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Linked Data past, present and futures

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Keynote presentation at ESSnet conference

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Linked Data past, present and futures

  1. 1. Linked Data past, present and futures Pierre-Yves Vandenbussche ESSnet May 27th 2019 @pyvandenbussche
  2. 2. Introduction – Python notebook 2 +
  3. 3. Outline • 1. Semantics on the Web − Vision and construction • 2. Linked Data: Pragmatic use • 3. Promising futures 3
  4. 4. 1. Semantics on the Web Vision and construction 4
  5. 5. Creation of the Web (1989) 5 https://www.w3.org/History/1989/proposal.html • A “Graph” view of the Web with semantic relations between documents and entities
  6. 6. Theory towards the Semantic Web (1994) https://www.w3.org/Talks/WWW94Tim/ https://videos.cern.ch/record/2671957 • “Flat” view of the Web for a computer. No typed relations • A Web for agents • Coordination needed:
  7. 7. Construction of the Semantic Web (1999-2012) 7 • SPARQL – Querying • OWL – Formal semantic • RDF-S – Semantic • RDF – Representation
  8. 8. 2. Linked Data Pragmatic use of SW 8
  9. 9. 9 Adoption technology ≠ Original thoughts
  10. 10. Shift from Semantic Web to Linked Data • Ontology / AI implication fails to address most of real world data that is uncertain, incomplete, inconsistent and includes errors − Relation between concepts can be defined as logical entailments in a formal system (Student(?x) => Person(?x)) • Linked Data − Emphasis on sharing the information in form of a graph − Facilitating data integration through common vocabularies with light formal commitment − Constraints o ShEx (extension for wikidata May 2019) / SHACL 10
  11. 11. 2012 2015 Peak of inflated expectations Trough of disillusionment Shift from Semantic Web to Linked Data • Reflected in industry − Search Engines: o Google 2012 (Peter Norvig) vs o Google 2012 (Ramanathan V. Guha) − Wikipedia (Jimmy Wales) https://www.youtube.com/watch?v=MY4s8uuHmy0 • W3C broaden its Semantic Web activity (https://www.w3.org/2001/sw/) giving rise to the Data Activity / Web of Data (https://www.w3.org/2013/data/) 11 Gartner 2012 and 2015 Semantic Web Linked Data
  12. 12. Linked Data successes /Limitations • Common semantics for a community: − Schema.org: Web pages metadata / Enhanced search engines − Museum and art – Getty − Library – DC/Bibframe − Statistical data – ESSnet! • Linked Open Data − Dbpedia, Wikidata, Eurostat, etc. • Semantic pipeline − BBC − Thomson Reuters • Limitations − Cost / incentive − Use Feedback − Tools and maintenance 12 Jem Rayfield, https://www.slideshare.net/JemRayfield/dsp-bbcjem- rayfieldsemtech2011
  13. 13. The Rise of Graph data • Enterprise Knowledge Graphs − Google Knowledge Vault − Microsoft Academic KG − Facebook Graph Search − … • Graph mining • Convergence with Property Graphs − RDF* (https://www.w3.org/Data/events/data-ws-2019/) 13
  14. 14. 3. Promising futures Knowledge Discovery and Sentient Web 14
  15. 15. Knowledge Discovery 15 • Literature based discovery - Swanson 1980 • Panama papers (2016) − Neo4J − Linkurious • Discovery of Cancer related protein interactions “Artificial Intelligence to win Nobel Prize and Beyond” Hiroaki Kitano - ISWC 2016
  16. 16. Discovery of Cancer related protein interactions 16 Prediction of new phosphorylation relationsData Angiogenesis was expressed in the majority of cases. In CRC, the microvascular density (MVD) was higher than that from ACC. The ratio CD31/CD105 was 1 in ACC and 3 in CRC. VEGF was positive in 25% of ACC and 80% of CRC. In CRC were more mature vessels, marked only with CD31 than immature vessels or endothelial isolated cells marked with both CD31 and CD105. In ACC prevailed the neoformed vessels marked with both CD31 and CD105. 18060184 Unstructured data LOD ESR1 Tamoxifen PIK3CA SGK1 AKT1 PDPK1 Copanislib GSK650394 S102 T291 S74 T37 T65 T369 S529 Known Links Fujitsu Prediction
  17. 17. Sentient Web (Graphs, + IoT + AI/ML) 17 “Ecosystems of services with awareness of the world through sensors, and reasoning based upon graph data & rules together with graph algorithms and machine learning” Dave Raggett (W3C/ERCIM) / Michael N. Huns (University of South Carolina) • Combining symbolic information with statistics based upon prior knowledge and past experience − Large range of reasoning techniques o Deductive, inductive, abductive, causal, counterfactual, temporal, spatial, etc. o Together with efficient graph algorithms − Continuous learning o Heuristics, simulated annealing, reinforcement learning
  18. 18. Thank you Pierre-Yves Vandenbussche @pyvandenbussche
  19. 19. Linked Data example 19 Sovereign State wd:Q3624078 wd:Q219 rdfs:label “Bulgaria”@en “74.61” “8031.59” “7,265,115” “BG” NUTS code wdt:P605 “BG” BULGARIA NUTS code BULGARIA NUTS Region ramon:NUTSRegion eus:geo Participation rates of 4-years-olds in education R05_1 “2012” “79.5” dic/geo#BG

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