Semantic Web Applications and Tools for Healthcare and Life Sciences
11th International SWAT4HCLS Conference, Antwerp 2018

Panel Discussion Topics

New in 2018 we are opening up the process of deciding panel discussion topics to the SWAT4HCLS public. We have already we have listed 13 fun topics from a brainstorming session. We want your input on these topics in the form of likes, and comments*. You can also recommend panelists for the topic. Specifically we want your topic suggestions too, so you can add additional topics for others to like or comment on. The overall goal is to engage you is a dialog and together discuss topics of interest to our community leading up to the event in December.

You can tweet this panels page to your friends. Join the discussion !

*The comments will be moderated for unsavoury language / obvious SPAM by a SWAT4HCLS organizer.

32 Comments

  1. “Making all Data FAIR will be more challenging than sequencing the Human Genome”




    10



    2
  2. “Ontologists will never make it out of the Ivory tower”




    6



    0
  3. “Interoperability is a dirty word in Healthcare”




    1



    3
  4. “Deep Learning is a Billion dollar Cul-De-Sac”




    3



    2
  5. “Today’s data privacy policies have irreparably failed future generations”




    2



    2
    • This is a very tricky as we may fail future generations both by limiting too much data use because of privacy policies, or on the other hand, not being bold enough in their use missing important discoveries for the coming future.




      1



      0
    • Failed or not I don’t know. But definitely there is a trade-off between privacy (individuals’ goal) and the ability to mine aggregated information (community’s goal).




      1



      0
  6. “The biggest limitation in industry is that senior decision makers have no understanding of new technologies and aren’t prepared to admit it”




    3



    5
  7. “Semantic publishing is not advancing scientific discovery and never will”




    7



    1
    • I think this is an interesting topic. At the same time, I go against the topic statement.

      Semantic publishing of data supporting scientific articles and Semantic publishing of experimental data is definitely leading to a more transparent and reproducible science. Data is the only proof to all the claims in a publication. Data in life sciences has evolved from excel sheets to databases and to linked data graphs. Semantic fairness of data is a way forward and gives power to do scientific discovery via machine-readability and usability of ML and deep learning.




      0



      0
  8. “Investors are like Magpies, they only collect shiny objects with no understanding of what they are good for”




    0



    5
  9. “The reviewers of my SWAT4HCLS paper were fools and should be ashamed to come to the conference”




    2



    4
    • Make this happen and invite a couple of selected refused papers and another pair of reviewers to also review the paper. Let them discuss their different perspectives. Panelists can wear invisibility cloaks and use voice changers to keep reviewer identity private. Alternatively just ask reviewers to accept that their reviews and identities will be made public. Don’t some publishers already do this ?




      0



      0
  10. If FAIR data is so valuable, why don’t we have companies that make data FAIR as a business, and other companies willing to pay for that?




    8



    0
  11. “People think RDF is a pain because it is complicated. The truth is even worse. RDF is painfully simplistic, but it allows you to work with real-world data and problems that are horribly complicated.” quote from Dan Brickley




    3



    1
    • Very interesting topic. Building SPARQL queries over multiple data graphs is indeed horribly complicated. The big question here is how can we mine information from a data graph without knowing the nitty gritty details about it.




      2



      0
  12. Ten simple rules to make the best Linked Data that we can!




    2



    0
  13. Examples of good linked data resource and how you can be one too.




    1



    1
  14. solr is for search, sparql is for research




    3



    0
  15. I am expecting a comeback for formal logic and ontological knowledge bases, I’m personally very wary of NNs.




    1



    0
  16. The role of Semantic Web Technologies for enabling secondary use of EHR data for Clinical Research and Pharmacovigilance — Current Challenges and Future Outcomes




    5



    0
  17. What will we have first? The Semantic web publishing egg or the AI assisted data integration chicken?




    0



    0
  18. “FAIR is all around: industry fell in love, how to avoid broken hearts?”




    0



    0
  19. “The Semantic Web technologies reached certain maturity, lots of ‘solution’ providers are available, academia (since long ago) and industry (relatively recently) are investing on ‘semantic’ platforms to accelerate their activities. How do you guys (panelists) see the future of those technologies? Has it reached the summit of the hype? is there any disrupting thing coming up?, …”




    0



    0
  20. “What are the key learnings for the Semantic Web community in Life Sciences so far?”




    0



    0

Leave a Reply to Reuben Cancel reply

Required fields are marked *.