1) The document discusses developing cognitive agents to improve deep question answering and discovery by coupling two platforms: the Berkeley Data Analytics Stack (BDAS) for big data analysis and SolrSherlock for literature-based discovery. 2) It describes how these agents could harvest and represent patterns, contexts, and relations from literature to discover new processes and connections between concepts. 3) The goal is to augment existing methods by allowing hypothesis formation and evidence gathering across both structured data and unstructured literature at scale.

1. Towards Cognitive Agents for
BigData Discovery
Finding Solutions to Complex, Urgent Problems
Jack Park
BigData Science Meetup
Freemont, CA: 19 April, 2014
Shyam Sarkar, Organizer
© 2014, TopicQuests Foundation
Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

2. A Narrative Arc

3. Context: Two Kinds of Discovery
• Data-based
– Harvesting nuggets from collected data
• Literature-based: Deep Question Answering
– Discovering connections between dots in the
literature

4. Target: Deep Question Answering
Breadth
Depth
Information
Retrieval
Semantic
Representation
Goal
Diagram adapted from a talk by Percy Liang at
Stanford, 20140407

5. Our Goals
• Improve Human-Tool Capabilities
• Augment existing analytic methods
– Increase opportunities for discovery
– Improve already sophisticated methods
“Discovery consists of
seeing what everybody
has seen and thinking
what nobody has
thought.”
–Albert Szent-Györgyi

6. Our Approach
• Explore and develop the technologies of so-
called Cognitive Agents
– Current examples
• IBM’s Watson
• SIRI
• An opportunity
– Couple two platforms
• Berkeley Data Analytics Stack (BDAS)
• SolrSherlock

7. Berkeley Data Analytics Stack
Deep QA Issues*
• Low latency queries
– Perform faster inferences
– Explore larger spaces
– Better decisions
• Sophisticated analysis
– Better forecasts
– Better decisions
• Unification of existing data computation models
– Integrate interactive queries, batch and streaming
processing
*http://strata.oreilly.com/2013/02/the-future-of-big-data-with-bdas-the-berkeley-data-analytics-stack.html

8. An Observation
In this context,
interesting literature is
about the social lives
of data

9. Literature-based Discovery
• Forming bisociative links* between information in
different literature sources which are not known
to be related
• Swanson example (simplified)**:
– Literature associated with Raynaud’s
• Raynaud’s therapy linked to blood thinners
– Literature associated with fish oils
• Fish oil linked to blood thinners
– “Blood thinners” as an implicit link between fish oil
and Raynaud’s Syndrome
• Akin to the wormholes formed by tags on web pages or
hashtags
*Arthur Koestler (1964). The Act of Creation
** Swanson, Don (1986) "Fish oil, Raynaud's syndrome, and undiscovered
public knowledge." Perspectives in Biology and Medicine 30(1): 7-18.

10. Cognitive Agents
• Examples
– Proprietary
• IBM’s Watson
• SIRI
• SRI’s CALO
– Part of which: IRIS, was made open source as OpenIRIS
• Others…
– Open Source
• Cougaar
– http://www.cougaar.org/
• Open Cog
– http://opencog.org/
• Open Advancement of Question Answering Systems
– Closely related to IBM’s Watson
– http://oaqa.github.io/
• SolrSherlock
– http://debategraph.org/SolrSherlock
• Many others…

11. Use Cases for Big Data Harvesting
• Resource Collection
– Federation
• bring together and organize without filters
• Resource Augmentation
– Tagging
– Annotating
– Debate
• Knowledge Cartography
– Connecting resources
– Map maintenance
– More Debate
• Research Augmentation
– Crowd-sourced discovery
– Harvesting
– Automated inferences /reasoning
– Knowledge sharing
Federated
Information
Resources
Harvesting
Activities
Adapted from http://www.slideshare.net/jackpark/big-datasciencemeetup-final Slide 29
Harvesting
Activities
Harvesting
Activities

12. A Strong Conjecture
• A Knowledge Federation’s topic map provides
a Rosetta Stone-like substrate
– Reasoning by analogy
– Big Data mined for clues
– Map:
• Where we have been
• Where we haven’t (Dragons be here)
Adapted from http://www.slideshare.net/jackpark/big-datasciencemeetup-final Slide 33

13. Topic Maps for Knowledge Federation
• Maintain well-organized by topic structure
• Key issue:
– For any given information resource added to a
map:
• Agents must answer this question:
– Have I seen this before by any other name or description?

14. Are We There Yet?
• We are now at the edges of discovery:
– Deeper ways of representing
– Deeper ways of knowing
• Relational Biology

15. Relational Biology
• Paraphrasing Nicholas Rashevsky*:
– We can tease open a living cell and count all its
components, but we cannot put it back together
and we have no clue why
• Interpreting Robert Rosen**:
– Rashevsky’s quest for a relational mathematics for
biology (complex systems) entails topological
algebras (Category Theory)
• Category theory is said to facilitate modeling
the social lives of members of the categories
*http://en.wikipedia.org/wiki/Nicholas_Rashevsky
**http://en.wikipedia.org/wiki/Robert_Rosen_(theoretical_biologist)

16. Relational Modeling 1
• Starts with Ontologies
– Ontologies grant uniform vocabularies to
universes of discourse
• Including describing data
– Ontology-based frameworks provide ways to
model social and other relational structures
• SIOC: Semantically Interlinked Online Communities*
• SWAN: Semantic Web Applications in Neuromedicine**
*http://www.sioc-project.org/
**http://www.w3.org/TR/hcls-swan/

17. SIOC Closer Look
• A way to model
components entailed by a
situation (blog post in this
case)
– Uniform vocabulary
– Structural relations
• Creates a foundation for
much deeper modeling
– Including:
• Other ontologies
• Other structures
• Feedback loops
SIOC Blog Post*
*http://rdfs.org/sioc/spec/

18. Massive Connectivity and Feedback
http://geography.oii.ox.ac.uk/?page=home
Complex Communication Processes

19. Feedback Loops: Crucial to Learning
Image: FEDERAL HEALTH FUTURES SUMMIT LEADERSHIP LEARNING for TRANSFORMATIONAL
CHANGE. September 10-11, 2012 Washington DC Metro Region Page 23

20. Relational Biology: Context
• Context is about Relations among the
components themselves
• Context is about Relations among the
components and their environment
• Context is about Feedback

21. Example from Breast Cancer 1
Extracellular Matrix (EM) as Context
Complex Communication Processes
Milk producing tissue
http://www.ted.com/talks/mina_bissell_experiments_that_point_to_a_new_understanding_of_cancer

22. Example from Breast Cancer 2
Cells missing their EM Cells with restored EM
http://www.ted.com/talks/mina_bissell_experiments_that_point_to_a_new_understanding_of_cancer

23. Towards Cognitive Agents
• Harvest and represent
– Patterns
• Actors
• Relations
• States
– Context in which patterns exist
• Discover
– Processes
– Unrecognized connections
– …

24. Watson’s Architecture* (Simplified)
• Analysis determines
answer type and topics in
play
• Hypothesis formation
seeks candidate answers
from sources
– Pattern matching
• Hypothesis scoring
weighs evidence for each
hypothesis
• Answer ranking uses
models to select answer
Question
Analysis
Answer
Sources
Evidence
Sources
Hypothesis
Formation
Hypothesis
Scoring
Answer
Ranking
Answer
*http://www.aaai.org/Magazine/Watson/watson.php

25. SolrSherlock Architecture (Simplified)
Topic Map
Conceptual Graphs
Harvested Documents
Harvester:
HyperMembrane
Information Fabrics,
Agents
Literature-based
Discovery:
Process documents
into structures
(information fabrics)
from which patterns
are harvested.
Federate Data
Analysis with
Literature:
Federate Data
Observations and
predictions with
concepts and relations
harvested from the
literature
Model Processes,
Structures, and
Analogies

26. SolrSherlock Component Diagram
Topic
Map
Conceptual
Graph
Information
Fabrics
TSC
TM
Provider
CG Provider
Machine
Reader
TSC
Provider
Open DeepQA Harvester
PersistenceProvidersAgents

27. Looking Forward
• Coupling Literature-based research with
BigData analysis
– Common ontologies
– Hypothesis formation
– Evidence gathering
– Relation discovery

28. Completed Representation
antioxidants
kill
free radicals
Contraindicates
macrophages use
free radicals to
kill bacteria
Bacterial Infection Antioxidants
Because
Appropriate For
Compromised Host
Let us co-create Cognitive Agents for Discovery
jackpark@topicquests.org
Thanks to Martin Radley , Patrick Durusau Sherry Jones, and Mark
Szpakowski for valuable comments
SolrSherlock at:
http://debategraph.org/SolrSherlock and https://github.com/SolrSherlock