1. Ontology Summit Synthesis
29 March 2017

2. Summit theme: Relationships between AI and ontology
Tracks focus on 3 specific relationships
Track A: Using Automation and ML to Extract Knowledge and Improve Ontologies (Learning →Ontology)
Track B: Using background knowledge to improve machine learning results (Ontology→Learning)
Track C: Using ontologies for logical reasoning (Ontology→Reasoning)

3. Track A, B, C What was covered in Session 1
Key points from session Chat Log etc.
Material synthesized in Track blog page

4. Track A: 8 March 2017 Track Co-champion Gary Berg-Cross
Estevam Hruschka (Associate Professor at Federal University of Sao Carlos DC-UFSCar & adjunct Professor at Carnegie Mellon University)
“Never-Ending Language Learning (NELL)”
Valentina Presutti, (Semantic Technology Laboratory of the Institute of Italian National Research Council (CNR)),
“Semantic Web machine reading with FRED”
Alessandro Oltramari (Research Scientist at Bosch)
"From machines that learn to machines that know: the role of ontologies in machine intelligence"

5. Track B: 15 March 2017 Co-champions: Mike Bennett, Andrea Westerinen
Track B Session 1 Content
SimonDavidson, Psonify
The Investigator's Toolkit – Deriving Immediate, Actionable Insights from Unstructured Data
Ken Baclawski (College of Computer and Information Science, Northeastern University)
Combining Ontologies and Machine Learning to Improve Decision Making

6. Track C: 22 March 2017 Co-Champions: Donna Fritzsche and Ram D. Sriram
Eugen Kuksa, University of Magdeburg, Germany.
“Reasoning with Ontologies in Ontohub”
Pascal Hitzler, Wright State University in Dayton, Ohio, U.S.A.
“On the Roles of Logical Axiomatizations for Ontologies”
 Jans Aasman, Franz Inc.
“Cognitive Probability Graphs need an Ontology”

7. Track A Report-back

8. Track B Report-back

9. Track C Report-back

10. Discussion and Common Themes

11. Synthesizing All That

12. Adjourn

13. ANNEX: Possible things to ruminate on
Use Cases
Regulatory parsing / action
Chatter / malfeasance and intel
Company filings
Questions
Natural language analysis
Kidsn of ontology needed
What’s generated (Track A) and how deep os the meaning
What’s needed to enhance – and how Context, heteronymy handled
Humans in the loop
Logical formalisms
Compared to other SW use cases, do these applications require the same kinds of formalisms?
Is DL a limitation or a requirement?
Statistics / clustering v deeper taxonomies
The role of reasoning