3. LANGUAGE
Symbolic,
USER
information is
stored in the
form of a code
or symbol
Resides in the
mind of people,
NOT in
STRINGS
Governed by
rules -
understanding
rules help us
understand one
another
STRING THING
(SYMBOL) (REFERENT)
6. KNOWLEDGE GRAPH
1. Language becomes less
ambiguous
(FINDING)
2. Key facts are displayed
along with the query
(KNOWING)
3. Inter-relationship among
âThingsâ brings the
intelligence to the machine
(UNDERSTANDING)
4. Reality unveils curiosities
(DISCOVERING)
SEARCH ENGINES UNDERSTAND...
7. GOOGLE
KNOWLEDGE GRAPH
âą Includes today more then
500 million things
(~ 0.077 of people alive on
earth)
âą Itâs made of data from
Freebase, Wikipedia, the
CIA World Factbook and
data publicly available on
the Web
âą3.5 billion facts about
and in relationship with
these 500 million entities
âą Itâs based on usersâ
search behaviors
9. In a nutshell
âą Knowledge comes from many sources search engines - closed index
âą Search engines are building their
Cast information
knowledge graphs using web crawling from billion of web
Attributes
pages
techniques
âą We want to do it with the help of
Entity
web editors providing:
âą an open knowledge base aemoo web - open knowledge base
cast information
from Blogger A
âą an aggregated and consistent views
cast information
of all entities Attributes
from Blogger B
Cast information
âą an intuitive classiïŹcation ontology from open
repository
(DBPedia, Freebase)
âą a new way to browse the web of
Entity
objects
10. Social WORDLIFT 2.0 Thing
Media Ready Plot Summary
(RSS/GEORSS/JSON)
Company
Truly
Product
engaging
Creative
Work
Person
Supporting
Schema.org Event
How a Content Intensive web site
can beneïŹt from a semantic driven UI
and a more structured content architecture
Place
11. Mission Statement
âą Empowering editors with new publishing tools and
a federated platform for named entity
sharing,Â
a suite of technologies to enable intuitive
âą Designing
content discovery and recommendation
using Semantic Web and the LOD Cloud,
new means of information
âą Providing
consumption for multiple languages,
âą Marketing web contents using âthingsâ.
13. CREDITS
this presentation is the result of many inspiring ideas and world-wide famous memes,
here is the list:
Amit Singhal, Engineer at Google - http://en.wikipedia.org/wiki/
Amit_Singhal
Michael Bergman, Semantic Expert - http://www.mkbergman.com/about-
mike/
any idea, graphics or meme belonging to us is available
for sharing, copying and re-mixing under
creative commons license 3.0