Machine Reading: What it means for publishers?
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A talk on machine reading and knowledge graphs and what it means for publishers
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Machine Reading: What it means for publishers?
- 1. MACHINE READING WHAT DOES IT MEAN FOR PUBLISHERS? Paul Groth (@pgroth) Disruptive Technology Director labs.elsevier.com #SSP2015 Big Literature, Big Usage
- 2. “THE AUTOMATIC, UNSUPERVISED UNDERSTANDING OF TEXT” Etzioni, Oren, Michele Banko, and Michael J. Cafarella. "Machine Reading." AAAI. Vol. 6. 2006.
- 5. D. A. Ferrucci “Introduction to this is Watson” 10.1147/JRD.2012.2184 356
- 10. From Kuansan Wang: https://mix.office.com/watch/17q8a2u1ahep
- 14. MACHINE READING
- 16. MACHINE READING Etzioni, Oren, Michele Banko, and Michael J. Cafarella. "Machine Reading." AAAI. Vol. 6. 2006.
- 20. Figure 1. Schematic representation of the PaleoDeepDive workflow. Peters SE, Zhang C, Livny M, Ré C (2014) A Machine Reading System for Assembling Synthetic Paleontological Databases. PLoS ONE 9(12): e113523. doi:10.1371/journal.pone.0113523 http://127.0.0.1:8081/plosone/article?id=info:doi/10.1371/journal.pone.0113523
- 22. SOME THINGS TO THINK ABOUT
- 23. THINK PAPERS NOT PAPER
- 25. AUTOMATE AND THEN CURATE WORK
- 26. MULTIPLE FORMATS
- 27. SAVE EVERYTHING 99% of query logs contain entities 25% of queries contain new terms Your data scientists love data
- 29. 6 THINGS 1. Think papers not paper 2. Augment your content 3. Automate then curate 4. Embrace multiple formats 5. Save everything 6. A PDF download isn’t the only APP Machine reading is the automated, unsupervised understanding of text.
- 30. A NOTE ON PRODUCTIVITY Benjamin F. Jones The Burden of Knowledge and the ‘Death of the Renaissance Man’: Is Innovation Getting Harder? “if one is to stand on the shoulders of giants, one must first climb up their backs, and the greater the body of knowledge, the harder this climb becomes.” - Jones
Editor's Notes
- Knowledge graphs are becoming integral to search
- Qustion answering
- We do data integration but for single domains. Deep high quality data Is important
- Source centric approach
- Source centric approach
- Source centric approach
- Yield centric approach. We use frequencies to be able to “the automatic, unsupervised understanding of text”
- A great database but manually curated
- Automatically build such a database
- Data is open
- If you’re publisher to be part of these systems
- blender
- We curate and then automate
- xml
- Content snippets, you have key data!
- Scholarly productive is not growing fast enough