Privatization and Disinvestment - Meaning, Objectives, Advantages and Disadva...
Gic2011 aula10-ingles
1. Information &
Knowledge Management
Discussion 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
mzacaria@ualg.pt
2. Summary
The Vision of Collective Intelligence
Collective Knowledge Systems
The Role of the Semantic Web
Augmenting User-Contributed Data with Structured Data
Enabling Data Sharing and Computation Across Applications
Example
Collective Knowledge System for Travel
3. The vision of
Collective Intelligence
Web 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
5. The vision of
Collective Intelligence
Harnessing 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
6. The vision of
Collective Intelligence
Collective 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 blogosphere
Unmatched potential for knowledge sharing
Collected intelligence
But not collective intelligence
No emergence of new levels of undersanding of
knowledge
7. The vision of
Collective Intelligence
Collective intelligence has been goal of several
visionaries
Grand challenge is to boost the collective IQ of
organizations and society
human-machine system for
collecting knowledge for learning
evolving technology for collective learning
humans and machines actively contribute doing what they
do best
8. The vision of
Collective Intelligence
Tim 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
9. The vision of
Collective Intelligence
The key is the synergy between humans and machines
What 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
10. The Vision of
Collective Intelligence
With the rise of the social web we have now millions of
humans offering their knowledge online i.e.
The information is stored, searchable and easily shared
Challenge: match between what is put online and
methods for doing useful reasoning with data
True collective knowledge emerges if the knowledge
collected from all those people is aggregated or
recombined to create new knowledge or new ways of
learning
11. Collective Knowledge Systems
human-machines systems in
which machines enable the
collection and harvesting of
large amounts of human-
generated knowledge
12. 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
13. 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
15. Collective Knowledge Systems
Citizen Journalism
blog-o-sphere
Product Reviews
computer products, gadgets, digital cameras
Collaborative filtering
Amazon recomendations
16. 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
17. Semantic Web
The problem of semantics
what we say
how we say it
different symbols/terms with same meaning
same symbols/terms with different meaning
18. Traditional web
“My mouse is broken. I need a new one…”
Problem
html Computers don’t understand meaning
keyword-based searh
Solution?
25. Ontologies
Concept name email
conceptual entity of the domain
student Person research
Attribute nr. field
property of a concept isA – hierarchy (taxonomy)
Relation Student Professor
relationship between concepts
or properties attends holds
Axiom Lecture
coherent description between
Concepts / Properties / lecture
Relations via logical expressions topic
nr.
26. 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
27. 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
28. The role of the semantic web
Augmenting user-contributed data with
structured 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
29. 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)
30. Example: Real 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.
34. Real Travel
Group Stories together by destination
Aggregate cities to states to countries
Inherit locatioins down to photos
Infer geo-coordinatees, which drive dynamic
rout management
Destinations map
to external contents (travel guides)
to targeted advertising
35. Real Travel as
Collective Knowledge System
User generated content
Most of the content is from real traveler experiences
Human-machine synergy
travel planners could do the equivalent asking asking thousands of other
travelers advice
Increasing 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)
36. Real Traveler as
Collective Knowledge Systems
Snap to grid Travel Destinations
auto-completion of candidate locations
allow introducing new locations
Contextual 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)
38. Real Travel
Pivot 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.
39. Real Travel as
Collective 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
40. Resources used
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 geo-coordinate data and services
41. 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
