Open source "cognitive computing" systems, specifically OpenSherlock; describes a HyperMembrane structure, a kind of information fabric, for machine reading, literature-based discovery, deep question answering. Platform is open source, uses ElasticSearch, topic maps, JSON, link-grammar parsing, and qualitative process models.

1. HyperMembrane Structures for
Open Source Cognitive Computing
Japanese Agency for Science and Technology
Tokyo, Japan
3 March, 2015
Jack Park
© 2015 TopicQuests Foundation CC by SA 4.0

2. The Present Situation
Upon this gifted age, in its dark hour,
Rains from the sky a meteoric shower
Of facts . . . they lie unquestioned,
uncombined.
Wisdom enough to leech us of our ill
Is daily spun; but there exists no loom
To weave it into fabric
Edna St. Vincent Millay, 1939
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3. Topics To Cover
• Discovery, learning, problem solving
• Topic Maps
• OpenSherlock
• HyperMembranes
• Open Source
• Key reasons for building open source cognitive
systems
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4. Cognitive Computing: My View
• Cognitive Computing is:
– Far less about what a computer knows
– Far more about how computers can
augment human cognitive capabilities
– Based on the J.C.R Licklider and
Douglas Engelbart augmentation work
J.C.R. Licklider
Douglas Engelbart
4Imgs: Wikipedia

5. A Domain-specific Problem Statement
• An Example:
– Do these two sentences say the same thing?
• CO2 is a causal factor in climate change.
• Climate change is caused by carbon dioxide.
• Problem Statement
– Software agents need elegant methods for
reading, representing, organizing, and modeling
information resources to support discovery and
answering questions.
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6. A Framing Thought
• From [1]
– The understanding of global brain organization
and its large-scale integration remains a challenge
for modern neurosciences.
• To
– The understanding of global conversations about
topics that matter and their large-scale federation
remain a challenge for modern information
technology.
[1] Petri G, Expert P, Turkheimer F, Carhart-Harris R, Nutt D, Hellyer PJ, Vaccarino F. (2014)
Homological scaffolds of brain functional networks. J. R. Soc. Interface 11: 20140873.
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7. Our Goals
• Improve Human-Tool Capabilities
• Augment existing analytic methods
– Increase opportunities for discovery
– Improve already sophisticated methods
• Build Looms
– Read documents
– Map and model topics read
– Weave information fabrics
Douglas Engelbart
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8. Discovery
• Is it really possible for people to see
everything?
– Part of discovery is connecting dots not
yet connected.
– “Cognitive Agents” can help increase
chances of serendipity.
“Discovery consists of
seeing what everybody
has seen and thinking
what nobody has
thought.”
–Albert Szent-Györgyi
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9. Related Work
• Commercial
– IBM Watson
– Wolfram Alpha
– Viv
– Saffron 10
– Clueda
– Siri
– Google Now
– Cortana
– …
• Open Source
– OAQA
– DeepDive
– OpenCog
– OpenNARS
– Watsonsim
– YodaQA
– AKSW OpenQA
– AKSW QA
– AquaLog
– OpenSherlock
– OpenIRIS (CALO)
– …
• Research
– Project Aristo
– Project Halo
– FREyA
– CASIA
– NLP-Reduce
– EIS Sina
– WDAqua ITM
– Intui2
– …
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10. Biologically Inspired Design
• Humans are blessed with:
– Memory to keep concepts organized and
connected
– Internal mechanisms which map sensor data into
memory for processing and storage
– The abilities of complex, adaptive, anticipatory
systems
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11. Memory: Introducing Topic Maps
• A Topic Map is like a library without all the books*
– A Topic Map is indexical
• Like a card catalog
– Each topic has its own representation
• Improving on a card catalog, a topic can be identified many
different ways
• Captures metadata and optionally content
– A Topic Map is relational
• Like a good road map
– Topics are connected by associations (relations)
– Topics point to their occurrences in the territory
– A Topic Map is organized
• Multiple records on the same topic are co-located (stored as one
topic) in the map
*a map is not its territory
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12. TopicMap Structure
•Topics as Actors
•Topics as Relations
•Topics as Types
•Topics as Biographies
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13. Processing Mechanisms
• Typically, software processes take the form of
variants of NLP (natural language processing)
– Parsers
– Cluster analysis
– Entity recognition
– Relation detection
– Role recognition
– Probabilistic methods
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14. A Key Question in My Research
• Can a Topic Map learn (construct itself) by “reading” literature?
– Relevant issues:
• Bootstrapping
• Machine reading
– NLP
– Linguistics
– Statistics
– Analogy & Metaphor
– …
• Knowledge representation
• Model building
– Anticipation
• Weaving information fabrics
• Literature-based discovery
• Deep Question Answering
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15. A Simple Example
• Read this sentence:
– Gene expression is caused by insoluble hormones
binding to a plasma membrane hormone receptor
• Topic Map recognizes:
– Gene expression  GeneExpression
– insoluble hormones  InsolubleHormone
– plasma membrane hormone receptor 
PlasmaMembraneReceptor
• Software agents transform:
– is caused by  Cause
– binding to  Binds
• Final semantic structure:
• { {InsolubleHormone, Binds, PlasmaMembraneReceptor},
Cause, GeneExpression }
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16. Introducing OpenSherlock
• OpenSherlock is:
– A Topic Map for information resource identity and organization
– A HyperMembrane information fabric structure
– A society of agents system which can
• Read documents
• Process information resources
– Maintain the topic map
– Maintain the HyperMembrane
– Build and maintain models
– Perform discovery tasks
– Answer questions
– Agents are coordinated by:
• A blackboard system
• A dynamic task-based agenda
• Event propagation and handling
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17. Observations 1
• A Topic Map is central to the key question, and
therefore to a thesis entailed by this research
– It serves as a kind of memory for social processes
– It provides a robust platform for subject identity
– It can also serve as a repository for domain-
specific vocabularies (ontologies, taxonomies,
naming conventions,…)
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18. Observations 2
• A Topic Map is necessary but not sufficient to support
discovery, learning, or problem solving
– It really only provides a powerful indexical structure related to
the key artifacts in any universe of discourse:
• Actors
• Their relations
• Their states
• Rules, laws, theories,…
• To model those key artifacts, other representation
strategies are required
– Conceptual Graphs
– Qualitative Process Theory
– Belief Networks
– …
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19. A Research Question
• What processes are available which, if
performed while harvesting (reading)
documents, can reduce the amount of
processing required later during question
answering?
– The question entails
• Synthesis of ontology
• Co-reference resolution
• Re-representation during question lifting
• …
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20. A Working Hypothesis
• Process
– Build and maintain a content-addressable memory
of questions, claims, arguments, and evidence
fields.
• We call that a HyperMembrane
– Note:
• Every text object passed into the system is processed by
the same algorithms
– Sentences harvested from text
– Questions and responses posed by humans
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21. Key Concept: HyperMembrane
• HyperMembrane is a key concept in the
working hypothesis that OpenSherlock seeks
to explore and demonstrate
– A growing graph as a collection of woven and
intersecting fabrics
• constructed from normalized tuples (n-tuples) which
are designed to reduce the amount of NLP required to
read documents
• such that intersections of fabrics occur where named
entities in the graph of n-tuples are the same
– Inspired by Ted Nelson’s ZigZag Architecture
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22. Machine Reading in OpenSherlock
• Goals:
– Grow the topic map
• Topic Map then serves to support fabrication of higher-order
knowledge structures
– Conceptual Graphs
– Belief Networks
– QP Theory Models
– HyperMembrane
– …
• Process Loop:
– For a given document
• For every paragraph in that document
– For every sentence in each paragraph
» Read the sentence
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23. Sentence Reading
• First Step:
– Process sentence into word grams*
• Second Step:
– Where possible
• Transform word grams into n-tuples**
• n-tuples form the HyperMembrane
* A container of words, from 1 to 8 words per container
** A container of symbols based on words in word grams
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24. Process Sentence into WordGrams
• Approach
– Break sentence into word grams*
• WordGram objects are shared across sentences
– Count of sentence identifiers associated with each object
serves as basis for probabilistic models
– Either
• TopicMap recognizes terms
– Or
• Sentence is parsed by Link-Grammar Parser**
• TopicMap learns from parse results
*http://en.wikipedia.org/wiki/W-shingling **http://www.link.cs.cmu.edu/link/
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25. Transform WordGrams to N-Tuples
• Normalized tuple (N-Tuple)
– A structure where the subject, predicate, and object are normalized
• Nouns and verbs transformed
– CO2, Carbon Dioxide, …  CO2
– causes, is caused by, …  cause
• Two sentence example
– CO2 is a cause of climate change.
– Climate change is caused by carbon dioxide.
– Result:
» { CO2, cause, climate change }
– Normalization processes include general and domain specific lenses
• Rule-based interpreters which detect structures
– Taxonomy
– Causality
– Biomedical
– Geophysical
– …
• Process models
– Built and maintained while reading
– Predict while reading – Anticipatory Reading
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26. About N-Tuples
• An N-Tuple is a structured record of
– Topics in the topic map
– Those topics are harvested from text
• An N-Tuple takes the form:
– { Subject, Predicate, Object }
– Where
• Subject and/or Object can be one of:
– A topic from the topic map
– Another N-Tuple
• An N-Tuple is identified by the identities of the terms it contains
– When thinking in terms of terms (words) read from documents, the identities
(numeric representations) of those terms form the identity of the N-Tuple
object.
• N-Tuples are content addressable
• Disambiguation of subjects is a topic mapping process
– Learning means continuous refinement of subject identity
– Ambiguities can also be solved through human intervention
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27. N-Tuples as HyperMembrane
Tuples
{A, Bind, B}
{{A, Bind, B}, Cause, X}
{X, Bind, D}
{{X, Bind, D}, Cause, Y}
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A B
Bind
X
Y
D
Cause
Bind
Cause

28. Current State of OpenSherlock
ElasticSearch
Titan
or
Blazegraph
Ontology
Importer
Ontologies
PubMed
Reader
PubMed
Abstracts
HyperMembrane
Engine
TellAsk
UMLS
Importer
UMLS
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29. Observations 3
• HyperMembrane is a reminding system
– HyperMembrane is a record of federated human
conversation
• Harvested from books, papers, and recorded
conversation
• Includes statistical properties of recorded utterances
– HyperMembrane records:
• That which is common
• That which is novel
– Possibly wrong
– Possibly game changing
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30. TellAsk Interface
Conversation Tree
User can click a
node to select as
parent for any
user response
Response Type Selectors.
Selection required before
response.
User types here
Linear
conversation flow
Entry Forms Selector List
Map starts a new conversation
with entered topic
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31. The Open Source Stack
• Persistence
– ElasticSearch
– Considering Titan
– Considering Blazegraph (Bigdata™ RDF Store)
• Libraries
– Many from Apache Foundation and others
– LinkGrammarParser (Java version)
– XML PullParser
– Simple JSON Parser
• Tools
– Eclipse
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32. Summary
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33. Current State of Development
• Aim to answer simple questions about
casuality
– Current focus on biomedical domain
– Current focus on two lenses
• Taxonomy
• Casuality
– No Conceptual Graphs
– No Process Models
– No Probabilistic Models
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34. Future Work
• Aim to complete an anticipatory system
– Process models for anticipation
– Conceptual graphs
– Probabilistic models
– More lenses
• Pluggable lenses
• Adaptive lenses
– More domains
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35. Why Do This?
• Augment human capabilities in problem
solving
• Participate in Open Science
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36. Augmenting Social Sensemaking
1
2
3
Creating Ideas
Refining Connections
Connecting Ideas
Cancer patient
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37. Participate in Open Science
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38. Key Context for Open Science
• A planet-wide, collaborative quest for Global
Thrivability*.
– Issues include
• Sociological events
– Health, epidemics, wars,…
• Geophysical events
– Climate change, earthquakes, volcanoes, …
• Astrophysical events
– Asteroids, our Sun. …
* Let’s call the quest: EarthMoonshot
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39. 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
OpenSherlock documents at: http://debategraph.org/OpenSherlock
Code emerging at: https://github.com/opensherlock/
Slides online at http://slideshare.net/jackpark/
Acknowledgments:
Bob Gleichauf
David Alexander Price
Arun Majumdar
Robert S. Stephenson
Mark Szpakowski
Martin Radley
Sherry Jones
Alexander Wenzowski
Ted Kahn
Patrick Durusau
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