Knowledge is power (now again)

Hideaki Takeda

National Institute of Informatics

email:takeda@nii.ac.jp

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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