context, perspective, and generalities in a knowledge ontology · 2016-12-07 · context,...
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Context, Perspective, and Generalities
in a Knowledge Ontology
TM
Ontolog Forum
Michael K. Bergman
December 7, 2016
© Copyright 2016. Cognonto LLC
2
Outline
I. Genesis
II. What is KBpedia?
III. How is it Constructed?
IV. Why it Offers New Ontological Choices
V. Open Discussion
I. Genesis
TM
© Copyright 2016. Cognonto LLC
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8 Years in Process
2008: UMBEL – reference concepts for Web integration
2008: mapping to Cyc
2009: first typology design (‘SuperTypes’)
2010: mapping to Wikipedia; Wikipedia in KR
2011: my first writings on Charles Sanders Peirce
2011 ff: entity recognition, classification
2013: ‘Aha!’ moment; Cognonto effort begins
2014: re-inspection of UMBEL (Cyc, design, purpose)
2016: first release of Cognonto, KBpedia
© Copyright 2016. Cognonto LLC
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A Growing Fascination with Peirce
Charles Sanders Peirce (“purse”) (1839-1914)
Polymath, philosopher, scientist,
logician, mathematician
John Sowa’s writings
Key contributions (much untranscribed):
Logic of semiosis
Predicate logic, notations
Classification of signs, classification (general)
Universal categories (Firstness, Secondness, Thirdness)
Pragmaticism (Pragmatic Maxim)
Abductive logic
Existential graphs
IMO: Greatest thinker on knowledge and KR
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The ‘Aha!’ Moment
Inconsistent, incoherent Wikipedia categories
Wikipedia bespoke, core knowledge structure in:
DBpedia
Freebase
Google KG, Now
Siri
Big data was a key driver in recent AI breakthroughs
2013: Why not systematize knowledge bases for AI
purposes? KBAI
Intuition:
Multiple KBs
Shared foundation
Fine-grained types (70K +)
IBM Watson
Cortana
Viv
etc.
Need for common schema
Design for AI (features,
structure, KR model)
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Exciting Research and Growth Options
Nearly automatic creation of training sets and
corpuses
Rich structure and feature sets
New AI testbed for knowledge representation (KR)
Integrating graph models with standard KR, AI
Application of abductive logic to learning processes
More powerful basis for data interoperability,
integration
II. What is KBpedia?
TM
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Cognonto Overview
Cognonto = cognition + ontology
= knowledge-based AI (KBAI)
Boutique enterprise services:
Supervised, unsupervised, deep machine learning
Information integration
Recognition, extraction, tagging
Specialty expertise
Three technology components
KBpedia: integration of 6 + 20 KBs
Developing use cases with clients
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KBpedia Knowledge Structure
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20 Other KBs, Vocabularies
Bibliography Ontology
Creative Commons
DBpedia Ontology
Description of a Project
(DOAP)
Dublin Core
Event Ontology
FRBR
Friend of a Friend
Geo
Music Ontology
Open Organizations
Organization Ontology
Programmes Ontology
RSS Ontology
schema.org
SIOC
Time Ontology
TRANSIT
US PTO
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KBpedia Design Basis
Based on triadic logic of C.S. Peirce
Feature-rich KKO structure:
Entities
Attributes
Relations
Events
Written in OWL2:
Reasoning
Inference
SPARQL
Explicitly structured for AI in:
Natural language understanding (NLU)
Feature extraction and generation
Labeling training sets and corpuses
Easily extensible with client data, schema
Types
Concepts
Annotations
Text
Disjointedness
Aggregations
Restrictions
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KBpedia Statistics
Area Value
Knowledge bases Six (6) core 20 extended Domain-specific
Concepts (classes) 39 K ‘core’ reference concepts 138 K in standard Client-specific
Entities 32,000 K standard entities Client-specific
Assertions 3,700,000 K direct 6,500,000 K total (w/ inferred)
Analyzable text
Full articles Descriptions Titles Semsets Links Categories Infoboxes See also Multiple (200+) languages
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KBpedia Use Cases
Document-specific word2vec training corpuses
Text classification using ESA and SVM
Dynamic machine learning using the KBpedia knowledge
graph
Leveraging KBpedia ‘aspects’ to generate training sets
automatically
Benefits from extending KBpedia with private datasets
Mapping external data and schema
For latest list, see Cognonto use cases
III. How is it Constructed?
TM
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Cognonto Technology
Graph management
Tagging
Classification
Mapping
Domain integration
Build, update scripts
Consistency, logic checks
Graph expansion scripts
Bespoke data structures
See text
© Copyright 2016. Cognonto LLC
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KBpedia Knowledge Ontology (KKO)
Upper level of knowledge graph
Based on CSP’s universal categories (Firstness,
Secondness, Thirdness)
A ‘speculative grammar’ geared to KBAI
~ 165 concepts
Tie-in points to ~ 80 typologies (~ 30 “core”)
Open source
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KKO Top Three Branches (structure)
I. Monads
II. Particulars
III. Generals
Monads are the idea space or building blocks of the ontology. Monads
are potentials or possibilities, and are indivisible (‘indecomposable’) in
and of themselves. This category is a Firstness.
Particulars are actual or existing things (‘entities’) or events, also known
as instances or individuals. Particulars become evident through a dyadic
action-reaction relation. This category is a Secondness.
Generals arise from placing particulars into natural classes or types; they
are what mediates the commonalities or ‘laws’ among similar particulars.
Generals are real constructs, though are not actual. New knowledge
arises from generalization. This category is a Thirdness.
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KKO Monads Branch (1ns)
Monads [1ns]
FirstMonads [1ns]
Suchness [1ns]
Thisness [2ns]
Pluralness [3ns]
DyadicMonads [2ns]
Attributives [1ns]
Relatives [2ns]
Indicatives [3ns]
TriadicMonads [3ns]
Representation [1ns]
Mediation [2ns]
Mentation [3ns]
For complete branch: http://cognonto.com/docs/kko-upper-structure/
© Copyright 2016. Cognonto LLC
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KKO Particulars Branch (2ns)
Particulars [2ns]
MonadicDyads [1ns]
MonoidalDyad [1ns]
EssentialDyad [2ns]
InherentialDyad [3ns]
Events [2ns]
Action [1ns]
Reaction [2ns]
Continuous [3ns]
Entities [3ns]
SingleEntities [1ns]
PartOfEntities [2ns]
ComplexEntities [3ns]
For complete branch: http://cognonto.com/docs/kko-upper-structure/
© Copyright 2016. Cognonto LLC
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KKO Generals Branch (3ns)
Generals [3ns] (== SuperTypes)
SignElements [1ns]
AttributeTypes [1ns]
RelationTypes [2ns]
Symbols [3ns]
Constituents [2ns]
NaturalPhenomena [1ns]
SpaceTypes [2ns]
TimeTypes [3ns]
Manifestations [3ns]
NaturalMatter [1ns]
OrganicMatter [2ns]
Symbolic [3ns] For complete branch: http://cognonto.com/docs/kko-upper-structure/
© Copyright 2016. Cognonto LLC
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KBpedia’s Speculative Grammar (1ns)
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KBpedia’s Typologies
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KBpedia’s 32 ‘Core’ Typologies
Natural Phenomena Chemistry Products
Area or Region Organic Chemistry Food or Drink
Location or Place Biochemical Processes Drugs
Shapes Prokaryotes Facilities
Forms Protists & Fungus Audio Info
Activities Plants Visual Info
Events Animals Written Info
Times Diseases Structured Info
Situations Persons Finance & Economy
Atoms and Elements Organizations Society
Natural Substances Geopolitical
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An Expandable Typology Design
Collapsed Tree Expanded Tree
32+ K entity types presently available
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Extending with Domain Schema
Becomes the basis for domain ML
IV. Why it Offers New Ontological Choices
TM
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Context and Perspective
Knowledge is change, dynamic, emergent
Knowledge is meaning
Too many upper ontologies dichotomous:
abstract v tangible
endurant v perdurant
Perspective, context requires a thirdness
particulars v universals
3D v 4D
© Copyright 2016. Cognonto LLC
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Treatment of Events
Are events:
actions ?
particulars ?
objects ?
entities ?
instances ?
See Stanford Encyclopedia of Philosophy’s Events entry
What is relationship of events to actions, activities? the
relationship to predicates?
What is a situtation? what is a state?
properties ?
attributes ?
facts ?
perdurants ?
times ?
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Action Model
Events are particulars (1ns, in a monadic context)
Activities: general, durative events (2ns, in a dyadic context)
Processes: multiple activity durative events (3ns, this context)
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Separation of Dyadic Relations
Attributives
Inherent characteristics of particulars:
• Oneness
• Otherness
• Inherent
Relatives
Non-inherent relationships:
• Concurrents (A:A, mostly, internal ObjectProperties) (generally,
included with Attributes)
• Opposites (A:B, simple external)
• Conjunctives
Indicatives
Non-assertive, but do direct attention:
• Iconic
• Indexical
• Associative
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The Mindset of ‘Thirdness’
Firstness Secondness Thirdness
hic et nunc
quality reaction mediation
one here and now eternal
possibility fact law
inheres adheres coheres
being existence external
purity action conduct
beginning occurrence diffusion
original dependence continuity
feeling consciousness thought
qualia particularity generality
© Copyright 2016. Cognonto LLC
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The Process of Categorization
Determine if existing category needs splitting:
imbalance in size
emergences (!)
If so, look to the 3ns of the category and:
1. Determine the vocabulary (“building blocks”) for the new space
Firstness
2. Determine the particular real things and events for the space
Secondness
3. Determine the laws, regularities, generalities for the new space
Thirdness
4. Name and populate the three new sub-categories
“The fundamental principles of formal logic are not properly axioms, but definitions
and divisions; and the only facts which it contains relate to the identity of the
conceptions resulting from those processes with certain familiar ones.” (CP 3.149)
new mappings
new knowledge
V. Open Discussion
TM
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Additional Potentials
Mapping to more knowledge bases
Exposing more structural features
Peircean-based semantic parsers
ML using graph structure, analytics
Dynamic and reinforcement learning
Continued ‘snake eating its tail’
Further typology structuring of attributes and
relations actual data values
© Copyright 2016. Cognonto LLC
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Issues, Open Topics
Qualifying types by Firstness, Secondness
The application of Thirdness to Firstness and
Secondness
Treatment of dyadic relatives (attributes split)
(Nomenclature and Divisions of Dyadic Relations, 1903)
Treatment of values and quantities
Placement, treatment of ethics and aesthetics (e.g.,
goodness and beauty)
Continued Peircean scholarship further
refinements
© Copyright 2016. Cognonto LLC
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Ten Writings
i. ‘Cognonto is on the Hunt for Big AI Game’
ii. ‘The Irreducible Truth of Threes’
iii. ‘A Foundational Mindset: Firstness, Secondness, Thirdness’
iv. ‘Threes All of the Way Down to Typologies’
v. ‘A Speculative Grammar for Knowledge Bases’
vi. ‘How Fine Grained Can Entity Types Get?’
vii. ‘Rationales for Typology Designs in Knowledge Bases’
viii. ‘A (Partial) Taxonomy of Machine Learning Features’
ix. ‘Gold Standards in Enterprise Knowledge Projects’
x. ‘“Natural Classes” in the Knowledge Web’
© Copyright 2016. Cognonto LLC
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NASCAR Stickers
http://cognonto.com (demo + interactive knowledge graph)
https://github.com/cognonto/kko (KKO)
http://www.mkbergman.com/category/kbai/
http://mkbergman.com
http://fgiasson.com/blog
http://structureddynamics.com
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