1. How to publish in a format that enhances literature-based discovery?
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2. Key question of our work :
How to extract
molecules of knowledge from research results
so that new discoveries can be built from it? ? ?

3. Knowledge Graph of the Literature-Based Discovery

4. Defining molecules of knowledge in the text means:
1. Conceptualizing terms
“cyclooxygenase-2” is a
2. Identifying relationships between terms

5. Information Representation and Operations with Information

6. Linguistic Levels Linguistics Levels
Corresponding Algorithms of Reading and Understanding in Computational Linguistics
Pragmatics
Sentiment analysis
Topic segmentation and recognition
Anaphora resolution
Question answering
Semantics
Named entity recognition (NER)
Word sense disambiguation
Relationship extraction
Syntax
Parse (syntax) tree
Morphology
Stemming
Lemmatization
Part-of-Speech (POS) Tagging
* Line between Pragmatics and Semantics is very blurred

7. Basics Information Retrieval Operates with Syntactical Representation
Document and query are represented in vector space model:
If there is term, then corresponding value in the vector is non-zero
Relevant document ~ higher syntactic (string form)
similarity to the query 1. 2.

8. Semantic Biomedicine Semantic Web Features: 1. Web of Data
2. The same language for data exchange.
3. Relationships and Ontologies.
Royer L., Linse B., Wächter T., Furch T., Bry F., Schroeder M. (2007) Querying Semantic Web Contents. In: Baker C.J.O., Cheung KH. (eds) Semantic Web. Springer, Boston, MA

9. Literature-based discovery
Knowledge extraction
Reasoning
Hypothesis Generation

10. How to publish in a format that enhances literature-based discovery?
That is how search engines see your paper.

11. Standard vs XML-centered Publishing Process
[Bazargan K. A complete end-to-end publishing system based on JATS.

12. Semanticized Paper Publishing Workflow

13. Semanticization: conceptualizing terms and defining relationships

14. Practice. Single paper semanticizing
Step 1. Write research in Google Docs (support of other text editors is upcoming)
Step 2. Send for automatic semanticization
Step 3. Validate results in app.sci.ai

15. Practice. Validation of the semanticization results.
Validate biomedical concepts in the text.
1. It is / it is not a bio object
2. Confirm / Reject
proposed term-to-concept in ontology relationship
3. Add custom ontology concept

16. Practice. Validate Facts Extracted From The Text
Validate and label new facts:
1. Confirm / Reject Facts.
2. Create New Facts.

17. Practice. Single paper knowledge graph

18. Practice. Export semanticized paper in JATS and HTML

19. Recipients of the Publishing Formats
Humans
Machines
HTML
PDF
Printed
JATS
RDF
RDF/XML
CSV / XML / JSON Data
API
That is how search engines see your paper.

20. Extended JATS with biomedical metadata

21. Extended JATS validation
JATS specific validator. XML validator against specific tag set
Generic RDF/XML validator

22. HTML + RDFa with Biomedical Microdata

23. HTML Page Layout
Downloadable version of the paper in JATS, RDF/XML, RDF etc.
HTML with hints for reading

24. How Google reads HTML+RDFa

25. Practice. Publishing preprint. Annotating with hypothes.is

26. How would you use semantic data layer in your research communication?
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28. Bonus. Semantic Biomedicine Fundamentals

29. Bonus. Semantic Biomedicine Applications
Applications and Technologies

30. Nanopublications
Mons, Barend and Velterop, Jan. "Nano-Publication in the e-science era." Paper presented at the meeting of the International Semantic Web Conference, 2009.

31. Bonus Practice. Authors Authentication and Labeling with ORCID ID