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Knowledge is power (now again)
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Transcript of Knowledge is power (now again)
Knowledge is power (now again)
Hideaki Takeda
National Institute of Informatics
email:[email protected]
ORCID:0000-0002-2909-7163
Keynote talk, The 4th Joint International Semantic Technology Conference, Nov. 9-11, 2014, Chiang Mai, Thailand
It is my journey to seek knowledge …
scientia potentia est
- Sir Francis Bacon
Knowledge is power
"Pourbus Francis Bacon" by Frans Pourbus the younger - www.lazienki-krolewskie.pl. Licensed under Public domain via Wikimedia
Commons - http://commons.wikimedia.org/wiki/File:Pourbus_Francis_Bacon.jpg#mediaviewer/File:Pourbus_Francis_Bacon.jpg
Knowledge in Artificial Intelligence
• AI research in 60s.
• AI systems is to achieve intelligent activities instead of human• Theorem solver
• Chess play
• Scene recognition
• …
• AI system = Software
• AI system = reasoning + Knowledge
Knowledge is power in AI
• Edward Feigenbaum• "father of expert systems“
• Knowledge is power, and the computer is an amplifier of that power. We are now at the dawn of a new computer revolution... Knowledge itself is to become the new wealth of nations.
"27. Dr. Edward A. Feigenbaum 1994-1997" by United States Air Force - United States Air Force. Licensed under Public
domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:27._Dr._Edward_A._Feigenbaum_1994-
1997.jpg#mediaviewer/File:27._Dr._Edward_A._Feigenbaum_1994-1997.jpg
http://www.computerhistory.org/fellowawards/hall/bios/Edward,Feigenbaum/
Expert systems
• DENDRAL 1965-, discovery of hypothesis
• HEARSAY 1967-, Speech Recognition
• SHRDLU 1971-, Natural language understanding
• CASNET 1971-, diagnosis of disease
• MYCIN 1972-, diagnosis of disease
• INTERNIST 1972- , diagnosis of disease
• PROSECTOR 1975-, consultation of mineral exploration
Boom of Expert Systems
Then a lot of industry applications on diagnosis, planning … (80s)
Knowledge Acquisition Bottleneck
Knowledge Acquisition Bottleneck
• How can we tell knowledge to computers?• Knowledge Engineers & Domain Experts work together to extract and
transform knowledge good for computers. But it is time-consuming, and always insufficient and incomplete.
• How can we understand knowledge for computers?• Transformed knowledge is often hard to understand.
• How can we maintain knowledge for computers?• The real world is changing.
How to adapt it? Who and how?
"Bocksbeutel bottle" by Prince Grobhelm - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia
Commons - http://commons.wikimedia.org/wiki/File:Bocksbeutel_bottle.jpg#mediaviewer/File:Bocksbeutel_bottle.jpg
Knowledge Acquisition Bottleneck
• Solutions – how we can obtain knowledge• Ontology
• Sharable, sustainable, and formal knowledge about the world
• Learning• Learning from the initial knowledge (supervised learning)
• Learning from the real world (un-supervised learng)
PLACEnam e NAM E -S T RI NG
displ ay-point L OCA TI ON- P OI NT
fea ture
PRE FE CT UREC IT Y
L AKE
PAR K
L OC AT ION-POINT
x-loc at i ony-loc at i on
B OUNDAR Y-L INEbounda ry-l i ne s lis to f L OCA TI ON- P OI NT
bounda ry-kind
POINT
loc a t ion L OCA TI ON- P OI NT
C OURSE
l ine s lis to f L OCA TI ON- P OI NT
R AILR OAD-ST AT ION
B US-ST OP
JR-STATION
KINTE TSU-ST AT ION
STATIONt raffi c-fac i l it y
B UILDING
T EMPL ESPOT
L AKE
PAR K
ARE Aborder seto f B OU ND A RY - LI NE
R IVER
R AILR OAD
B US-LINE
JR-R AILR OAD
KINTE TSU-R AIL ROAD
T RAFFIC-L INE
R OAD
NAR AKOT U-B US-LINE
DOR MITORY
UNIVE RSITY-HALL
UNIVE RSITY
VISIT-
PLACENAM E -
S TR I NG A MO UNT -
O F- M ONEY T IM E -T O -
T IM E TI M E-
L ENG TH ACCES S -
I NF O TE L EP HO NE-
NUM BE R
nam e
fee
a dm ission-t im erequi re d-t imehow-to-ac c ess
t el e phone
nam e NAM E -S T RI NG
a ddress A DD RE S S- S TR I NGt el e phone T EL E PH ONE -NU MB E R
HOT EL
NUM BE R N
U MB ER A M
O UNT- O F-
M ONEY A M
O UNT- O F-
M ONEY T IM
E -
P OI NTT I ME
-
P OI NTA MO
U NT - OF -
M ONEY NU
M BE R AM O
U NT - OF -
M ONEY A M
O UNT- O F-
M ONEY NA
M E-
S TR I NG A D
singl e-room -numbertwin-room-num be rsingl e-room -fe e
twin-room-feec he c k-in-t imec he c k-out -t im e
perking-fe eparking-lim itmorning-fe edinner-fee
nam ea ddresst el e phone
how-to-ac c essspe c i al -fe a ture
room -number
c apa ci ty
nea rest -st a t ion
a cc e ss-me a nsa cc e ss-t im e
T EMPL EN
A ME -
S TR I NG A MO
U NT - OF -
M ONEY T IM E -
T O- T IM E TI M E-
L ENG TH ACC
E SS -
I NF O TE L EP H
O NE -
nam e
fee
a dm ission-t im erequi re d-t ime
how-to-ac c ess
t el e phone
ACC OMMODATION
nea rest -st a t ion S TA T IO N
GEOGR APHIC AL -THING
KC-Kansai: Knowledge-based multi-agent system
T. Nishida and H. Takeda: Towards the
Knowledgeable Community, K.
Fuchi and T. Yokoi eds., Knowledge
Building and Knowledge Sharing, pp 155–
164, Ohmsha, IOS Press (1994).
[KBKS94]
PLACEnam e NAM E -S T RI NG
displ ay-point L OCA TI ON- P OI NT
fea ture
PRE FE CT UREC IT Y
L AKE
PAR K
L OC AT ION-POINT
x-loc at i ony-loc at i on
B OUNDAR Y-L INEbounda ry-l i ne s lis to f L OCA TI ON- P OI NT
bounda ry-kind
POINT
loc a t ion L OCA TI ON- P OI NT
C OURSE
l ine s lis to f L OCA TI ON- P OI NT
R AILR OAD-ST AT ION
B US-ST OP
JR-STATION
KINTE TSU-ST AT ION
STATIONt raffi c-fac i l it y
B UILDING
T EMPL ESPOT
L AKE
PAR K
ARE Aborder seto f B OU ND A RY - LI NE
R IVER
R AILR OAD
B US-LINE
JR-R AILR OAD
KINTE TSU-R AIL ROAD
T RAFFIC-L INE
R OAD
NAR AKOT U-B US-LINE
DOR MITORY
UNIVE RSITY-HALL
UNIVE RSITY
VISIT-
PLACENAM E -
S TR I NG A MO UNT -
O F- M ONEY T IM E -T O -
T IM E TI M E-
L ENG TH ACCES S -
I NF O TE L EP HO NE-
NUM BE R
nam e
fee
a dm ission-t im erequi re d-t imehow-to-ac c ess
t el e phone
nam e NAM E -S T RI NG
a ddress A DD RE S S- S TR I NGt el e phone T EL E PH ONE -NU MB E R
HOT EL
NUM BE R N
U MB ER A M
O UNT- O F-
M ONEY A M
O UNT- O F-
M ONEY T IM
E -
P OI NTT I ME
-
P OI NTA MO
U NT - OF -
M ONEY NU
M BE R AM O
U NT - OF -
M ONEY A M
O UNT- O F-
M ONEY NA
M E-
S TR I NG A D
singl e-room -numbertwin-room-num be rsingl e-room -fe e
twin-room-feec he c k-in-t imec he c k-out -t im e
perking-fe eparking-lim itmorning-fe edinner-fee
nam ea ddresst el e phone
how-to-ac c essspe c i al -fe a ture
room -number
c apa ci ty
nea rest -st a t ion
a cc e ss-me a nsa cc e ss-t im e
T EMPL EN
A ME -
S TR I NG A MO
U NT - OF -
M ONEY T IM E -
T O- T IM E TI M E-
L ENG TH ACC
E SS -
I NF O TE L EP H
O NE -
nam e
fee
a dm ission-t im erequi re d-t ime
how-to-ac c ess
t el e phone
ACC OMMODATION
nea rest -st a t ion S TA T IO N
GEOGR APHIC AL -THING
Geography agent
Park Agent
Traffic Agent
Railway Agent
Kintetsu Agent
Railway Agent
JR AgentKintetsu Agent
Hotel Agent
JR Agent
Sight-seeing Agent
Temple Agent
Group
Park AgentTodaiji-temple
AgentAkishino-
temple AgentTemple Agent Group
KC-Kansaiのエージェント
KC-Kansaiの出力
Knowledge Acquisition Bottleneck
• Solutions – how we can obtain knowledge• Ontology
• Sharable, sustainable, and formal knowledge about the world
• Learning• Learning from the initial knowledge (supervised learning)
• Learning from the real world (un-supervised learng)
They are still inside of the computational world. But what we’ve
learnt from the expert systems issue is the difficulty lies on the
interface between the computational world and the human society
Knowledge Acquisition Dimensions
• People• Who is contributor of knowledge to computers?
• Form • What kind of form is good for sharing knowledge between people and
computers
• Way of contribution• How can people and computers share knowledge?
We need socio-technical solutions
bridging the computational world and the human society
The Web comes …
CC BY-NC-ND 2.0 https://www.flickr.com/photos/12693492@N04/1339026964/
http://www.w3.org/2004/Talks/w3c10-HowItAllStarted
Knowledge is on the Web!!
• People will put their knowledge into Web soon.
• Web will be the silo of information to extract knowledge
Extracting knowledge from Web
M. Iwazume, K. Shirakami, K. Hatadani, H. Takeda and T. Nishida: IICA: An Ontology-based Internet Navigation
System, in Working notes for AAAI96 Workshop on Internet-Based Information Systems, pp 65–71 (1996)
[AAAI96WS]
Extracting knowledge from Web
Knowledge Acquisition Dimensions
• People• Who is contributor of knowledge to computers?
• Form • What kind of form is good for sharing knowledge between people and
computers
• Way of contribution• How can people and computers share knowledge?
Web created the channels which people can contribute
their knowledge to global knowledge-sphere
Semantic Web
Information Management: A ProposalTim Berners-Lee, CERNMarch 1989, May 1990
Tim Berners-Lee, James Hendler and Ora Lassila, "The
Semantic Web", Scientific American, May 2001, p. 29-37.
Semantic Web
• "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."
The Semantic Web, Scientific American, May 2001, Tim Berners-Lee, James Hendler and Ora Lassila
Semantic Web
Tim Berners-Lee http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/
Layers of Semantic Web• Ontology
• Descriptions on classes
• RDFS, OWL
• Tasks
• Ontology building
• Consistency, comprehensiveness, logicality
• Alignment of ontologies
Tim Berners-Lee http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/
Descriptions on classes
Descriptions on instances
Ontology
Linked Data
Layers of Semantic Web• Linked Data
• Descriptions on instances (individuals)
• RDF + (RDFS, OWL)
• Pros for Linked Data
• Easy to write (mainly fact description)
• Easy to link (fact to fact link)
• Cons for Linked Data
• Difficult to describe complex structures
• Still need for class description (-> ontology)
Tim Berners-Lee http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/
Descriptions on classes
Description on instances
Ontology
Linked Data
RDF• Very Simple!: <subject> <predicate> <object> .
<http://www-kasm.nii.ac.jp/~takeda#me> rdfs:type foaf:Person .
<http://www-kasm.nii.ac.jp/~takeda#me> foaf:name “Hideaki Takeda”@en .
<http://www-kasm.nii.ac.jp/~takeda#me> foaf:gender “male”@en .
<http://www-kasm.nii.ac.jp/~takeda#me> foaf:knows
<http://southampton.rkbexplorer.com/id/person07113> .
http://www-kasm.nii.ac.jp/
~takeda#me
http://southampton.rkbexplorer.com
/id/person07113
foaf:knows
foaf:Person
rdfs:type
foaf:name foaf:gender
“Hideaki Takeda”@en “male”@en
“1955-06-08”
RDF
http://www-kasm.nii.ac.jp/
~takeda#mehttp://southampton.rkbexplorer.com/
id/person-07113
foaf:knows
foaf:Person
rdfs:type
foaf:name foaf:gender
<http://dbpedia.org/resource/Tim_Berners-Lee>
owl:sameAs
dbpprop:birthDatedbpprop:birthPlacedbpprop:name
dbpedia:Computer_scientist
dbpprop:occupation
“Hideaki Takeda”@en “male”@en
“London, England”@en“Sir Tim Berners-Lee”@en
RDF Schema
<rdf:RDF xml:lang="en"xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#">
<rdfs:Class rdf:ID="Person"><rdfs:comment>The class of people.</rdfs:comment><rdfs:subClassOf rdf:resource="http://www.w3.org/
2000/03/example/classes#Animal"/>
</rdfs:Class><rdf:Property ID="maritalStatus"><rdfs:range rdf:resource="#MaritalStatus"/><rdfs:domain rdf:resource="#Person"/>
</rdf:Property><rdf:Property ID="ssn"><rdfs:comment>Social Security Number</rdfs:comment><rdfs:rangerdf:resource="http://www.w3.org/2000/03/example/classes#Integer"/>
<rdfs:domain rdf:resource="#Person"/></rdf:Property><rdf:Property ID="age"><rdfs:rangerdf:resource="http://www.w3.org/2000/03/example/classes#Integer"/>
<rdfs:domain rdf:resource="#Person"/></rdf:Property><rdfs:Class rdf:ID="MaritalStatus"/><MaritalStatus rdf:ID="Married"/><MaritalStatus rdf:ID="Divorced"/><MaritalStatus rdf:ID="Single"/><MaritalStatus rdf:ID="Widowed"/></rdf:RDF>
Animal
Person
ssnage
maritalStatus
s
d
MaritalStatus
r
“The class of person”
rdfs:comment
Integer
d
r
d
“Social Security Number”
rdfs:comment
t = rdf:type
d = rdfs:domain
r = rdfs:range
= class
= class instance
= property
Resource Description Framework(RDF) Schema Specification 1.0
http://www.w3.org/TR/2000/CR-rdf-schema-20000327/
Married
Divorced
Single
Windowed
t
t
t
t
570 datasets,
Last updated: 2014-08-30
Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
Annotating documents by Linked Data
I. Yamada, T. Ito, S. Usami, S. Takagi, H. Takeda and Y. Takefuji: Evaluating the helpfulness of linked entities to readers, L. Ferres, G. Rossi, V. Almeida and E. Herder eds., Proceedings of the 25th ACM conference on Hypertext and social media, pp169–178, Santiago, Chile (2014), ACM.
[HT14]
LODAC Museum
• Purpose
• Enable creation, publishing, sharing and reuse of collection information distributed to
each museum by introducing LOD.
• Enable to uniquely identify resources such as works, creators, and institutions, and
relations between those on the web
• Activities
• Integrate and share collection data aggregated from data sources as RDF.
• Provide applications using generated LOD.
• Data sources
• Collection data obtained from websites of 114 museums.
• The Database of Japan Arts Thesaurus
• The database of government-designated cultural property
• Cultural Heritage Online
Work Creator
Institution
Resources
Over 40
millions triples
RDF type #
lodac:Specimen + lodac:Work 1,770,000
lodac:Specimen 1,690,000
lodac:Work 130,000
foaf:Person 8,800
foaf:Organization 200,000
Yokohama Art Spot
• provides information on art around Yokohama.• is a good example of how such efforts by local people can be
rewarded by flexible use of the provided data.
LODAC Museum × Yokohama Art LOD × PinQAMuseum Collection Local Event Information Q&A ic
al:lo
catio
n
RDF store
SPARQL endpoint
LODAC Museum OWLIM SE
artwork
institution
creator
User Yokohama Art Spot
HTML
JavaScript
Python
SPARQLWrapper
RDF store
SPARQL endpoint
Yokohama Art LOD
ARC2
RDF store
SPARQL endpoint
PinQA
event
question
institution
creator
answer
user
F. Matsumura, I. Kobayashi, F. Kato, T. Kamura, I.
Ohmukai and H. Takeda: Producing and Consuming
Linked Open Data on Art with a Local Community, J. F.
Sequeda, A. Harth and O. Hartig eds., Proceedings of the
Third International Workshop on Consuming Linked Data
(COLD 2012) (2012), CEUR Workshop Proceedings Vol-
905.
[COLD12]
Map View/Institute View
•Institution name
•Access
•Genre
•Closed
•Address
•Map
Event information
(Timeline)
These information are extracted from
Yokohama Art LOD.
Event information
(List)
LODAC Species: Interlinking species data
• Taxon names: 443,248
• Scientific name: 226,141
• Common name: 219,865
• hasScientificName property node: 87,160
• hasCommonName property node: 84,610
Y. Minami, H. Takeda1, F. Kato, I. Ohmukai, N. Arai, U. Jinbo, M.
Ito, S. Kobayashi and S. Kawamoto: Towards a Data Hub for
Biodiversity with LOD, H. Takeda, Y. Qu, R. Mizoguchi and Y.
Kitamura eds., Semantic Technology - Second Joint International
Conference, JIST 2012, Nara, Japan, December 2-4, 2012.
Proceedings, Vol 7774 ofLNCS, pp 356–361, Springer (2013).
• Integrating species databases as linked data
Specimen
rdf:type
species
institutionName
collectedDate
collectionLocality
crm:has_current_location
Bryophytes
TaxonName
ScientificNameCommonName TaxonRank
species
rdfs:subClassOfrdfs:subClassOf
rdf:typerdf:type
hasCommonName
hasScientificName hasSuperTaxon
rdf:type
hasTaxonRank
rdf:type
hasTaxonRank
rdf:type
Butterfly
BDLSdcterms:source
dcterms:publisher
: Named Graph: owl:Class
Named Graph for
the data sources
[JIST12]
An Application: Query expansion for paper search
Input species name
Papers include species
name
Papers include same genus species
Papers include
common name
http://lod.ac/apps/cinii_species
http://lod.ac/apps/lsdcs
RDF data of
Interspecies
Interactions
Projection
of Fungi
Collaborative
Filtering
Community
Structure
Biological
Classification
SPARQL
querying
being input of
Scoring Functions
ranking
predictions
in decreasing
order
Predicted Missing Links
of Fungus-Host together with
prediction scores
DATA PREPARATION LPII APPROACH
RESULT
Bipartite Graph
Missing
Links
Community
Detection Method
transform data using
a Weight Function
DOMAIN
EXPERT
found?yes
update
knowledgebase
NOTE
select
connected fungi
clustering using
Biological
Classification
make
observation
Data
Process
Third party method
Scoring Function
Input argument
Linear Operation
Decision
Dataflow
+
find
missing
linkssharing
LOD
Cloud
PII(f,h) +
PCF(f,h) PCS
(f,h) PBC(f,h)
1 2
3
4
42
R. Chawuthai, H. Takeda,
and T. Hosoya, Link
Prediction in Linked Data
of Interspecies
Interactions using
Hybrid Recommendation
Approach, JIST2014
[JIST14]
Public Vocabulary Framework project
• Infrastructure for Multilayer Interoperability (IMI)
• Prepare a framework that enables exchange of data, primarily vocabulary sets. • Divide into two areas.
• core and business domain
• Unnecessary to reconvert exiting systems.• International interoperability • Utilize existing standards as much as possible.
Citizen ID Enterprise ID Character-set
Vocabulary
Share, Exchange, Storage
(Format)
Applications
IMI
IMI
Japanese Local
government Standard
(APPLIC)
DefactStandard
(DC, foaf, etc)
NIEM
(US)
ISA
(EU)
Schema.org
International interoperability is highly
considered in preparing IMI.
Primary considerations:
vocabulary sets used in Japan
and existing standards
43
Vocabulary structure of IMI• IMI consists of core vocabulary, cross domain vocabulary and domain-
specific vocabularies.
Core
Vocabulary
Domain-specific VocabulariesVocabularies that are specialised for
the use in each domain.
Eg) number of beds, Schedule.
Shelter
Location
Hospital
Station
Disaster
Restoration
Cost
Cross Domain VocabularyKey vocabularies among domain-
specific vocabularies that are
referenced in other domains.
Eg) hospital, station, shelter.
Core VocabularyUniversal vocabularies that are widely used
in any domain.
Eg) people, object, place, date.
Geographical Space
/Facilities
Transportation
Disaster
Prevention
Finance
Domain-specific
Vocabularies
Cross Domain
Vocabulary
44
項目名 英語名 データタイプ 項目説明 項目説明(英語) キーワード サンプル値 Usage Info
人 PersonType
氏名 PersonName PersonNameType 氏名 Name of a Person -
性別 Gender<abstract element, no type>
性別 Gender of a Person -
Substitutable Elements:
性別コード GenderCode CodeType 性別のコード Gender of a Person 1
APPLIC標準仕様V2.3データ一覧住民基本台帳:性別引用
性別名 GenderText TextType 性別 Gender of a Person 男
現住所 PresentAddress
AddressType 現住所 -
本籍 AddressType 本籍 -
… … … … … … … … …
… … … … … … … … …
Image of IMI vocabulary• Vocabulary set and Information Exchange Package are
defined in trial area.
45
項目名(Type/Sub-properties) 英語名 データタイプ …
氏名 PersonNameType
氏名 FullName TextType
フリガナ TextType
姓 FamilyName TextType
カナ姓 TextType
… … …
AED
Location
Address
LocationTwoDimensionalGeographicCoordinate
Equipment Information
Spot of Equipment
Business Hours
Owner
Access Availability
User
Day of Installation
Homepage
AEDInformation
Type of Pad
Expiry date
Contact
Type
Model Number
Serial Number
Photo
NoteInformation
Source
Sample 1 : Definition of vocabularySample 2 : Information Exchange Package
Knowledge Acquisition Dimensions
• People• Who is contributor of knowledge to computers?
• Form • What kind of form is good for sharing knowledge between people and
computers
• Way of contribution• How can people and computers share knowledge?
Semantic Web created the form by which people can
contribute their knowledge to tell computers
Knowledge Acquisition Dimensions
• People• Who is contributor of knowledge?
• Form • What kind of form is good for sharing knowledge between people and
computers
• Way of contribution• How can people and computers share knowledge?
Social Web
• Active participation of people to Web• From one-way Web to two-way Web
• Examples• SNS: Facebook, twitter, instagram
• Blogs:
• “Crowds of Wisdoms” site: Wikipedia, freebase, Yahoo!Answers
• Recommendation: Amazon, tripadvisor
Finding human relationship at knowledge level
• Finding common knowledge as accumulation of personal knowledge
• Calculate relationship among people based on personal knowledge
Finding human relationship at knowledge level
• Calculate relationship among Web bookmarks
• Instance-based hierarchy matching algorithm (HICAL)
M. Hamasaki and H. Takeda: Experimental Results for a
method to discover of human relationship based on WWW
bookmarks, N. Baba, L. C. Jain and R. J. Howlett eds., In
Proceedings of Fifth International Conference on Knowledge-
Based Intelligent Information Engineering Systems & Allied
Thchnologies (KES-2001), Vol 2, pp 1291–
1295,Osaka (2001), IOS Press.
R. Ichise, H. Takeda and S. Honiden: Integrating Multiple
Internet Directories by Instance-based Learning, in Proceedings
of the Eighteenth International Joint Conference on Artificial
Intelligence, (IJCAI-03), pp 22–28 (2003).
[KES01]
[IJCAI03]
Associating personal knowledge by property: social infobox
M. Hamasaki, M. Goto, H. Takeda: Social Infobox: collaborative knowledge construction by social property tagging,
Proc. CSCW 2011, (2011)
[CSCW11]
Finding human relationship at knowledge level
• Calculate relationship among people based on academic records
(1) Search window (5) Graph type selector (3) Slide bar
Statistics
• No. of researchers
• No. of Links
(6) Bibliography list
(4) Tool box
(2) Author list
R. Ichise, H. Takeda and K.
Ueyama: Community Mining Tool using
Bibliography Data, in Proceedings of the
9th International Conference on
Information Visualization, pp953–
958 (2005).
[IV05]
Massively Collaborative Creation
• A new style for content creation enabled by Web• Web 2.0 style on content creation
• Key features• Massive participation
• Numerous people are involved, even though they often do not know each other
• Creating contents collaboratively • Contents are created as a result of many people’ effort
• Just sharing contents is not enough. Collaboration is important
M. Hamasaki, H. Takeda, T. Hope and T. Nishimura: Network Analysis of an Emergent Massively
Collaborative Creation Community -- How Can People Create Videos Collaboratively without
Collaboration?, E. Adar, M. Hurst, T. Finin, N. Glance, N. Nicolov and B. Tseng eds., Proceedings of the Third
International Conference on Weblog and Social Media (ICWSM-09), pp 222–225, AAAI (2009).
[ICWSM09]
Chain of creation
sm12825985(Original song)
sm12926280(Daning
Song/Voice/BGM
sm13129465(singing)
Song/Voice/Movie
※各動画を示す画像にはニコニコ動画上で公開されているサムネイル画像を利用しています 2013/5濱崎雅弘氏作成
Chain of creation
sm14298262(Hand-written Animation)
choreographyVoice
※各動画を示す画像にはニコニコ動画上で公開されているサムネイル画像を利用しています 2013/5濱崎雅弘氏作成
sm12825985(Original song)
sm12926280(Daning
Song/Voice/BGM
sm13129465(singing)
Song/Voice/Movie
Chain of creation
sm12825985,Original Song,2,592,882 views
sm12926280,Dancing
1,680,188 views
sm14982266 mixing
28,130 views
sm14209464,Playing
381,143 views
sm14977117,Playing
302,974 views
sm14298262,Hand-written Animation
215,244 views
choreography
sm13129465,Singing
783,424 views
Voice
Song/BGM/Movie
Song/Voice/BGM
BGM BGM
sm14065494,Group singing
36,217 views
sm14054482,arrangement
12,543 views
BGM
Song
sm12881690,Singing
101,994 views
Song/BGM/Movie
sm12938895,Singing
108,456 views
歌詞
lyricschoreography
Dancing,3000 movies
Singing,2000 movies
Song/BGM/Movie
Voice
※各動画を示す画像にはニコニコ動画上で公開されているサムネイル画像を利用しています 2013/5 濱崎雅弘氏作成
Song/Voice/BGMSong/Voice/BGM
A part of network of re-using relationship
among creators using Hatune Miku on Nico
Nico Douga
Relationship among categories of creation
W&I
I&C
W&I&
C
unknown
W(Songwriting )
C(Song creation)
I(Illustration)
W&C
241 149
152
200
203
131
102
58
14959
6853
5865
70
75
R. Cazabet and H. Takeda: Understanding
mass cooperation through visualization, L.
Ferres, G. Rossi, V. Almeida and E.
Herder eds.,Proceedings of the 25th ACM
conference on Hypertext and social media,
pp 212–217, Santiago, Chile (2014), ACM.
[HT14]
Knowledge Acquisition Dimensions
• People• Who is contributor of knowledge?
• Form • What kind of form is good for sharing knowledge between people and
computers
• Way of contribution• How can people and computers share knowledge?
Social Web involved people into global knowledge-sphere
not only inputting knowledge but evolving knowledge on it.
WebKnowledge sharing platform
Semantic WebKnowledge structure sharable between human and computers
Social WebPeople’s involvement mechanism
Social Semantic Web
as Knowledge Infrastructure
Where is knowledge?
Social Semantic Web is the platform to enable knowledge level Interaction and collaboration
Knowledge can emerge from silo of data when people interact to each other via Web or work with Web
The journey is continuing …
• Knowledge structure
• Knowledge creation process
ISWC2016
October 16 (SUN), 2016 – October 21 (FRI), 2016
(15th International Semantic Web Conference)
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