Collective Knowledge Systems: Where the Social Web meets the Semantic Web - 1 views
tomgruber.org/...CollectiveKnowledgeSystems.htm
3-étoiles Activité-A Folksonomies_Semantic_Collectivities Web2_To_Web3
shared by Harry Sahyoun on 26 Jul 13
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Collective Knowledge Systems: Where the Social Web meets the Semantic Web
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What can happen if we combine the best ideas from the Social Web and Semantic Web?
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The Vision of Collective Intelligence
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The Social Web is represented by a class of web sites and applications in which user participation is the primary driver of value.
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Collective intelligence is a grand vision, one to which I subscribe. However, I would call the current state of the Social Web something else: collected intelligence. That is, the value of these user contributions is in their being collected together and aggregated into community- or domain-specific sites
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The grand challenge is to boost the collective IQ of organizations and of society
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With the rise of the Social Web, we now have millions of humans offering their knowledge online, which means that the information is stored, searchable, and easily shared. The challenge for the next generation of the Social and Semantic Webs is to find the right match between what is put online and methods for doing useful reasoning with the data. True collective intelligence can emerge if the data collected from all those people is aggregated and recombined to create new knowledge and new ways of learning that individual humans cannot do by themselves.
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Technology can augment the discovery and creation of knowledge. For instance, some drug discovery approaches embody a system for learning from models and data that are extracted from published papers and associated datasets. By assembling large databases of known entities relevant to human biology, researchers can run computations that generate and test hypotheses about possible new therapeutic agents.
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The first approach is to expose the structured data that already underlies the unstructured web pages. An obvious technique is for the site builder, who is generating unstructured web pages from a database, to expose the structured data in those pages using standard formats.
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the second approach, to extract structured data from unstructured user contributions [2] [28] [39] . It is possible to do a reasonable job at identifying people, companies, and other entities with proper names, products, instances of relations you are interested in (e.g., person joining a company) [1] [7] , or instances of questions being asked [24] . There also techniques for pulling out candidates to use as classes and relations, although these are a bit noisier than the directed pattern matching algorithms [8] [23] [31] [32] [36] [38] [42]
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Tomorrow, the web will be understood as an active human-computer system, and we will learn by telling it what we are interested in, asking it what we collectively know, and using it to apply our collective knowledge to address our collective needs.
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The third approach is to capture structured data on the way into the system. The straightforward technique is to give users tools for structuring their data, such as ways of adding structured fields and making class hierarchies.
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In a sense, the TagCommons project is attempting to create a platform for interoperability of social web data on the Semantic Web that is akin to the "mash-up" ecology that is celebrated in Web 2.0.
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An example of how a system might apply some of these ideas is RealTravel. RealTravel is an example of "Web 2.0 for travel". It attracts travelers to share their experiences: sharing their itineraries, stories, photographs, where they stayed, what they did, and their recommendations for fellow travelers. Writers think of RealTravel as a great platform to share their experiences -- a blog site that caters to this domain. People who are planning travel use the site as a source of information to research their trip,
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The collection of tags for a site is called the folksonomy, which is useful data about collective interests.
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like many Web 2.0 sites, combines these structured dimensions to order the unstructured content. For example, one can find all the travel blogs about diving, sorted by rating. In fact, the site combines all of the structured dimensions into a matrix, which offers the user a way to "pivot browse" along any dimension from any point in the matrix.
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This paper argues that the Social Web and the Semantic Web should be combined, and that collective knowledge systems are the "killer applications" of this integration. The keys to getting the most from collective knowledge systems, toward true collective intelligence, are tightly integrating user-contributed content and machine-gathered data, and harvesting the knowledge from this combination of unstructured and structured information.
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Structured and unstructured, formal and informal -- these are not new dimensions. They are typically considered poles of a continuum.
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We are beginning to see companies launching services under the banner of Web 3.0 [25] that aim explicitly at collective intelligence. For instance, MetaWeb [35] is collecting a commons of integrated, structured data in a social web manner, and Radar Networks [25] is applying semantic web technologies to enrich the applications and data of the social web.
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The other major area where Semantic Web can help achieve the vision of collective intelligence is in the area of interoperability. If the world's knowledge is to be found on the Web, then we should be able to use it to answer questions, retrieve facts, solve problems, and explore possibilities.
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Technology can augment the discovery and creation of knowledge. For instance, some drug discovery approaches embody a system for learning from models and data that are extracted from published papers and associated datasets. By assembling large databases of known entities relevant to human biology, researchers can run computations that generate and test hypotheses about possible new therapeutic agents
