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Is knowledge engineering still relevant?

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Presentation at Weizembaum Institute, Berlin, 16/08/2019

Publicado en: Tecnología
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Is knowledge engineering still relevant?

  1. 1. Is knowledge engineering still relevant? Mathieu d’Aquin - @mdaquin mathieu.daquin@insight-centre.org Summary of the talk: yeah, probably...
  2. 2. A quick something about me 2002-2006 2006-2017 2017-now Knowledge representation, case-based reasoning, oncology Ontology engineering, semantic web, linked data, smart cities, education, privacy Data science, data analytics, data and knowledge engineering, data ethics, knowledge graphs
  3. 3. knowledge engineering ?
  4. 4. Knowledge-based systems Knowledge-based system Input Question Problem Situation Answer Inference Solution Decision Knowledge Base
  5. 5. Knowledge engineering Knowledge-based system Input Question Problem Situation Answer Inference Solution Decision Knowledge Base Design, representation, implementation, manipulation, curation and processing of this
  6. 6. Example (oncology) M. d’Aquin et al., Knowledge editing and maintenance tools for a semantic portal in oncology, International journal of human-computer studies 62 (5), 2005
  7. 7. But that’s old stuff, right? Knowledge base Reasoning Oncology expert Knowledge engineer Patient data Treatment Manual effort Classification Machine learning model Training Lots of data about patients and treatments Patient data Treatment
  8. 8. So what else did we use knowledge engineering for? Making knowledge available and machine processable on the Web, i.e. the Semantic Web Ontologies: Knowledge representation with concepts and relations. Web ontologies: Ontologies that live well on the web. Linked data: Data as a graph of web entities, and where the elements (nodes and edges) are defined by web ontologies. Knowledge graphs: See linked data ;)
  9. 9. Shameless plug The semantic web Network of knowledge artefact Linked Data Network of data artefact The Web Network of documents The Internet Network of machines
  10. 10. Example Annotating correspondances from early women philosophers (PhD of Ioana Kyvernitou)
  11. 11. Example Annotating correspondances from early women philosophers (PhD of Ioana Kyvernitou)
  12. 12. Knowledge engineering for the meta-aspects of Data Science Hypo. / Question Plan Collect data Analyse data Extract results Exploit results Data Models New info What- ever was the goal
  13. 13. Anything new? Knowledge engineering for the meta-aspects of Data Science Hypo. / Question Plan Collect data Analyse data Extract results Exploit results Data Models New info What- ever was the goalDataset License Regulation Source Dataset Characteristics Data Science Task Technique Model Model Parameters ... associated with obtained from with derived from used for implemented by using produced version of produced
  14. 14. Propagating data policies Propagation of policies in rich data flows E Daga, M d'Aquin, A Gangemi, E Motta, International Conference on Knowledge Capture, K-CAP 2015
  15. 15. Explaining data patterns Dedalo: Looking for clusters explanations in a labyrinth of linked data, I Tiddi, M d’Aquin, E Motta European Semantic Web Conference, 2014 An ontology design pattern to define explanations, I Tiddi, M d'Aquin, E Motta Proceedings of the 8th International Conference on Knowledge Capture, 2015
  16. 16. Understanding the link between data and what is done with it (PhD Mohamed Adel)
  17. 17. Understanding the link between data and what is done with it (PhD Mohamed Adel)
  18. 18. Making technological artefacts more non-expert friendly An ontology-based approach to improve the accessibility of ROS-based robotic systems I Tiddi, E Bastianelli, G Bardaro, M d'Aquin, E Motta, Knowledge Capture Conference, 2017 Extracting robot’s capabilities through automatically annotating components of ROS (the Robot Operating System) with an ontology
  19. 19. Making technological artefacts more non-expert friendly An ontology-based approach to improve the accessibility of ROS-based robotic systems I Tiddi, E Bastianelli, G Bardaro, M d'Aquin, E Motta, Knowledge Capture Conference, 2017
  20. 20. What about making research data more non-data expert friendly?
  21. 21. Ontologies to represent the base capabilities of datasets
  22. 22. Mapping data access to basic ontology operations [{ "datasetId": "tempo", "load": "pd.read_csv('/home/mdaquin/data/TempoData/Beethoven_Op57_Tempi.csv')", "types": { "performance": { "list": "[{'music': 'Bethoveen Op57', 'name': s.lower()} for s in list(data.columns.values)[:-2]]", "attributes": "{'music': 'music', 'name': 'value', bars: '[peformedBar]'}", "values": "if attribute=='bars':n result=[]n col=0n for x,coln in enumerate(list(data.columns.values)[:-2]):n if coln.lower()==obj['name']:n col=colnn for i in range(0, len(data[col])):n result.append({'bar': {'music': 'Bethoveen Op57', 'number': i}, 'performance': {'music': 'Bethoveen Op57', 'name': obj['name']}})" }, "performedBar": { "list": "[{'bar': {'music': 'Bethoveen Op57', 'number':m}, 'performance': {'music': 'Bethoveen Op57', 'name': n.lower()}} for m in list(range(0,data.shape[0])) for n in list(data.columns.values)[:-2]]", "attributes": "{'bar': 'bar','performance': 'performance','tempo': 'value'}", "values": "result=['unknown']nif attribute=='bar':n result = [obj['bar']]nif attribute=='performance':n result=[obj['performance']]nif attribute=='tempo':n col=0n for x,coln
  23. 23. Ontology-based data access in scratch
  24. 24. Ontology-based data access in scratch
  25. 25. Ontology-based data access in scratch
  26. 26. Ontology-based data access in scratch
  27. 27. Ontology-based data access in scratch
  28. 28. Conclusion Knowledge is, more than ever, a necessary, valuable asset, and some amounts of knowledge engineering can (and is) useful in event the most top-down, data-centric of processes. Need to not only scale, but also make more accessible, and more integrated the tools to enable knowledge curation, knowledge-based explanations/interpretation, and knowledge-driven data access, integration and interpretation. One size does not fit all: querying web polystores, Y Khan, A Zimmermann, A Jha, V Gadepally, M D’Aquin, R Sahay Ieee Access 7, 2019 Towards an ethics by design methodology for AI research projects, M d'Aquin, P Troullinou, NE O'Connor, A Cullen, G Faller, L Holden AAAI/ACM Conference on AI, Ethics, and Society, 2018 Crowdsourcing Linked Data on listening experiences through reuse and enhancement of library data, A Adamou et al. International Journal on Digital Libraries 20 (1), 2019

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