La evolución de la Web hacia lo 3.0 está en camino.Link relacionado: Semantic Wave 2008 Report: Industry Roadmap to Web 3.0 and Multibillion Dollar Market Opportunities
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Slide 1: Where the Social Web Meets the Semantic Web Tom Gruber RealTravel.com tomgruber.org
Slide 2: Doug Engelbart, 1968 “The grand challenge is to boost the collective IQ of organizations and of society. ”
Slide 3: Tim Berners-Lee, 2001 “The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” Scientific American, May 2001
Slide 4: Tim O’Reilly, 2006, on Web 2.0 “The central principle behind the success of the giants born in the Web 1.0 era who have survived to lead the Web 2.0 era appears to be this, that they have embraced the power of the web to harness collective intelligence”
Slide 5: Web 2.0 is about The Social Web “Web 2.0 Is Much More About A Change In People and Society Than Technology” -Dion Hinchcliffe, tech blogger 1 billion people connect to the Internet 100 million web sites over a third of adults in US have contributed content to the public Internet. – 18% of adults over 65 source: Pew Internet and American Life Project via futureexpolporation.net diagram source: http://web2.wsj2.com/
Slide 6: Tim Berners-Lee, 5 days ago “The Web isn’t about what you can do with computers. It’s people and, yes, they are connected by computers. But computer science, as the study of what happens in a computer, doesn’t tell you about what happens on the Web.” NY Times, Nov 2, 2006
Slide 7: But what is “collective intelligence” in the social web sense? intelligent collection? collaborative bookmarking, searching “database of intentions” clicking, rating, tagging, buying what we all know but hadn’t got around to saying in public before blogs, wikis, discussion lists “database of intentions” – Tim O’Reilly
Slide 8: the wisdom of clouds? http://flickr.com/photos/tags/
Slide 9: “Collective Knowledge” Systems The capacity to provide useful information based on human contributions which gets better as more people participate. typically mix of structured, machine-readable data and unstructured data from human input
Slide 10: Collective Knowledge is Real FAQ-o-Sphere – self service Q&A forums Citizen Journalism – “We the Media” Product reviews for gadgets and hotels Collaborative filtering for books and music Amateur Academia
Slide 11: What about the Semantic Web?
Slide 12: Roles for Technology capturing everything storing everything distributing everything enabling many-to-many communication creating value from the data
Slide 13: Potential Roles for Semantic Net Technology: Two examples Composing and integrating user- contributed data across applications example: tagging data Creating aggregate value from a mix of structured and unstructured data example: blogging data
Slide 14: “Ontology is overrated.” — Clay Shirky “[tags] are a radical break with previous categorization strategies” hierarchical, centrally controlled, taxonomic categorization has serious limitations e.g., Dewey Decimal System free-form, massively distributed tagging is resilient against several of these limitations http://shirky.com/writings/ontology_overrated.html
Slide 15: But… ontologies aren’t taxonomies they are for sharing, not finding they enable cross-application aggregation and value-added services
Slide 16: Ontology of Folksonomy What would it look like to formalize an ontology for tag data? Functional Purpose: applications that use tag data from multiple systems tag search across multiple sites collaboratively filtered search “find things using tags my buddies say match those tags” combine tags with structured query “find all hotels in Spain tagged with “romantic” http://tomgruber.org/writing/ontology-of-folksonomy.htm
Slide 17: Example: formal match, semantic mismatch System A says a tag is a property of a document. System B says a tag is an assertion by an individual with an identity. Does it mean anything to combine the tag data from these two systems? “Precision without accuracy” “Statistical fantasy”
Slide 18: Engineering the tag ontology Working with tag community, identify core and non core agreements Use the process of ontology engineering to surface issues that need clarification Couple a proposed ontology with reference implementations or hosted APIs
Slide 19: Core concepts Term – a word or phrase that is recognizable by people and computers Document – a thing to be tagged, identifiable by a URI or a similar naming service Tagger – someone or thing doing the tagging, such as the user of an application Tagged – the assertion by Tagger that Document should be tagged with Term
Slide 20: Issues raised by ontological engineering is term identity invariant over case, whitespace, punctuation? are documents one-to-one with URI identities? (are alias URLs possible?) can tagging be asserted without human taggers? negation of tag assertions? tag polarity – “voting” for an assertion tag spaces – is the scope of tagging data a user community, application, namespace, or database?
Slide 21: Volunteers Needed Applications that need shared tagging data Tag spaces and sources of tag data Ontology engineers who can run an open source-style project http://www.tagcommons.org
Slide 22: Role 2: Creating aggregate value from structured data
Slide 23: Role 2: Creating aggregate value from structured data Problem: In a collective knowledge system, the value of the aggregate content must be more than sum of parts Approach: Create aggregate value by integrating user contributions of unstructured content with structured data.
Slide 24: Example: Collective Knowledge about Travel RealTravel attracts people to write about their travels, sharing stories, photos, etc. Travel researchers get the value of all experiences relevant to their target destinations. http://tomgruber.org/technology/realtravel.htm
Slide 26: Pivot Browsing – surfing unstructured content along structured lines Structured data provides dimensions of a hypercube location author type date quality rating Travel researchers browse along any dimension. The key structured data is the destination hierarchy Contributors place their content into the destination hierarchy, and the other dimensions are automatic.
Slide 27: Destination data is the backbone Group stories together by destination Aggregate cities to states to countries, etc Inherit locations down to photos From destinations infer geocoordinates, which drive dynamic route maps Destinations must map to external content sources (travel guides) Destinations must map to targeted advertising
Slide 30: Contextual Tagging Tags are bottom up labels, words without context. A structured data framework provides context. Combining context and tags creates insightful slices through the aggregate content.
Slide 33: Problems that Semantic Web could have helped No standard source of structured destination data for the world or way to map among alternative hierarchies Integrating with other destination-based sites is expensive e.g. travel guides No standard collection of travel tags or way to share RealTravel’s folksonomy Integrating with other tagging sites is ad hoc need a matching / translation service
Slide 34: Resources That Did Help Open source software or free services powerful databases fancy UI libraries search engines usage analytics Open APIs from Google (maps) and Flickr (photos) Commercially available geocoordinate data and services
Slide 35: (Semantic Web) projects that could help collective knowledge systems Tag spaces and tag data sharing World destination hierarchy and other geocoordinate databases Portable user identity and reputation Site-independent rating and filtering Alternatives to Google-style search __audience contributions here___
Slide 36: Activities already going Semantically-Interlinked Online Communities (SIOC) http://sioc-project.org/ semantic wiki projects http://wiki.ontoworld.org/wiki/Category:Semanti __audience contributions here___
Slide 37: Challenges for our Community How to get knowledge from all those intelligent people on the Internet How to give everyone the benefit of everyone else’s experience How to leverage and contribute to the ecosystem that has created today’s web.
Slide 38: What will the future look like? Social Web Social + Semantic Web