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Towards Knowledge-Enabled Society
Hideaki TakedaNational Institute of Informatics
[email protected]@takechan2000
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.
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
Layers of Semantic Web
Tim Berners-Lee http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/
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
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
• 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
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
21
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:references dc:references
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]
• 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
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
35
36http://lodc.med-ontology.jp/
37
Have we built “knowledge is power” world?
NO
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
MergedInto I. galbula
I. bullockii
I. galbula
50
1758 1827 1964 1995
I. galbula
I. bullockii
MergedInto I. galbula
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
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
Designing of Agricultural Activity Ontology(AAO)
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
Designing of Agricultural Activity Ontology(AAO)
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)
Designing of Agricultural Activity Ontology(AAO)
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
Designing of Agricultural Activity Ontology(AAO)
Water retention
Land leveling Pulverization
Puddling
Polysemic concepts (Protégé)
Designing of Agricultural Activity Ontology(AAO)
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 」
?
suppression of pest animals
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)
suppression of pest animals
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
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
Public Vocabulary Framework project- Infrastructure for Multilayer Interoperability (IMI) -
• 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
Public Vocabulary Framework project- Infrastructure for Multilayer Interoperability (IMI) -
• 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
Our Society (real world)
Computational World
Our Society (real world)
Computational World
New Technical development
Challenge #1
Our Society (real world)
Computational World
Forming new knowledge
Challenge #2
Our Society (real world)
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)