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HyperMembrane Structures for
Open Source Cognitive Computing
Japanese Agency for Science and Technology
Tokyo, Japan
3 Mar...
The Present Situation
Upon this gifted age, in its dark hour,
Rains from the sky a meteoric shower
Of facts . . . they lie...
Topics To Cover
• Discovery, learning, problem solving
• Topic Maps
• OpenSherlock
• HyperMembranes
• Open Source
• Key re...
Cognitive Computing: My View
• Cognitive Computing is:
– Far less about what a computer knows
– Far more about how compute...
A Domain-specific Problem Statement
• An Example:
– Do these two sentences say the same thing?
• CO2 is a causal factor in...
A Framing Thought
• From [1]
– The understanding of global brain organization
and its large-scale integration remains a ch...
Our Goals
• Improve Human-Tool Capabilities
• Augment existing analytic methods
– Increase opportunities for discovery
– I...
Discovery
• Is it really possible for people to see
everything?
– Part of discovery is connecting dots not
yet connected.
...
Related Work
• Commercial
– IBM Watson
– Wolfram Alpha
– Viv
– Saffron 10
– Clueda
– Siri
– Google Now
– Cortana
– …
• Ope...
Biologically Inspired Design
• Humans are blessed with:
– Memory to keep concepts organized and
connected
– Internal mecha...
Memory: Introducing Topic Maps
• A Topic Map is like a library without all the books*
– A Topic Map is indexical
• Like a ...
TopicMap Structure
•Topics as Actors
•Topics as Relations
•Topics as Types
•Topics as Biographies
12
Processing Mechanisms
• Typically, software processes take the form of
variants of NLP (natural language processing)
– Par...
A Key Question in My Research
• Can a Topic Map learn (construct itself) by “reading” literature?
– Relevant issues:
• Boo...
A Simple Example
• Read this sentence:
– Gene expression is caused by insoluble hormones
binding to a plasma membrane horm...
Introducing OpenSherlock
• OpenSherlock is:
– A Topic Map for information resource identity and organization
– A HyperMemb...
Observations 1
• A Topic Map is central to the key question, and
therefore to a thesis entailed by this research
– It serv...
Observations 2
• A Topic Map is necessary but not sufficient to support
discovery, learning, or problem solving
– It reall...
A Research Question
• What processes are available which, if
performed while harvesting (reading)
documents, can reduce th...
A Working Hypothesis
• Process
– Build and maintain a content-addressable memory
of questions, claims, arguments, and evid...
Key Concept: HyperMembrane
• HyperMembrane is a key concept in the
working hypothesis that OpenSherlock seeks
to explore a...
Machine Reading in OpenSherlock
• Goals:
– Grow the topic map
• Topic Map then serves to support fabrication of higher-ord...
Sentence Reading
• First Step:
– Process sentence into word grams*
• Second Step:
– Where possible
• Transform word grams ...
Process Sentence into WordGrams
• Approach
– Break sentence into word grams*
• WordGram objects are shared across sentence...
Transform WordGrams to N-Tuples
• Normalized tuple (N-Tuple)
– A structure where the subject, predicate, and object are no...
About N-Tuples
• An N-Tuple is a structured record of
– Topics in the topic map
– Those topics are harvested from text
• A...
N-Tuples as HyperMembrane
Tuples
{A, Bind, B}
{{A, Bind, B}, Cause, X}
{X, Bind, D}
{{X, Bind, D}, Cause, Y}
27
A B
Bind
X...
Current State of OpenSherlock
ElasticSearch
Titan
or
Blazegraph
Ontology
Importer
Ontologies
PubMed
Reader
PubMed
Abstract...
Observations 3
• HyperMembrane is a reminding system
– HyperMembrane is a record of federated human
conversation
• Harvest...
TellAsk Interface
Conversation Tree
User can click a
node to select as
parent for any
user response
Response Type Selector...
The Open Source Stack
• Persistence
– ElasticSearch
– Considering Titan
– Considering Blazegraph (Bigdata™ RDF Store)
• Li...
Summary
32
Current State of Development
• Aim to answer simple questions about
casuality
– Current focus on biomedical domain
– Curre...
Future Work
• Aim to complete an anticipatory system
– Process models for anticipation
– Conceptual graphs
– Probabilistic...
Why Do This?
• Augment human capabilities in problem
solving
• Participate in Open Science
35
Augmenting Social Sensemaking
1
2
3
Creating Ideas
Refining Connections
Connecting Ideas
Cancer patient
36
Participate in Open Science
37
Key Context for Open Science
• A planet-wide, collaborative quest for Global
Thrivability*.
– Issues include
• Sociologica...
Completed Representation
antioxidants
kill
free radicals
Contraindicates
macrophages use
free radicals to
kill bacteria
Ba...
Prochain SlideShare
Chargement dans…5
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HyperMembrane Structures for Open Source Cognitive Computing

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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.

Publié dans : Technologie
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HyperMembrane Structures for Open Source Cognitive Computing

  1. 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. 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 2
  3. 3. Topics To Cover • Discovery, learning, problem solving • Topic Maps • OpenSherlock • HyperMembranes • Open Source • Key reasons for building open source cognitive systems 3
  4. 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. 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. 5
  6. 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. 6
  7. 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 7
  8. 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 8
  9. 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 – … 9
  10. 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 10
  11. 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 11
  12. 12. TopicMap Structure •Topics as Actors •Topics as Relations •Topics as Types •Topics as Biographies 12
  13. 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 13
  14. 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 14
  15. 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 } 15
  16. 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 16
  17. 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,…) 17
  18. 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 – … 18
  19. 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 • … 19
  20. 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 20
  21. 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 21
  22. 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 22
  23. 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 23
  24. 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/ 24
  25. 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 25
  26. 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 26
  27. 27. N-Tuples as HyperMembrane Tuples {A, Bind, B} {{A, Bind, B}, Cause, X} {X, Bind, D} {{X, Bind, D}, Cause, Y} 27 A B Bind X Y D Cause Bind Cause
  28. 28. Current State of OpenSherlock ElasticSearch Titan or Blazegraph Ontology Importer Ontologies PubMed Reader PubMed Abstracts HyperMembrane Engine TellAsk UMLS Importer UMLS 28
  29. 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 29
  30. 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 30
  31. 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 31
  32. 32. Summary 32
  33. 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 33
  34. 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 34
  35. 35. Why Do This? • Augment human capabilities in problem solving • Participate in Open Science 35
  36. 36. Augmenting Social Sensemaking 1 2 3 Creating Ideas Refining Connections Connecting Ideas Cancer patient 36
  37. 37. Participate in Open Science 37
  38. 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 38
  39. 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 39

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