Information &Knowledge ManagementDiscussion of “Collective Knowledge Systems:When the Social Web Meets the Semantic Web” from Tom Gruber (TomGruber.org) Marielba Zacarias Prof. Auxiliar DEEI FCT I, Gab 2.69, Ext. 7749 firstname.lastname@example.org
SummaryThe Vision of Collective IntelligenceCollective Knowledge SystemsThe Role of the Semantic Web Augmenting User-Contributed Data with Structured Data Enabling Data Sharing and Computation Across ApplicationsExample Collective Knowledge System for Travel
The vision of Collective IntelligenceWeb 2.0 (Social Web) Class of web sites and applications in which user participation is the main driver of value Wikipedia, MySpace, YouTube, FIicker, Del.icio.us, Facebook, Technorati, etc., Blogger, WordPress
The vision ofCollective Intelligence web 1.0 web 2.0
The vision of Collective IntelligenceHarnessing Collective Intelligence Hyperlinking works as brain synapsis Yahoo!’s role as a portal of net users’ collective work Google’s PageRank search exploits web structure rather than just doc characteristics eBays’ product is the collective activity of its users Amazon has made a science of user engagement Flicker & Del.icio.us pioneered folksonomies Wikipedia based on the idea that any user may edit any entry Collaborative spam ﬁltering like Cloudmark Greatest internet successes driven by viral marketing Internet infrastructure (php, apache, mysql, python) mostly based on peer-production of open source software
The vision of Collective IntelligenceCollective Intelligence or Wisdom of the Crowds Value created by collective writing articles in wikipedia, sharing tagged photos in ﬂicker, sharing bookmarks in del.icio.us or streaming their personal blogs in the open space called the blogosphereUnmatched potential for knowledge sharingCollected intelligence But not collective intelligence No emergence of new levels of undersanding of knowledge
The vision of Collective IntelligenceCollective intelligence has been goal of severalvisionariesGrand challenge is to boost the collective IQ oforganizations and societyhuman-machine system for collecting knowledge for learning evolving technology for collective learning humans and machines actively contribute doing what they do best
The vision of Collective IntelligenceTim Berners-Lee inventor of the semantic web Semantic web is an extension of social web in which information is given precise meaning better enabling people and computers to cooperate
The vision of Collective IntelligenceThe key is the synergy between humans and machinesWhat kind of synergy? People are producers and customers knowledge sources have real world problems and interests learn/create knowledge communicating with each other Machines are enablers store & remember data search & combine data draw mathematical & logical inferencex
The Vision of Collective IntelligenceWith the rise of the social web we have now millions ofhumans offering their knowledge online i.e.The information is stored, searchable and easily sharedChallenge: match between what is put online andmethods for doing useful reasoning with dataTrue collective knowledge emerges if the knowledgecollected from all those people is aggregated orrecombined to create new knowledge or new ways oflearning
Collective Knowledge Systems human-machines systems in which machines enable the collection and harvesting of large amounts of human- generated knowledge
Collective knowledge systems the faq-o-sphere social system supported by ICT which generates self-service problem solving discussions in the internet product support forums special interest mailing lists structured question-answer catalogs in which some people pose problems and others reply with answers
Collective Knowledge Systems the faq-o-sphere A search engine able of ﬁnding questions and answers in this body of content Google is very good in ﬁnding a message in public forums in which someone has asked a question similar to one’s query intelligent users, who know how to formulate their queries and provide feedback about which query/doc pairs were effective though not designed as a system, faq-o-sphere behave as competent expert systems
Collective Knowledge Systems Citizen Journalism blog-o-sphere Product Reviews computer products, gadgets, digital cameras Collaborative ﬁltering Amazon recomendations
Collective Knowledge Systems User-generated content (by a lot of users!) Human-machine synergy Increasing returns with scale Emergent Knowledge new ideas, products, concepts, theories, ways of doing things, etc. how? with the semantic web
Semantic WebThe problem of semantics what we say how we say it different symbols/terms with same meaning same symbols/terms with different meaning
Traditional web “My mouse is broken. I need a new one…” Problem html Computers don’t understand meaningkeyword-based searh Solution?
OntologiesConcept name emailconceptual entity of the domain student Person researchAttribute nr. fieldproperty of a concept isA – hierarchy (taxonomy)Relation Student Professorrelationship between conceptsor properties attends holdsAxiom Lecturecoherent description betweenConcepts / Properties / lectureRelations via logical expressions topic nr.
The role of the semantic web Technology has enabled the generation of collected knowledge by making it easy and cheap to: Capture Store Distribute Communicate Create new value from the collected data
The role of the semantic web Creating value from data is the main role of the semantic web in collective knowledge systems semantic web adds structure to data related to user contributions enabling sharing and computation among independent, heterogeneous social web applications
The role of the semantic webAugmenting user-contributed data withstructured data structured data exposed in a structured way distinguish Paris Hilton from Paris, France expose data in data bases used to build html documents extract data retrospectively from user contributions capture data as people share information
The role of the semantic web Enabling data sharing and computation among applications RDF enables structured data referencing well maintained namespaces, unambiguous entity reference with URIs Ontologies for common conceptualizations independent of data models in social web applications enables integrating tagging data tagCommons project (mapping rather than homogenizing)
Example: Real TravelRealTravel attracts people to writeabout their travels, sharing stories,photos, etc.Travel researchers get the value of allexperiences relevant to their targetdestinations.
Real TravelGroup Stories together by destinationAggregate cities to states to countriesInherit locatioins down to photosInfer geo-coordinatees, which drive dynamicrout managementDestinations map to external contents (travel guides) to targeted advertising
Real Travel as Collective Knowledge SystemUser generated content Most of the content is from real traveler experiencesHuman-machine synergy travel planners could do the equivalent asking asking thousands of other travelers adviceIncreasing returns with scale as more people report their experiences, better coverage (more exotic locations) and depth (what to do or avoid)Emergent knowledge recommendations from unsupervised learning from travel blog texts and multi-dimensional match with structured data (e.g. traveler demographics, declared interest)
Real Traveler asCollective Knowledge SystemsSnap to grid Travel Destinations auto-completion of candidate locations allow introducing new locationsContextual browsing combining tags, location and rating data (feedback from users and editors of content quality)Snap to grid Tags associate tags to useful domain concepts (e.g. arts)
Real TravelPivot searching 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.
Real Travel asCollective Knowledge Systems Learning from semi-structured data System processes every contribution looking at text, tags, user proﬁles and other structured data Clustering of the content to ﬁnd synthetic dimensions Stable classiﬁcation of blogs and users in buckets when users ask for recommendations they introduce desired location, trip length and demographic data this data is used to ﬁlter some dimensions and they are asked to rate the remaining dimensions the system matches this information with classiﬁed users and docs and ranks places to go and traveler blogs for those places
Resources usedOpen source software or free services powerful databases fancy UI libraries search engines usage analyticsOpen APIs from Google Maps and Flickr (photos)Commercially available geo-coordinate data and services
How could semantic could help? 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 Integration with other tagging sites is ad-hoc