Open Source Cognitive Computing: Progress and Prospects
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Progress report on OpenSherlock, including anticipatory story reading, distributed prolog, and epic planetary biographical quests on any topic.
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Open Source Cognitive Computing: Progress and Prospects
- 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 © 2017, TopicQuests Foundation
- 3. Modern Terms for Science • Citizen Science • Public participation in science • Democratization of • Data • Research • Ownership of results • Open Science • Scientists publishing in the open • Data • Software • Lab research • Open access publication • Democratizes research • Popular Related Hashtags • #opensource • #openaccess • #opendata • #opentrials • #openscience • #citizenscience • #citsci • #MachineLearning • #CancerMoonshot • #climatechange • #opengov • #democracy © 2017, TopicQuests Foundation
- 4. Citizen Science • Before Citizen Science • Research conducted in universities and corporations • Medical clinics retained patient data • … • With Citizen Science • Patients own their data • Able to conduct personal research • Human-powered literature-based discovery • Anyone can participate in scientific research © 2017, TopicQuests Foundation
- 5. Some Citizen Science Examples • Quantified Self • http://quantifiedself.com/ • Patients Like Me • https://www.patientslikeme.com/ • Amateur Astronomy • https://en.wikipedia.org/wiki/Amateur_astronomy • Mark2Cure • https://mark2cure.org/ • Foldit • https://fold.it/portal/ • Eterna • http://www.eternagame.org/web/ • Citizen Science Games • http://citizensciencegames.com/ • Citizen Science: How Ordinary People are Changing the Face of Discovery (book) • http://www.carencooper.com/ • Make: (magazine) • http://makezine.com/ © 2017, TopicQuests Foundation
- 6. Engage Citizen Scientists Early: AI Systems as Learning Platforms • My daughter, Nefer, at around age 8, typed sentences into OpenSherlock’spredecessor, 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 © 2017, TopicQuests Foundation
- 7. Douglas Engelbart: Knowledge Gardens for All Scientists • 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 © 2017, TopicQuests Foundation
- 8. On Knowledge Gardens • The term Knowledge Garden is intended to convey a particular sentiment: • The gardening process is useful way to think about communication, including interdisciplinary communication, and learning • Knowledge is inherently social • In a knowledge garden, humans engage in thoughtful collaborative research • A knowledge item (information resource, recorded idea) is inherently connected to many other knowledge items, world wide • It is like a mushroom poking out of mycelium* • Knowledge gardens give knowledge a local social life, and also a global one • Knowledge used to have a social life through humans; now it can have that autonomously, through software agencies, which can explore new relationships. *A mass of branching roots © 2017, TopicQuests Foundation
- 10. What is a Knowledge Garden? • Citizen Science through distributed knowledge gardens • An actively cultivated knowledge ecology/eco-network • Collaborative gardening among citizen and professional scientists • It is persistent, re-visitable, sharable • It is cultivated by ongoing sensitive software agents (Siri, OpenSherlock, and others become your gardeners; User experience in the garden is critical here) • Each citizen gets their personal knowledge garden (they can get their own 24/7 gardening agents for their personal knowledge ecosystem) © 2017, TopicQuests Foundation
- 11. What is Federation? • We speak of federating information resources created during participation in gardens: • Bringing those resources together in a public digital library • On the surface, it is a process which • Brings information resources together • Maintains a topic-centric organization of those resources to improve their findability • It is an enabler of whole ecosystem networking, sharing, and intending • It nudges the ecosystem toward growth directions • It protects an open (not owned by anybody) space for meeting, discovering and innovating © 2017, TopicQuests Foundation
- 16. Responses Leading to New Concepts Compromised Host is new Concept © 2017, TopicQuests Foundation
- 17. A Beneficial Outcome antioxidants kill free radicals Contraindicates macrophages use free radicals to kill bacteria Bacterial Infection Antioxidants Because Appropriate For Compromised Host Co-created resource © 2017, TopicQuests Foundation
- 18. True story behind the Garden Scenario • There were no gardeners • There were no web pages or tags • There was TSC, my predecessor to OpenSherlock • TSC was “taught” about immunology: immune response to bacterial and viral antigen by Robert Trelease from the UCLA Med School • I was the cancer patient in the scenario • TSC’s model of immune response to bacterial infection said: • T-cells use free radicals against bacteria • I made the connection between the model and my high-dose vitamin pills • Me as Citizen Scientist • I chose to do my own research on my visitation • Chronic Myelogenous Leukemia • My diagnosing physician recommended • Bone marrow transplant • I chose not do that because radiation is part of the medical procedure • Research uncovered clinical trials on a different therapy • Interferon: Immune Response Modification • I chose that, but… • My body did not respond the same as those in clinical trials • Research uncovered a possible link between how my body was responding and • Seaonal Affective Disorder (SAD) • Prozac + Interferon finished the job! • I am here to talk about it! © 2017, TopicQuests Foundation
- 19. AI Systems in Citizen Science, Again • Consider the tags used by two individuals from our scenario: • The individuals do not know each other. • It is a serendipitous event that they happened to use the same tag • Free Radical • That lone tag helped another person make a discovery • What if our AI platform could identify topics and add tags to documents and offer other research services? * * © 2017, TopicQuests Foundation
- 20. What, Really, are Tags? • They are Reminders • They are Metadata • They help humans form mental models of subjects • They can be thought of as similar to what Word Vectors do: • Associate words with related attributes and concepts. • We seek increased use of quality tags on documents © 2017, TopicQuests Foundation
- 21. Outline From Here • About OpenSherlock • Big questions asked about my OpenSherlock project • Not to be answered here, just recorded • Questions like these could be asked of any similar project • OpenSherlock Inference Capabilities • OpenSherlock in a Knowledge Garden • Project Status: • Data Preparation • Current Exploration: Anticipatory Story Reading • A Deeper Research Issue: Prolog • Towards Large Scale Engagement in Research © 2017, TopicQuests Foundation
- 22. About OpenSherlock • Started life as The Scholar’s Companion (TSC) • Rational reconstruction of Douglas Lenat’s Eurisko • https://en.wikipedia.org/wiki/Eurisko • Added a variant of Kenneth Forbus’ Qualitative Process Theory • http://www.qrg.northwestern.edu/ideas/qptidea.htm • Began life in 1983 from Lenat’s chapter in the book Machine Learning: An Artificial Intelligence Approach (Volume 1) • http://www.springer.com/gp/book/9783662124079 • Written in Forth under Pascal on a Macintosh • Took a new life as a Topic Map merge platform • Anticipated in my 2010 PhD Thesis Proposal • http://kmi.open.ac.uk/publications/techreport/kmi-10-01 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.560.6742&rep=rep1&type=pdf © 2017, TopicQuests Foundation
- 23. Big Questions Posed for OpenSherlock • How do you navigate large topic maps? • How do you maintain enormous knowledge bases? • How is the OpenSherlock WordGram network similar (or not) with word vectors? • How do you maintain state in conversations with the knowledge base? • How does the knowledge base explain its findings? © 2017, TopicQuests Foundation
- 24. OpenSherlock Inference Capabilities • Simple OpenSherlock Capabilities (prototype) • Simple conversations • Learn from assertions • Answer simple questions • Facts at first • Modus ponens next • Advanced OpenSherlock (directions) • Deep question answering and Literature-based Discovery • Logical deduction • common logic à Conceptual Graphs • Inductive reasoning • Abductive reasoning • Probabilistic reasoning © 2017, TopicQuests Foundation
- 25. OpenSherlock in a Knowledge Garden • Primary tasks • Topic Map maintenance • Identifying topics for merging • For the Federation • For each Topic Garden which uses a topic map • Federation Topic Map creation • Story reading • Background task • Literature-based discovery • Finding “unconnected dots” • Finding contradictions • … • Future tasks • Research assistance • Deep question answering © 2017, TopicQuests Foundation
- 26. An important issue: Data Preparation • Data Types • OWL ontologies • Opensource text books • PubMed abstracts (XML) • PubMed full text (XML) • Target Representation • JSON documents capable of being imported directly into OpenSherlock • Processes • OWL à JSON-LD à JSONdocument à Import into topic map • Textbook PDF à Textbook text à JSONdocument à import into reader • PubMed XML à JSONdocumentà import into reader © 2017, TopicQuests Foundation
- 27. Mapping OWL documents to SubjectProxies – a first example • The TopicQuestsplatform has a requirement to craft SubjectProxy* objects in the federation’s topic map which are directly imported from OWL ontologies. OWL documents are just one of several ways in which domain ontologies are available. • First cut: • Hand-built OWL document readers and mappers • Final cut: • Using open source tools, read OWL documents in and write JSON-LD documents out • JSON-LD provides a simple, intermediate re-representation of structure of OWL documents • Write a single mapper which reads JSON-LD documents and writes JSON files ready to import directly into the topic map. • Not round-trip: no use case for that (yet) • Following slides illustrate this process. *A SubjectProxy is a container for all of the key-value descriptions of a topic in a TopicMap; it is a database object. © 2017, TopicQuests Foundation
- 28. Example: Pizza Ontology to JSON-LD <owl:Class rdf:about="#American"> <rdfs:label xml:lang="pt">Americana</rdfs:label> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="#hasTopping"/> <owl:someValuesFrom rdf:resource="#TomatoTopping"/> </owl:Restriction> </rdfs:subClassOf> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="#hasTopping"/> <owl:allValuesFrom> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <rdf:Description rdf:about="#MozzarellaTopping"/> <rdf:Description rdf:about="#TomatoTopping"/> <rdf:Description rdf:about="#PeperoniSausageTopping"/> </owl:unionOf> </owl:Class> </owl:allValuesFrom> </owl:Restriction> { "@id": "pizza:American", "@type": "owl:Class", "label": { "@language": "pt", "@value": "Americana" }, "subClassOf": [ "_:b284", "_:b285", "_:b286", "_:b212", "_:b287", "pizza:NamedPizza" ], "disjointWith": [ "pizza:QuattroFormaggi", "pizza:AmericanHot" ] } OWL Fragment JSON-LD Fragment © 2017, TopicQuests Foundation
- 31. Anticipatory Story Reading 1 • A working hypothesis • Humans read by forming expectations as they read • What the topic is? • What comes next? • What is the perspective? • LinkGrammar parsing is similar • Each word broadcasts its expectations of what it wants to link with • Parser processing satisfies those expectations • Issue: Anaphora -- pronoun reference back to prior sentence • Expectation Failure • What can we learn from LinkGrammar parsing? © 2017, TopicQuests Foundation
- 32. LinkGrammar Parsing 1 • What is it? • “The Link Grammar Parser is a syntactic parser of English, based on link grammar, an original theory of English syntax. Given a sentence, the system assigns to it a syntactic structure, which consists of a set of labeled links connecting pairs of words. The parser also produces a "constituent" representation of a sentence (showing noun phrases, verb phrases, etc.). “* • Example • Scientists believe that CO2 causes climate change. * http://www.link.cs.cmu.edu/link/ © 2017, TopicQuests Foundation
- 33. LinkGrammar Parsing 2 • The sentence • Scientists believe that CO2 causes climate change • Yields – after mapping: • {Scientists, believe, {CO2, cause, climate change}} • Observations: • LinkGrammar parsing reads a sentence left to right forming expectations on each word • That triggers a new exploration: • PredicateCentricparsing © 2017, TopicQuests Foundation
- 34. Charles Fillmore and Frame Semantics • Frame Semantics as a Predicate Centric reading approach 1. Find predicates first 2. Find subjects and objects around those predicates 3. Apply language and domain models as needed • Explore FrameNet* and VerbNet** for reading • Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing*** *https://framenet.icsi.berkeley.edu/fndrupal/ ** https://verbs.colorado.edu/~mpalmer/projects/verbnet.htm ***http://web.eecs.umich.edu/~mihalcea/papers/shi.cicling05.pdfl © 2017, TopicQuests Foundation
- 35. Anticipatory Story Reading 2 • This is the general OpenSherlock research plan: • Develop PredicateCentric reading tools to grow a large WordGram Graph • Form Triples where possible • {Subject, Predicate, Object} • Form hypotheses at the topic level • Use graph centrality and other graph analysis to validate hypotheses • Track topics to facilitate anaphoric reference resolution © 2017, TopicQuests Foundation
- 36. Ted Nelson and New Computing Cosmologies • A Cosmology for a Different Computer Universe: Data Model, Mechanisms, Virtual Machine and Visualization Infrastructure • https://journals.tdl.org/jodi/index.php/jodi/article/view/131/129 • Introduces ZigZag http://xanadu.com/zigzag/ We believe the computer world can be simplified and unified. Today, ordinary people must deal with an appalling variety of programs and mechanisms to maintain their information. We have discovered a new simplification based on one simple concept: a new, liberated form of data that shows itself in wild new ways. Conventional data structures-- especially tables and arrays-- are confined structures created from a rigid top-down specification that enforces regularity and rectangularity. . But this structure (our trademark is ZigZag®) is created from individual relations, bottom-up; it can be irregular and unlimited. Its uses range from database and spreadsheet to unifying the internals of large-scale software. © 2017, TopicQuests Foundation
- 37. ZigZag® in OpenSherlock • Sentences • (s1) A causes B • (s2) M causes N • (s3) A isA X • WordGrams • A • B • causes • M • N • isA A M XisA causes B N s1 s3 s1 s3 s2 s2 Like ZigZag, there is only one WordGram per specific word or phrase for all sentences read by the system; A sentence’s identity serves as a dimension (labeled arc) in the structure. © 2017, TopicQuests Foundation
- 38. Anticipatory Story Reading 3 • The current OpenSherlockresearch question (as before) • Can a topic map learn to read? • Premise behind that question • Reading performance improves over time • Current research is evolving a PredicateCentric approach • Organize a sentence around its predicates (verbs) • Language and domain models to help where appropriate • Syntax models (Regex rules) which help with words which are not known to the language models • Eventual inclusion of connectionist, e.g. TensorFlow, and other NLP technologies • We then ask: • Can a combination of language models, syntax rules, and pre-defined vocabularies lead to an adaptive reading system (much like a new language learner)? • We aim for a platform which can continuously improve its reading ability © 2017, TopicQuests Foundation
- 39. PredicateCentric: A First Example 1 • Sentence • Some scientists believe that carbon dioxide causes climate change • Verbs known before reading: • believe that and its canonical form believe • causes and its canonical form cause • Nouns known before reading, from a TopicMap: • carbon dioxide • climate change • Determiners known before reading: • some © 2017, TopicQuests Foundation
- 40. PredicateCentric: A First Example 2 • The sentence resolves to this pattern: • D?VNVN (determiner, unknown, verb, noun, verb, noun) • To craft a Triple from this sentence, we need this pattern • NVNVN • We recognize that pattern as a compound triple {S,P,{S,P,O}} • A language rule fires which recognizes this pattern: • D?V • ? à N • The result (from a test): {160._156._8.9_141._10.11TT, scientists, believe, {8.9_141._10.11T, carbon dioxide, cause, climate change}} © 2017, TopicQuests Foundation
- 41. A Deeper Research Issue: Prolog • OpenSherlock is fundamentally a logic-based platform • That opens a research question: • Is Prolog a viable approach to representation of facts and logic in OpenSherlock? • If so: • How to scale Prolog? © 2017, TopicQuests Foundation
- 42. Distributed Prolog 1 • An open question: • Can we adapt a biological model to OpenSherlock?* • Short-term, working memory • Long-term memory • A hypothetical architecture: • Working memory • Available Prolog, e.g. SWI, and others, using in-memory stores • Long-term memory • Distributed Prolog • Foreground process: supporting working memory • Background processes: • Studies on structures • Literature-based discovery • Topic merging • … Whaf if OpenSherlock is implemented in Prolog? *Inspiration in Complimentary Learning https://www.ncbi.nlm.nih.gov/pubmed/7624455 as recently seen in DeepMind https://singularityhub.com/2016/06/19/how-to-build- a-mind-this-learning-theory-may-hold-the-answer/ © 2017, TopicQuests Foundation
- 43. Distributed Prolog 2 • The notion of sharded database, accomplished different ways: • Radovan Zvoncek [1] spoke of using P2P processing to spread unification over several databases. • He used Apache Cassandra [2] and tuProlog[3] • HypergraphDB [4] uses their own open source graph database with tuProlog and P2P • HypergraphDB uses BerkeleyDB for persistence • Perhaps it could be migrated to Cassandra? • A goal: • Explore Prolog on ElasticSearch • ElasticProlog 1: https://fenix.tecnico.ulisboa.pt/downloadFile/395145796597/thesisRadovan.pdf 2: http://cassandra.apache.org/ 3: http://apice.unibo.it/xwiki/bin/view/Tuprolog/WebHome 4: http://www.hypergraphdb.org/ 5: http://www.oracle.com/technetwork/database/database- technologies/berkeleydb/overview/index.html © 2017, TopicQuests Foundation
- 45. Towards Large Scale Engagement in Research • What? • Let us imagine a Grand Epic Quest • One that covers all possible research subjects • Let us imagine the purpose of such a quest is to: • Research, create, and federate biographies * • Organize those biographies in topic maps and historical timelines • A Planetary Timeline • Why? • Discovering biographies is discovering events, processes, causality • Just the biography of humans entails all of science *Inspiration for biographies from James Burke’s Connections © 2017, TopicQuests Foundation
- 46. Planetary Timeline 1 • Let us imagine a global citizen science project: • A timeline of planet-wide history • The project would engage humans, AI systems, and online tools of collaborative inquiry. • The project would provide an opportunity for all participants to discover, debate, and refine our understanding of our shared history. “People generally won’t take drastic action until it is nearly too late, or is indeed too late. We can’t see things coming if we have no precedent for them. * * https://medium.com/@theonlytoby/was-the-russian-ambassador-to-turkey-todays-arch-duke-ferdinand-449b40877d3b#.qi5qtjyb7 http://simile-widgets.org/wiki/Timeline © 2017, TopicQuests Foundation
- 47. Planetary Timeline 2 • Needs • Ability to see and to think clearly about historical events • Ability to understand and reason more clearly about current and future events • Approach • Every historical event has a biography • Use the Internet, AI, and Global Collaboration to collect, debate, and improve biographies • Citizen Science + Professional Science • Crafting biographies (Channeling James Burke’s Connections) • Knowledge Gardening • Benefits • Human engagement with the details and discovered connections • More detailed and accurate always evolving picture of the past • Emergent properties • Increased human awareness • Competition • Personal research • Lectures • The entire Big History project • Why compete? © 2017, TopicQuests Foundation
- 48. Planetary Timeline 3 • Existing related activity • Known as Big History • Has an existing open source project • http://www.chronozoom.com/ • Has an International Association • http://www.ibhanet.org/ • Has a TED Talk • https://www.ted.com/talks/david_christian_big_history • Is at Khan Academy • https://www.khanacademy.org/partner-content/big-history-project • Is at Coursera • https://www.coursera.org/learn/big-history • Has critics • https://www.theguardian.com/commentisfree/2014/sep/10/big-history-bill-gates-uk-state-schools- education © 2017, TopicQuests Foundation
- 49. Planetary Timeline 4 • Parallels between ChronoZoomand a Planetary Timeline project • Open source • Aimed at broad, participatory learning experiences • Possible differences between ChronoZoomand a Planetary Timeline project • Sources of data in the timeline • Teacher/expert vs citizen scientists • Organization and presentation (possible) differences • Planetary Timeline project anticipates the fabrication of intersecting biographies for all subjects © 2017, TopicQuests Foundation
- 50. 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: Yuzuru Tanaka Randy Goebel Bob Gleichauf Mark Szpakowski John Stewart Patrick Durusau Marc-Antoine Parent Ted Kahn Jerry Michalski Alec Wenzowski David Alexander Price ありがとうございます