2. Purpose and Context
• Context in Bold Claims
• Understanding many wicked
problems is like understanding
arms races
• Few people win
• The game is to find the
right questions to ask
• All stakeholders must
participate
• Scientific research means
citizens collaborating with
professionals
Solving and
Preventing
Wicked Problems
3. Engage Citizen Scientists Early:
AI Systems as Learning Platforms
• My daughter, Nefer, at around age 8, typed sentences into
OpenSherlock’s predecessor, TSC.
• Sentences like:
• An animal is a living thing
• A mammal is an animal
• A fish is an animal
• Her goal, at that age, was to define a taxonomy and teach
the program where she was in that taxonomy.
• The program said: “Nefer is a fish”.
• She successfully diagnosed her sentences and corrected the
taxonomy.
• At age 15, she coauthored a chapter in a Topic Maps book
about the Linnaean Taxonomy
Nefer Lin Park
Age: Very young
4. Douglas Engelbart:
Knowledge Gardens for All Citizens
• Douglas Engelbart believed that
• People, their Knowledge, and the
Tools they use to collaborate:
• Must co-evolve
• Must network together
• He used the term Dynamic
Knowledge Repository when
talking about that concept.
• The term Dynamic Knowledge Repository
became Knowledge Garden*
*”Knowledge Garden” suggested by Ted Kahn Diagram: Mark Szpakowski
11. AI Sketches
• Distinctions
• Symbolic
• E.g. Expert Systems
• Numeric
• E.g. neural nets
• Hybrid
• Symbolic
• Numeric
• Humans in the loop
12. Generalized Human Capabilities
Human knowledge and
capabilities are nothing like this
curve, but it sketches the idea
that there exists some
generalized ability to remain
competent in a wide range of
topics.
These diagrams are illustrative of
certain concepts, but do not
represent any kind of reality
13. Generalized Expert System Capabilities
Expert Systems have been
shown to be very competent in
some topic, but brittle when
pushed to the edges of a
trained domain
14. Generalized Neural Net Capabilities
Recent advances in neural nets
show enormous potential in
very narrow applications
16. A Use Case: Reading An Information Resource
• A sentence †
• The pandemic of obesity, type 2 diabetes
mellitus (T2DM) and nonalcoholic fatty
liver disease (NAFLD) has frequently been
associated with dietary intake of
saturated fats (1) and specifically with
dietary palm oil (PO) (2). This Photo by Unknown Author is licensed under CC BY-SA
† https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5272194/
17. AI and NLP – Machine Reading
• What is in that sentence to find
• Obesity associated with saturated fats
• Obesity associated with palm oil
• T2DM associated with saturated fats
• T2DM associated with palm oil
• NAFLD associated with saturated fats
• NAFLD associated with palm oil
18. Obesity associated with saturated
fats —In 3 Slides—The Predicate
{
"pred": {
"gramSize": "2",
"sentences": ["ef471f0c-08c9-4097-ac17-354a32944fe1"],
"vers": "1489448293804",
"words": "associated with",
"lexTypes": ["vp"],
"predicateTense": "present",
"id": "27637.27587",
"gramType": "pair",
"lensCodes": ["BioLens"]
},
Predicate Phrase metadatametadata
Side Note:
In OpenSherlock these
blocks of metadata are
called WordGrams
20. The Subject and WordGram Triple Identity
"id": "27000._27637.27587_27738.13882T",
"subj": {
"dbpo": {
"@percentageOfSecondRank": "1.0799338377810428E-21",
"@URI": "http://dbpedia.org/resource/Obesity",
"@support": "3012",
"@surfaceForm": "obesity",
"@offset": "16",
"@similarityScore": "1.0",
"@types": "DBpedia:Disease"
},
"gramSize": "1",
"sentences": ["ef471f0c-08c9-4097-ac17-354a32944fe1"],
"vers": "1489448293875",
"words": "obesity",
"lexTypes": ["n"],
"id": "27000.",
"gramType": "singleton"
}
DBPedia
recognized
a topic
Subject
ID of this Triple
21. Another Use Case: Undiscovered Knowledge
• Undiscovered Knowledge
• Universe of Discourse A: Raynaud’s Syndrome
• Known Rx: Blood Thinners
• Universe of Discourse B: Fish Oil
• Known property: Blood Thinner
• The two domains of discourse do not know about each other
• A Lens on This Situation
• Information Silos
22. On Information Silos
• An insular management system†
• Silo effect arises from
• Silo mentality
• Incompatible data and information systems
• Domain specialization
• …
• Silo effect creates
• Reduced signal to noise ratio in public information systems
• Redundant resources
• Unconnected concepts (dots)
• ... †https://en.wikipedia.org/wiki/Information_silo
Img: Doc Searls:
https://www.flickr.com/photos/docsearls/5500714140
23. Silo Effect: A Complex Example
• Alzheimer’s Context†
• Dr. Trumble and the Tsimané Project‡
• Anthropologist studying evolutionary medicine
• Indigenous people, Bolivia
• Higher elderly cognitive performance with the ApoE4 gene
• Dr. Liddelow studying immune response in brains
• Some people die without dementia but with brains clogged with Alzheimer’s pathology
• A Quote (emphasis mine):
• “I asked Dr. Liddelow whether he was familiar with the Tsimane research. He admitted that he was not
— the field of evolutionary biology is distant from his own. But he said the hypothesis that the ApoE4
gene evolved to protect our brains from the effects of parasitic infection made perfect sense. “That’s
absolutely in line with what we found. For our ancestors, an ApoE4 gene could have been beneficial,”
Dr. Liddelow said, in part because it would have helped the astrocytes go on the attack.”
†
Kennedy, P. (2017, July 14). An Ancient Cure for Alzheimer's? The New York Times.
‡ University of New Mexico. The Tsimane Health and Life History Project. http://www.unm.edu/~tsimane/
24. Literature-based Discovery
• Using literature (documents) to
find new relations in existing
“knowledge” (discovery)
• Mitigate silo effect
• Literature-based Discovery, the
field, was created by the
information scientist Don R.
Swanson in the 1980s.†
• Does not produce new
“knowledge” as would laboratory
experiments Swanson Linking
†see Swanson, Don (1988). "Migraine and Magnesium: Eleven Neglected
Connections". Perspectives in Biology and Medicine. 31 (4): 526–557.
Find Intermediate literature B
which links topic A with topic C
25. Swanson’s ABC Linking Method†
1. Pick a topic of interest (Raynaud’s Disease)
2. Search to find literature A = {Raynaud’s}
3. Hypothesize that B (e.g., blood factors) should be studied in relation to
Raynaud’s
4. Search literature A’ = A ∩ {blood}
5. Notice two common descriptors: blood viscosity, red blood cell rigidity
6. Search literature C = {blood viscosity} U { red blood cell rigidity }
7. Notice the term “Fish Oil”
8. Search literature C = {Fish Oil}
9. Show {Fish Oil} ∩ {Raynaud’s} = { }
10. Show plausible connection between Raynaud’s and Fish Oil
Show the literature has not yet made
the connection (empty set)
†Adapted from: http://www.dimacs.rutgers.edu/~billp/pubs/JASISTLBD.pdf
26. Let’s Revisit the Reynaud’s Example
Photo by Michal Vrba on Unsplash
27. Machine Reading In the Two Silos
• Silo A • Silo B • Machine Reading collects
graph structures from
different sources
• Form tuple-like structures
which are graphs
28. Ways to Federate those Silos?
Photo by You X Ventures on Unsplash
29. Machine Reading: Topic Mapping
• TopicMap Process
• Rule:
• One Location in the Map for each
Subject
• Federates (merges topics about
the same subject) collected from
different resources
• Federation does a Union on
topics from disparate sources
• Federation Intersects silos
around common topics
• Topic Map