic Web technologies.

Considering the two examples above, the list of scenarios that could potentially benefit from Semantic Web technologies as they continue to evolve is limited only by the imagination. Think of the possibilities opened to everything from crime investigation, scientific research, and literary analysis – to shopping, finding long-lost friends, and vacation planning – when computers can find, present, and act on data in a meaningful way.

The Semantic Web agent does not include artificial intelligence – rather, it relies on structured sets of information and inference rules that allow it to “understand” the relationship between different data resources. The computer doesn’t really understand information the way a human can, but it has enough information to make logical connections and decisions.
Comentário de Carlos Boyle em 25 novembro 2009 às 15:35
Siii. No entiendo nada de eso de web semántica, es más, tengo un plug in en mi Firefor para la web semántica y no se que hace. Jeje
Comentário de Augusto de Franco em 25 novembro 2009 às 15:29
Boa Boyle! Levy estará no conosco na CIRS. Vamos explorá-lo bastante sobre a tal web semântica.

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