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From Knowledge Bases to Knowledge Infrastructures for Intelligent Systems

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Presentation at Amazon Cambridge, UK, on 18/05/2017

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From Knowledge Bases to Knowledge Infrastructures for Intelligent Systems

  1. 1. From Knowledge Bases to Knowledge Infrastructures for Intelligent Systems Mathieu d’Aquin Professor of Informatics, Insight Centre, NUI Galway, Ireland @mdaquin - mdaquin.net
  2. 2. Old-School Knowledge-Based Systems Cancer treatment guidelines Formalised Knowledge Base Interactive Decision Support Knowledge encoding and representation Reasoning and interrogation
  3. 3. Scale…. Data Information Know- ledge Machines
  4. 4. Scale… Data Information Know- ledge Machines storage interrogation processing and analysis reasoning and decision
  5. 5. The Web of Data
  6. 6. The Web of Data
  7. 7. The Web of Data Gene Ontology FMA OntologyLODE BIBO Geo Ontology DBPedia Ontology Dublin Core FOAF DOAP SIOC Music Ontology Media Ontology rNews
  8. 8. Need a different kind of intelligent systems
  9. 9. Watson Semantic Web Search Engine http://watson.kmi.open.ac.uk/
  10. 10. Watson Semantic Web Search Engine Accessing ontologies on the semantic web through smart APIs - making it possible to build intelligent systems using online ontologies as their knowledge bases.
  11. 11. Example application - search
  12. 12. Example application - ontology edition
  13. 13. Example application - ontology matching
  14. 14. Example application - question answering
  15. 15. Shameless Plug...
  16. 16. Scale… Data Information Know- ledge Machines storage interrogation processing and analysis reasoning and decision Watson: An attempt to scale up the knowledge level
  17. 17. Next step Data Information Know- ledge Machines storage interrogation processing and analysis reasoning and decision Using the knowledge level... To make large scale information and data levels more exploitable
  18. 18. Example: The MK Data Hub Data Infrastructure for the city of Milton Keynes, enabling sharing and consuming varied and diverse city scale data.
  19. 19. Example applications
  20. 20. Example: MK Insight
  21. 21. But… a large number of datasets for a large number of applications MK Data Hub Analytics Integration Curation Storage Import Sensor Data Local Stats Gov. Open Data ... Mobile Apps Dashboards Business Intelligence Social Web Apps ...
  22. 22. Data cataloging needs to do more... Data cataloging component to index data based on their provenance, categories, format, existing use, etc. But needs to do more to answer questions such as : - Can I use those data for a commercial application? Do I need to attribute somebody? Even after processing? - What can this data do? What kind of things I can apply on it?
  23. 23. Ontological approach to data policies Explicit, semantic representation of the licences attached to data As well as the data flows through which they are processed.
  24. 24. Automatic propagation of policies through dataflows
  25. 25. Understanding what data can answer Example of using formal concept analysis to extract relevant questions from an RDF (graph) dataset.
  26. 26. Ongoing work on generating interactive interface to ontology-based data Servicecode Area Restaurant Organisation isa population deprivation locatedIn rating employee Person The population of Walnut Tree is 4096 What is the population of Walnut Tree?
  27. 27. Towards populating ontologies based on dialog Mood Good Mood Bad Mood Very Bad Mood Very Good Mood isa isa isa isa excellent type horrible type bad OK good typetype type better worse inverseOf better TT better better better Thanks! I don’t know “great”, is it better or worse than “OK”? ... Alexa, tell moody that I’m feeling great!
  28. 28. Towards the automatic exploitation of data Example, in autonomous agents, using an ontology that provides a typology of datasets and of data analytics techniques, making them better able to automatically exploit the data they come across.
  29. 29. Conclusion Knowledge representation and ontology engineering have gone a long way from top down, closed, domain centric knowledge-based systems. From encoding expert knowledge to dealing with scale, variety and diversity. Now, becoming central in the necessary automation of information processing, making data analytics and mining more directly accessible, with fewer bottlenecks.
  30. 30. Contact: @mdaquin - mdaquin.net

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