towards knowledge-enabled society

Towards Knowledge-Enabled Society

Hideaki TakedaNational Institute of Informatics

[email protected]@takechan2000

mailto:[email protected]

Knowledge is power

Knowledge is powerWe have developed our society by/with knowledge.

Then

How will we develop the society in the digital era by/with knowledge?

Knowledge is power

Scientia est potentia. - Sir Francis Bacon

"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 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/

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?

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 learning)

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

Web comes

• World Wide Web creates the inforsphere that everyone can contribute her/his information

http://www.flickr.com/photos/rorycellan/8314288381/http://www.w3.org/2004/Talks/w3c10-HowItAllStarted

Semantic Web

Information Management: A ProposalTim Berners-Lee, CERN March 1989, May 1990

Tim Berners-Lee, James Hendler and Ora Lassila, "The Semantic Web", Scientific American, May 2001, p. 29-37.

http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21

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

http://www.scientificamerican.com/2001/0501issue/0501berners-lee.html

http://www.scientificamerican.com/2001/0501issue/0501berners-lee.html

Layers of Semantic Web

Tim Berners-Lee　 http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/



Descriptions on classes

Descriptions on instances

Ontology

Linked Data

• Ontology– Descriptions on classes– RDFS, OWL– Tasks

• Ontology building– Consistency, comprehensiveness,

logicality• Alignment of ontologies



Descriptions on classes

Descriptions on instances

Ontology

Linked Data

• 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)

Linked Data Principle• Use URIs as names for things• Use HTTP URIs so that people can look up

those names.• When someone looks up a URI, provide useful

information, using the standards (RDF*, SPARQL)

• Include links to other URIs. so that they can discover more things.

Description in Linked Data• Use RDF(+RDFS, OWL)

– 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 “H. Takeda” .<http://www-kasm.nii.ac.jp/~takeda#me> foaf:gender “male” .<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

“H. Takeda” “male” 15

“1955-06-08”

Description in Linked Data

http://www-kasm.nii.ac.jp/~takeda#me

http://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_scientistdbpprop:occupation

“H. Takeda” “male”

“London, England”“Sir Tim Berners-Lee”

16

http://www4.wiwiss.fu-berlin.de/bizer/pub/lod-datasets_2008-09-18.html

LOD Cloud(Linking Open Data)

570 datasets, Last updated: 2014-08-30Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/

20

LODAC (LOD for Academia) Project 2011-2016 • Collect and publish academic data as LOD

LODAC SPECIES: Linking species-related data by name

Specimen DB

Species Info. DB

Taxon Name DBGBIF BioSci.

DB

Category DB

Names：　　　　 113118Triples： 14,532,449

Data from Source BIntegrated data

dc:references dc:references



dc:creatordc:creator

crm:P55_has_current_location

crm:P55_has_current_location

crm:P55_has_current_locationdc:creator

Data from Source AWork

Museum

Creator

Minimum Data to identify entitiesRaw Data for entities Raw Data for entities

Query expansion App.

CKAN (Japanese): Dataset registry

DBPedia Japanese

LODAC Museum: Collecting and Linking museum data

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 40millions triples

RDF type #lodac:Specimen + lodac:Work 1,770,000lodac:Specimen 1,690,000lodac:Work 130,000foaf:Person 8,800foaf: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

ical:location

RDF store

SPA R Q L endpo in t

LODAC Museum OWLIM SE

artw ork

ins titu tion

creator

User Yokohama Art Spot

HTML JavaScript P ython SPARQLWrapper

RDF store

SPA RQ L endpo in t

Yokohama Art LOD A R C 2

RDF store

S PAR Q L endpo in t

PinQA

event question

institu tion

creator

answ er

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]

http://ceur-ws.org/Vol-905/MatsumuraEtAl_COLD2012.pdf



• In s titu tio n n a m e •A c cess

•G en re •C lo sed •A d d re ss •M ap

E v e n t in fo rm a tio n (T im e lin e )

These information are extracted from

Yokohama Art LO D .

E v en t in fo rm a tio n (L is t)

Map View/Institute View

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

[JIST12]Specimenrdf:type

species

institutionName

collectedDate

collectionLocalitycrm: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

ButterflyBDLS

dcterms:source

dcterms:publisher

: Named Graph: owl:Class

Named Graph for the data sources

http://dx.doi.org/10.1007/978-3-642-37996-3_29

An Application: Query expansion for paper search

Input species name

Papers include species name

Papers include same genus species

Papers include common name

DBpedia Japanese

• http://ja.dbpedia.org

• since 2012

• To promote LOD to Japanese communities

• To provide a hub of Japanese resources

30

Applications

• Total: 26– By Category:

• General: 11, Specific 15– By Platform:

• Web:21, Smartphone: 2, Software Extension: 3

Databases

• Total 28:

Publication/CultureGovernmentGeographyGeneralLife ScienceMediaIndustryUser-Generated

33http://fukushima.archive-disasters.jp

34http://fukushima.archive-disasters.jp/id/resource/M2013011819361283671

36http://lodc.med-ontology.jp/

Have we built “knowledge is power” world?

Our Society (real world)

Computational World

We’ve just dealt with knowledge fitted to the computational world

Three challenges to fill the gap

• Representation of Scientific Names – Knowledge revision

• Agriculture Ontology– Integration of domain specific terms

• Core Vocabulary– Integration of terms across domains

Challenge #1Representation of Scientific Names

Dynamics of Scientific Name

• Scientific name looks unique, but more precisely unique as long as the current knowledge– Scientific name changes in time according to new

scientific discovery– Information on species is described with names in

some time (not always now)• How to represent information with

knowledge revision?

44

Northern Oriole These birds are found in the Nearctic in summer, primarily the eastern United States.

Challenge

45

Challenge

Icterus bullockii(Swainson, 1827)

Icterus galbula(Linnaeus, 1758)“Baltimore Oriole”

“Bullock’s Oriole”

46

1758 1827

I. bullockii

I. galbula

47

1758 1827 1964

I. galbula

I. bullockii

I. bullockiiI. galbula

48

1758 1827 1964

I. galbula

I. bullockii

MergedInto I. galbula

49

1758 1827 1964 1995

I. galbula

I. bullockii


I. bullockii

I. galbula

50

1758 1827 1964 1995

I. galbula

I. bullockii


I. bullockii

I. galbula SplitInto

Ontology for Change in Taxonomy

Event-Centric Model for Taxon Revision- case: merge of two families -

• At time t1, Buidae is merged into Audiae.

Event-Centric Model

Different URIs

URI

URI

URI

URI

URI : URI for taxon concept

Taxon concept = Taxon + Synonym

Generating simpler descriptions- From Event-centric model to Transition model -

• Track the history of names

URI

URI

URI

URI

URI

URI

Generating simpler descriptions- From Event-centric model to Snapshot model -

• Just show the current names

URI

URI

URIURI

URI

Linked Taxonomic Knowledge

Linking “Linked Taxonomic Knowledge”

Challenge #2Agriculture Ontology

Standardization of Agricultural Activities Background

Issues

Purpose

Agricultural IT systems are widely adopted to manage and record activities in the fields efficiently. Interoperability among these systems is needed to integrate and analyze such records to improve productivity of agriculture.

To provide the standard vocabulary by defining the ontology for agricultural activity

Data in agricultural IT systems is not easy to federate and integrate

due to the variety of the languages

It prevents federation and integration of these systems and their data.

http://www.toukei.maff.go.jp/dijest/kome/kome05/kome05.html

しろかき

“Puddling”

砕土“Pulverization”

代かき“Puddling”

代掻き

“Puddling”

代掻き作業“Puddling Activity”

荒代 ( かじり )

“Coarse pudding”

荒代かき

“Coarse pudding”

整地“Land grading”

均平化

“land leveling”

AGROVOC

ThesaurusAGROVOC organizes words by synonym, narrower/broader, and related relationship.

harvesting topping(beets)balinggleaningmechanical harvestingmowing

AGROVOC. . .

Narrower/broader relationship is not clearly defined. So relationship among bother words are often mixed and misunderstood.

relationship between siblings

AGROVOC is the most well-known vocabulary in agriculture supervised by Food and Agriculture Organization(FAO) and the thesaurus containing more than 32,000 terms of agriculture, fisheries, food, environment and other related fields.

The number of activity names about rice farming, which is important in Asia including Japan, are insufficient.

Lessons learnt 　– What should be considered Define hierarchy clearly

Accept various synonymous words

Hierarchy is convenient for human to understand and for computers to process. But it often be confused by mixing different criteria on relationship among concepts/words. It causes difficulty when adding new concepts/words and when integrating different hierarchies.

Names for a single concept may be multiple by region and by crop

Define relationship clearly between upper and lower concepts as basis of classification

Clarify an entry word and their synonyms for each concept

harvesting topping(beets)balinggleaningmechanical harvestingmowing

Thesaurus (AGROVOC)

. . .

harvesting mechanical harvesting

manual harvesting

[means]. . .

Harvest Harvest

Harvest

InheritbyMachine

manually

＋

＋

relationship between siblings

Representation: ”Harvesting”

[means][Act]

Ontology!

Define activity concepts

Define hierarchy

Seeding: activity to sow seeds on fields for seed propagation.

Purpose: seed propagationPlace : fieldTarget : seedAct : sow

“Seeding”

Define activities with properties and their values

The hierarchy of activities is organized by property- New properties and their values are added

- “purpose”, “act”, “target”, “place”, “means” , “equipment”, “season”, and “crop” in order.

- Property values are specialized

Seeding

property value

Designing of Agricultural Activity Ontology(AAO)

Formalization by Description Logics

Crop production activity

Crop growth activity

purpose ： crop production

purpose ： crop growth

Agricultural activity

Activity for control of propagationActivity for seed

propagation

purpose ： control of propagation

purpose ： seed propagation

Seedingact : sowtarget ： seedplace ： field

Activity for seed propagation

Seeding

Hierarchy by purpose


Differentiate concepts by property

purpose : seed propagationplace : paddy fieldtarget : seedact : sowcrop ： rice

purpose : seed propagation purpose : seed propagationplace : fieldtarget : seedact : sow

Agricultural activity 　 >…> Activity for seed propagation > Seeding

purpose : seed propagationplace : well-drained paddy fieldtarget : seedact : sowcrop ： rice

Direct sowing of rice on well-drained paddy field Direct seeding in flooded paddy field

Well-drained paddy field < field paddy field < field


Activity for seeding Direct seeding in flooded paddy field

Direct sowing of rice on well-drained paddy field

Seeding on nursery box

The Structuralizaion of the Agricultural Activities (Protégé)


Polysemic concepts

[disjunction form]

[conjunction form]

Pudlling

Subsoil breakingPulverizationLand preparation

Water retentionActivity for water management

Land leveling

Polysemic relationship

Pulverization by harrow

purpose : pulverizationpurpose : water retentionpurpose : land leveling

Definition of agriculture activities with multiple purposes or other properties.

Puddling 　


Water retention

Land leveling Pulverization

Puddling 　

Polysemic concepts (Protégé)


Reasoning by Ontology

Reasoning by Agriculture Activity Ontology

Activity for biotic control

Activity for suppression of pest animals

Activity for suppression of pest animals by physical

means

control of pest animals

Physical means

means(0,1)

purpose(0,1)

Biotic control

purpose(0,1)

Activity for suppression of pest animals by chemical

means

Chemical means

purpose(0,1)

means(0,1)

Making scarecrow‘

suppression of pest animals

Purpose(0,1)

build

act(0,1)

scarecrow

target(0,1)

Physical means

Means (0,1)

？ Example of 「 Making scarecrow 」

?


Infer the most feasible upper concept for the given constraints for a new words

Reasoning by Ontology

かかし作り

物理的手段

means(0,1)

means(0,1)

Inference with SWCLOS

[1] Seiji Koide, Theory and Implementation of Object Oriented Semantic Web Language, PhD Thesis, Graduate University for Advance Studies, 2011

[1]

[1]

Activity for biotic control

Activity for suppression of pest animals

Activity for suppression of pest animals by physical

means

control of pest animals

Physical means

means(0,1)

purpose(0,1)

Biotic control

purpose(0,1)


Activity for suppression of pest animals by chemical

means

Chemical means

purpose(0,1)

means(0,1)

Making scarecrow

make

act(0,1)

scarecrow

target(0,1)

Infer the most feasible upper concept for the given constraints for a new words

Reasoning by Agriculture Activity Ontology

Making scarecrow is a subclass of Activity for suppression of pest animals by physical means

Applying Agricultural Activity Ontology URI

Give a unique URI for each concept

http://cavoc.org/aao/ns/1/ は種

Web Services based on Agriculture Activity Ontology　 Converting synonyms to core vocabulary

http://www.tanbo-kubota.co.jp/foods/watching/14_2.html

“Puddling Activity”“sowing”

…

AAO

PuddlingSeeding

…

Converting

[system]

API

Puddling Activity and sowing…

[system’]

Puddlingand seeding…

http://cavoc.org/Common Agricultural VOCabulary

Agriculture Activity Ontology (AAO) ver 1.31http://cavoc.org/aao/

Agriculture Activity Ontology(AAO): Summary

• Standardize the vocabulary for agricultural activities with the logical model

• Define concepts of agriculture activities beyond • Conceptual variety (often dependent to crop and farm style)• Linguistic diversity (often dependent to crop and area)

• adopted as the part of ”the guideline for agriculture activity names for agriculture IT systems” issued by Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan in 2016,

Challenge #3Core Vocabulary

Data in Government

Information needed to register new cooperation

Information needed to register new cooperation

Managed by multiple agenciesDifferent formats

Lack of linkage

LocalGovernment User

User

Company

Company

LocalGovernment

Government

Company

Product Name

Code Maker Buyer Name

Organization Product

Name Address Name Code

Product Nmae

Product Code

Price Purchase Date

Maker

Public Vocabulary Framework project- Infrastructure for Multilayer Interoperability (IMI) -

• Sharing terms – among administration units– among administration unites and companies– among administration units, companies and users


• A framework that enables exchange of data by sharing primary vocabulary. – Provide basic common concepts

• A core and domains• Extensible vocabulary (application vocabularies)

– For Open data and data exchanges between systems• RDF, XML, and texts

82Citizen ID Enterprise ID Character-set

Vocabulary

Share, Exchange, Storage（ Format）

Applications

IMI

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

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

• Vocabulary set and Information Exchange Package are defined in trial area.

85

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 1APPLIC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 -… … … … … … … … …… … … … … … … … …

85

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

AddressLocationTwoDimensionalGeographicCoordinate

Equipment Information

Spot of Equipment

Business Hours

Owner

Access Availability

UserDay 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

Adaptation by (local) Governments• Ministry of Economics, Trade, and Industries (METI): Corporate

Information Portal• Local Governments:

– Mori Town, Yakumo Town [Hokkaido]– Hirono Town, [Iwate]– Ishinomaki [Miyagi]– Ota City [Gunma]– Kawaguchi City [Saitama]– Kanazawa-Ward (Yokohama City) [Kanagawa]– Shizuoka City [Shizuoka]– Tsuruga City [Fukui]– Osaka City [Osaka]– Oku-izumo Town, Yasugi City [Shimane]– Tokushima Pref., Awa City [Tokushima]– Ube City [Yamaguchi]– …

Corporate Information portal website

Corporate numberCorporate Name

Corporation Type

Area

Resource

Search

Government

RegistersApplications

Gather the data by using IMI based data structure

Corporation

Benefit of the website

CSVPDFRDF

Open Data

Other websites

New ServicesAPI

Knowledge base for all government department

Adaption by Corporate Information Portal• This website uses the IMI core vocabulary that is national standard vocabulary project for

interoperability. • The IMI define basic data items. (Name, Address, Corporation, Facility, - - - )

• corporateBusinessinfo• corporateActivityInfo

hj:Corporate information Type

• name(en)• codeOfIndustry• objectiveOfBusiness• abstractOfBusiness• areaOfBusiness• stakeholder• majorStockHolder• financialInformation•・・・

hj:Corporate business information Type

• adressNumber

hj:Address Type

• noOfStock• holder• ratio

hj:Stock holder Type

•・・・hj:Subsidy Type

•・・・hj:Award Type

•・・・hj:Certification Type

•・・・hj:Contact Type

• typeOfNote• memo

hj:Note Type

• positionOfOrgtype

• organizationType

• capiltal• noOfEmployee

ic:Corporation Type

•・・・ic: Address Type

• dateOfCertification• title• category• block• area• type

hj:Corporate activity Type

• target• reason• amount• status• period• note

IMICore Vocabulary

Corporate Information Domain Vocabulary• ID

• name• abbreviation• alternativeName• status• abstract• contactInformation• relatedOrganizati

on• place• address• representative• dateOfEstablishm

ent• additionalInforma

tion

ic:Organization Type

• businessDomain• startDateOfFy• noOfMember• agent

ic:Business unit Type

enhancerefer


• Towards interoperability beyond regions– Community of Practice on Core Data Models

• Sharing good practice • Mapping between core vocabularies• DG Informatics (EC)• IMI (Japan)• NIEM (USA)

NIEM

ISAJoinUp

UNCEFACT

IMI

Lessons learnt from the challenges


Computational World


Computational World

New Technical development

Challenge #1


Computational World

Forming new knowledge

Challenge #2


Computational World

Forming Structure in Society

Challenge #3

Lessons learnt from the challenges

The challenges arenot just in the computational world

rather between the computational and the real

worldseven

in the real world

We must be socio-computer scientists

Summary

Semantic Web created the first step for knowledge representation in the computer world

But the computational world alone is not enough. We should commit (or even change) both the computational and real world to real “knowledge is power” world. In order to do so, we must work with people in our society.

Acknowledgement• Thanks to

– Ikki Ohmukai (NII/LODI)– Fumihiro Kato (NII/LODI)– Seiji Koide (NII/LODI/Ontolonomy)– Sungmin Joo (NII)– Rathachai Chawuthai (NII/Sokendai)– Akane Takezaki (NARO)– Daisuke Horyu (NARO)– Iwao Kobayashi (LODI/Scholex)– Fumiko Matsumura (LODI/Aoyama Gakuin U.)– Kenji Hiramoto (METI)– Shuichi Tashiro (IPA)– Korosue Kazuyoshi (IPA)– (and more)