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Towards Knowledge-Enabled Society

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Keynote talk at ER2016, Gifu, November 16, 2016

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Towards Knowledge-Enabled Society

  1. 1. Towards Knowledge-Enabled Society Hideaki Takeda National Institute of Informatics takeda@nii.ac.jp @takechan2000
  2. 2. Knowledge is power
  3. 3. Knowledge is power We have developed our society by/with knowledge. Then How will we develop the society in the digital era by/with knowledge?
  4. 4. 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:Pourbu s_Francis_Bacon.jpg
  5. 5. 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/
  6. 6. 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?
  7. 7. 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
  8. 8. 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
  9. 9. Semantic Web Information Management: A Proposal Tim 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.
  10. 10. 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
  11. 11. Layers of Semantic Web Tim Berners-Lee http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/
  12. 12. 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
  13. 13. 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)
  14. 14. 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.
  15. 15. 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
  16. 16. “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_scientist dbpprop:occupation “H. Takeda” “male” “London, England”“Sir Tim Berners-Lee” 16
  17. 17. LOD Cloud (Linking Open Data)
  18. 18. 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/ 20
  19. 19. 21
  20. 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: 113118 Triples:14,532,449 Data from Source BIntegrated data dc:references dc:references dc:references dc:references dc:references dc:references dc:creator dc:creator crm:P55_has_current_location crm:P55_has_current_location crm:P55_has_current_location dc:creator Data from Source A Work 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
  21. 21. 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
  22. 22. 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
  23. 23. 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 × PinQA Museum Collection Local Event Information Q&A ical:location 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]
  24. 24. •Institution name •Access •Genre •Closed •Address •Map Event information (Timeline) These information are extracted from Yokohama Art LOD. Event information (List) Map View/ Institute View
  25. 25. 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]Specimen rdf:type species institutionName collectedDate collectionLocality crm:has_current_location Bryophytes TaxonName ScientificName CommonName TaxonRank species rdfs:subClassOf rdfs:subClassOf rdf:type rdf:type hasCommonName hasScientificName hasSuperTaxon rdf:type hasTaxonRank rdf:type hasTaxonRank rdf:type Butterfly BDLS dcterms:source dcterms:publisher : Named Graph : owl:Class Named Graph for the data sources
  26. 26. An Application: Query expansion for paper search Input species name Papers include species name Papers include same genus species Papers include common name
  27. 27. DBpedia Japanese • http://ja.dbpedia.org • since 2012 • To promote LOD to Japanese communities • To provide a hub of Japanese resources 3 0
  28. 28. Applications • Total: 26 – By Category: • General: 11, Specific 15 – By Platform: • Web:21, Smartphone: 2, Software Extension: 3
  29. 29. Databases • Total 28: Publication/Culture Government Geography General Life Science Media Industry User-Generated
  30. 30. 33 http://fukushima.archive-disasters.
  31. 31. 34 http://fukushima.archive-disasters.jp/id/resource/M2013011819361
  32. 32. 35
  33. 33. 36 http://lodc.med-ontology.jp/
  34. 34. 37
  35. 35. Have we built “knowledge is power” world?
  36. 36. NO
  37. 37. Our Society (real world) Computational World We’ve just dealt with knowledge fitted to the computational world
  38. 38. 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
  39. 39. Challenge #1 Representation of Scientific Names
  40. 40. 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?
  41. 41. Northern Oriole These birds are found in the Nearctic in summer, primarily the eastern United States. 44 Challenge
  42. 42. Challenge 45 Icterus bullockii (Swainson, 1827) Icterus galbula (Linnaeus, 1758) “Baltimore Oriole” “Bullock’s Oriole”
  43. 43. 1758 1827 46 I. bullockii I. galbula
  44. 44. 1758 1827 1964 47 I. galbula I. bullockii I. bullockiiI. galbula
  45. 45. 1758 1827 1964 48 I. galbula I. bullockii Merged Into I. galbula
  46. 46. 1758 1827 1964 1995 49 I. galbula I. bullockii Merged Into I. galbula I. bullockii I. galbula
  47. 47. 1758 1827 1964 1995 50 I. galbula I. bullockii Merged Into I. galbula I. bullockii I. galbula Split Into
  48. 48. Ontology for Change in Taxonomy
  49. 49. Event-Centric Model for Taxon Revision - case: merge of two families - • At time t1, Buidae is merged into Audiae. ltk:Taxon Merger ltk:Change HigherTaxon ex:merge1 ex:reclass1 ex:event1 rdf:type rdf:type cka:interval “t1” “t2” tl:beginsAt DateTime tl:endsAt DateTime cka:effect ex:Auidae_1 ex:Buidae_1 ex:Auidae_2 ex:Xus_1 (OPR) (OPR) (opr)(opr) (con) (con) (con) (con) (event) Event-Centric Model Different URIs URI URI URI URI URI : URI for taxon concept Taxon concept = Taxon + Synonym
  50. 50. ltk:Taxon Merger ex:merge1 ex:Auidae_1 ex:Auidae_2 ex:Buidae_1 rdf:type cka:Concept Evolution rdfs:subClassOf ltk:mergedInto ltk:linking Property ltk:mergedInto (OPR) (opr) (con) (con) (con) ex:event1 cka:interval “t1” “t2” tl:beginsAt DateTime tl:endsAt DateTime cka:assures (event) rules ex:Auidae_1 ex:Buidae_1 ex:Auidae_2 ltk:major MergedInto ltk:major MergedInto ex:inv1 ltk:major Link “t1” “t1” “t2” “t1” ltk:expired ltk:expired ltk:entered ltk:expired Event-Centric Model Transition Model Generating simpler descriptions - From Event-centric model to Transition model - • Track the history of names URI URI URI URI URI URI
  51. 51. Generating simpler descriptions - From Event-centric model to Snapshot model - • Just show the current names ltk:Change HigherTaxon ex:reclass1 rdf:type cka:Relationship Evolution rdfs:subClassOf ltk:higherTaxon cka:relation ltk:higher Taxon ex:event1 cka:interval “t1” “t2” tl:beginsAt DateTime tl:endsAt DateTime ex:Auidae_2 ex:Xus_1 ex:Xus_1 ex:Buidae_1 ex:Auidae_2 cka:assures (OPR) (opr) (event) (con) (con) (con) (con) (con) rule Event-Centric Model Snapshot Model ex:inv1 ex:inv1 “t1” “t2” tl:endsAt DateTime tl:beginsAt DateTime (the name of the graph) (named graph) URI URI URI URI URI
  52. 52. Linked Taxonomic Knowledge Linked Taxonomic Knowledge Concept Nyctea scandiaca Preview: Present status Expired Entered 1826 Expired 1999 Input Concept Time point View Information: Subject Predicate Object species:Nyctea_scandiaca_1826 skos:prefLabel "Nyctea scandiaca" species:Nyctea_scandiaca_1826 dct:isVersionOf ltk:Nyctea_scandiaca species:Nyctea_scandiaca_1826 rdf:type lodac:Species species:Nyctea_scandiaca_1826 ltk:higherTaxon genus:Nyctea species:Nyctea_scandiaca_1826 ltk:replacedTo species:Bubo_scandiacus_1999 species:Nyctea_scandiaca_1826 foaf:depiction http://www.natgeocreative.com/comp/ MI/001/1304380.jpg http://rc.lodac.nii.ac.jp/taxon/sp 2014-12-18T02:15:00Z Linked Concepts: Subject Object Date species:Nyctea_scandiaca_1826 ltk:replacedTo species:Bubo_scandiacus_1999 1999 -
  53. 53. Linked Taxonomic Knowledge
  54. 54. Linking “Linked Taxonomic Knowledge”
  55. 55. Challenge #2 Agriculture Ontology
  56. 56. 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”
  57. 57. AGROVOC  Thesaurus AGROVOC organizes words by synonym, narrower/broader, and related relationship. harvesting topping(beets) baling gleaning mechanical harvesting mowing 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.
  58. 58. 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) baling gleaning mechanical harvesting mowing Thesaurus (AGROVOC) . . . harvesting mechanical harvesting manual harvesting [means]. . . Harvest Harvest Harvest Inherit byMachine manually + + relationship between siblings Representation: ”Harvesting” [means][Act] Ontology!
  59. 59.  Define activity concepts  Define hierarchy Seeding: activity to sow seeds on fields for seed propagation. Purpose: seed propagation Place : field Target : seed Act : 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)
  60. 60.  Formalization by Description Logics Crop production activity Crop growth activity purpose:crop production purpose:crop growth Agricultural activity Activity for control of propagation Activity for seed propagation purpose:control of propagation purpose:seed propagation Seeding act : sow target:seed place:field Activity for seed propagation Seeding Designing of Agricultural Activity Ontology(AAO)
  61. 61.  Differentiate concepts by property purpose : seed propagation place : paddy field target : seed act : sow crop:rice purpose : seed propagation purpose : seed propagation place : field target : seed act : sow Agricultural activity >…> Activity for seed propagation > Seeding purpose : seed propagation place : well-drained paddy field target : seed act : sow crop: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)
  62. 62. 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)
  63. 63.  Polysemic concepts [disjunction form] [conjunction form] Pudlling Subsoil breaking PulverizationLand preparation Water retention Activity for water management Land leveling Polysemic relationship Pulverization by harrow purpose : pulverization purpose : water retention purpose : land leveling Definition of agriculture activities with multiple purposes or other properties. Puddling Designing of Agricultural Activity Ontology(AAO)
  64. 64. Water retention Land leveling Pulverization Puddling  Polysemic concepts (Protégé) Designing of Agricultural Activity Ontology(AAO)
  65. 65.  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
  66. 66.  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
  67. 67. Applying Agricultural Activity Ontology  URI Give a unique URI for each concept http://cavoc.org/aao/ns/1/は種
  68. 68. 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 Puddling Seeding … Converting [system] API Puddling Activity and sowing… [system’] Puddling and seeding…
  69. 69. http://cavoc.org/ Common Agricultural VOCabulary Agriculture Activity Ontology (AAO) ver 1.31 http://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,
  70. 70. Challenge #3 Core Vocabulary
  71. 71. Data in Government
  72. 72. Information needed to register new cooperation
  73. 73. Information needed to register new cooperation
  74. 74. Information needed to register new cooperation Managed by multiple agencies Different formats Lack of linkage
  75. 75. Local Government User User Company Company Local Government Government Company Product Name Code Maker Buyer Name Organization Product Name Address Name Code Product Nmae Product Code Price Purcha se 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
  76. 76. 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
  77. 77. Vocabulary structure of IMI • IMI consists of core vocabulary, cross domain vocabulary and domain-specific vocabularies. Core Vocabulary Domain-specific Vocabularies Vocabularies that are specialised for the use in each domain. Eg) number of beds, Schedule. Shelter Location Hospital Station Disaster Restoration Cost Core Vocabulary Universal vocabularies that are widely used in any domain. Eg) people, object, place, date. Geographical Space /Facilities Transportation Disaster Prevention Finance Domain-specific Vocabularies
  78. 78. Image of IMI vocabulary • 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 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 PresentAddr ess AddressType 現住所 - 本籍 AddressType 本籍 - … … … … … … … … … … … … … … … … … … 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 LocationTwoDimensional GeographicCoordinate Equipment Information Spot of Equipment Business Hours Owner Access Availability User Day of Installation Homepage AED Information Type of Pad Expiry date Contact Type Model Number SerialNumber Photo Note Information Source Sample 1 : Definition of vocabulary Sample 2 : Information Exchange Package
  79. 79. 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] – …
  80. 80. Corporate Information portal website Corporate number Corporate Name Corporation Type Area Resource Search Government Registers Applications Gather the data by using IMI based data structure Corporation
  81. 81. Benefit of the website CSV PDF RDF Open Data Other websites New ServicesAPI Knowledge base for all government department
  82. 82. 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 IMI Core Vocabulary Corporate Information Domain Vocabulary• ID • name • abbreviation • alternativeName • status • abstract • contactInformation • relatedOrganization • place • address • representative • dateOfEstablishment • additionalInformation ic:Organization Type • businessDomain • startDateOfFy • noOfMember • agent ic:Business unit Type enhance refer
  83. 83. 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 ISA JoinUp UN CEFACT IMI
  84. 84. Lessons learnt from the challenges
  85. 85. Our Society (real world) Computational World
  86. 86. Our Society (real world) Computational World New Technical development Challenge #1
  87. 87. Our Society (real world) Computational World Forming new knowledge Challenge #2
  88. 88. Our Society (real world) Computational World Forming Structure in Society Challenge #3
  89. 89. Lessons learnt from the challenges The challenges are not just in the computational world rather between the computational and the real worlds even in the real world We must be socio-computer scientists
  90. 90. 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.
  91. 91. 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)

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