The Knowledge                   Reengineering Bottleneck                                       Rinke Hoekstra             ...
Knowledge Engineering                         “Critical scientific problem [...] successful applied AI requires that       ...
‣ The lack of adequate and appropriate hardware                         ‣ Lack of cumulation of AI methods and techniques ...
Knowledge Acquisition Bottleneck                         “The problem of knowledge acquisition is the critical bottleneck ...
The Dark Agesvrijdag 24 februari 12
Knowledge Elicitation                         Repertory Grids                         Think Aloud Method                  ...
Knowledge Elicitation                         Repertory Grids                         Think Aloud Method                  ...
Knowledge Elicitation                         Repertory Grids                         Think Aloud Method                  ...
Knowledge Elicitation                         Repertory Grids                         Think Aloud Method                  ...
How to build            the right            ontology?vrijdag 24 februari 12
How to build            the right     Methodologies                          Middle Out Approach                          ...
How to build            the right                                                     Methodologies                       ...
How to build            the right                                                     Methodologies                       ...
How to build            the right                                                              Methodologies              ...
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/vrijdag 24 februari 12
Linked                                                                                                                    ...
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
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The Knowledge Reengineering Bottleneck

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Keynote talk at CSHALS 2012 in Boston on the Knowledge Reengineering Bottleneck: knowledge engineering in the linked data age.

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The Knowledge Reengineering Bottleneck

  1. 1. The Knowledge Reengineering Bottleneck Rinke Hoekstra rinke.hoekstra@vu.nl VU University Amsterdam/University of Amsterdamvrijdag 24 februari 12
  2. 2. Knowledge Engineering “Critical scientific problem [...] successful applied AI requires that knowledge move from the heads of experts into programs” FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.xvrijdag 24 februari 12
  3. 3. ‣ The lack of adequate and appropriate hardware ‣ Lack of cumulation of AI methods and techniques ‣ Shortage of trained knowledge engineers ‣ The problem of knowledge acquisition ‣ The development gap Problems of Knowledge Engineeringvrijdag 24 februari 12
  4. 4. Knowledge Acquisition Bottleneck “The problem of knowledge acquisition is the critical bottleneck problem in artificial intelligence” FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.xvrijdag 24 februari 12
  5. 5. The Dark Agesvrijdag 24 februari 12
  6. 6. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting ...vrijdag 24 februari 12
  7. 7. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting MYCIN and GUIDON ... Knowledge Typesvrijdag 24 februari 12
  8. 8. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting MYCIN and GUIDON ... Knowledge Types CommonKADS Engineering Methodology Problem Solving Methods Domain Modelsvrijdag 24 februari 12
  9. 9. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting MYCIN and GUIDON ... Knowledge Types CommonKADS Engineering Methodology Problem Solving Methods Domain Models Ontolingua “Explicit specification of a shared conceptualization” Sharing ontologiesvrijdag 24 februari 12
  10. 10. How to build the right ontology?vrijdag 24 februari 12
  11. 11. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2vrijdag 24 februari 12
  12. 12. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 Top Ontology Ontology Types Representation Ontology Generic Ontology Top Foundation Core Ontology Generic Domain Domain Ontology Application Corevrijdag 24 februari 12
  13. 13. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 Top Ontology Ontology Types Representation Ontology Generic Ontology Top Foundation Core Ontology Generic Domain Domain Ontology Application Core Principles OntoClean Ontology vs. Epistemologyvrijdag 24 februari 12
  14. 14. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 Top Ontology Ontology Types Representation Ontology Generic Ontology Top Foundation Core Ontology Generic Domain Domain Ontology Application Core Principles OntoClean Ontology vs. Epistemology Ontology Reuse Merging & Alignment Modularization Ontology Design Patternsvrijdag 24 februari 12
  15. 15. Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/vrijdag 24 februari 12
  16. 16. Linked LOV User Slideshare tags2con Audio Feedback 2RDF delicious Moseley Scrobbler Bricklink Sussex Folk (DBTune) Reading St. GTAA Magna- Lists Andrews Klapp- tune stuhl- Resource NTU DB club Lists Resource Tropes Lotico Semantic yovisto John Music Man- Lists Music Tweet chester Hellenic Peel Brainz NDL (DBTune) (Data Brainz Reading subjects FBD (zitgist) Lists Open EUTC Incubator) Linked Hellenic Library Open t4gm Produc- Crunch- PD Surge RDF info tions Discogs base Library Radio Ontos Source Code Crime ohloh Plymouth (Talis) (Data News LEM Ecosystem Reading RAMEAU Reports business Incubator) Crime data.gov. Portal Linked Data Lists SH UK Music Jamendo (En- uk Brainz (DBtune) LinkedL Ox AKTing) FanHubz gnoss ntnusc (DBTune) SSW CCN Points Thesau- Last.FM Poké- Thesaur Popula- artists pédia Didactal us rus W LIBRIS tion (En- (DBTune) Last.FM ia theses. LCSH Rådata reegle research patents MARC AKTing) (rdfize) my fr nå! data.gov. data.go Codes Ren. NHS uk v.uk Good- Experi- Classical List Energy (En- win flickr ment (DB Pokedex Norwe- Genera- AKTing) Mortality BBC Family wrappr Sudoc PSH Tune) gian (En- tors Program MeSH AKTing) semantic mes BBC IdRef GND CO2 educatio OpenEI web.org SW Energy Sudoc ndlna Emission n.data.g Music Dog VIAF EEA (En- Chronic- Linked (En- ov.uk Portu- Food UB AKTing) ling Event MDB AKTing) guese Mann- Europeana BBC America Media DBpedia Calames heim Ord- Recht- Wildlife Deutsche Open Revyu DDC Openly spraak. Finder Bio- lobid Election nance legislation Local nl RDF graphie Resources NSZL Swedish Data Survey Tele- data Ulm EU New Book Project data.gov.uk graphis bnf.fr Catalog Open Insti- York Open Mashup Cultural tutions Times URI Greek P20 UK Post- Burner Calais Heritage codes DBpedia ECS Wiki statistics lobid GovWILD data.gov. Taxon iServe South- Organi- LOIUS BNB Brazilian uk Concept ECS ampton sations Geo World OS BibBase STW GESIS Poli- ESD South- ECS Names Fact- (RKB ticians stan- reference ampton data.gov.uk book Freebase Explorer) Budapest dards data.gov. NASA EPrints uk intervals Project OAI Lichfield transport (Data DBpedia data Guten- Pisa Spen- data.gov. Incu- dcs RESEX Scholaro- ISTAT ding bator) Fishes berg DBLP DBLP uk Geo meter Immi- Scotland of Texas (FU (L3S) Pupils & Uberblic DBLP gration Species Berlin) IRIT Exams Euro- dbpedia data- (RKB London TCM ACM stat lite open- Explorer) NVD Gazette (FUB) Gene IBM Traffic Geo ac-uk Scotland TWC LOGD Eurostat Daily DIT Linked UN/ Data UMBEL Med ERA Data LOCODE DEPLOY Gov.ie CORDIS YAGO New- lingvoj Disea- (RKB some SIDER RAE2001 castle LOCAH CORDIS Explorer) Linked Eurécom Eurostat Drug CiteSeer Roma (FUB) Sensor Data GovTrack (Ontology (Kno.e.sis) Open Bank Pfam Course- Central) riese Enipedia Cyc Lexvo LinkedCT ware Linked PDB UniProt VIVO EURES EDGAR dotAC US SEC Indiana ePrints IEEE (Ontology totl.net (rdfabout) Central) WordNet RISKS (VUA) Taxono UniProt US Census EUNIS Twarql HGNC Semantic Cornetto (Bio2RDF) (rdfabout) my VIVO FTS XBRL PRO- ProDom STITCH Cornell LAAS SITE KISTI NSF Scotland Geo- GeoWord LODE graphy Net WordNet WordNet JISC (W3C) (RKB Climbing Linked Affy- KEGG SMC Explorer) SISVU Pub VIVO UF Piedmont GeoData metrix Drug ECCO- Finnish Journals PubMed Gene SGD Chem Munici- Accomo- El AGROV Ontology TCP Media dations Alpine bible palities Viajero OC Ski ontology Tourism KEGG Ocean Austria Enzyme PBAC Geographic Metoffice GEMET ChEMBL Italian Drilling OMIM KEGG Weather Open public Codices AEMET Linked MGI Pathway schools Forecasts Data Open InterPro GeneID Publications EARTh Thesau- KEGG Turismo rus Colors Reaction de Zaragoza Product Smart KEGG User-generated content Weather DB Link Medi Glycan Janus Stations Product Care KEGG AMP UniParc UniRef UniSTS Government Types Italian Homolo Com- Yahoo! Airports Museums pound Ontology Google Gene Geo Art Planet National wrapper Chem2 Cross-domain Radio- Bio2RDF activity UniPath JP Sears Open Linked OGOLOD way Life sciences Corpo- Amster- Reactome dam medu- Open rates Numbers Museum cator As of September 2011Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/vrijdag 24 februari 12
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