context, perspective, and generalities in a knowledge ontology · 2016-12-07 · context,...

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


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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


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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


14

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

http://cognonto.com/use-cases/document-specific-word2vec-training-corpuses/




http://cognonto.com/use-cases/text-classification-using-esa-and-svm/



http://cognonto.com/use-cases/dynamic-machine-learning/





http://cognonto.com/use-cases/leveraging-kbpedia-aspects/



http://cognonto.com/use-cases/benefits-from-extending-kbpedia-with-private-datasets/




http://cognonto.com/use-cases/mapping-external-data-and-schema/




http://cognonto.com/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


17

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/

http://cognonto.com/docs/kko-upper-structure/






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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/







21

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/







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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


28

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


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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 ?

https://plato.stanford.edu/entries/events/


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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


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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


35

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


36

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


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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’

http://www.mkbergman.com/1981/cognonto-is-on-the-hunt-for-big-ai-game/

http://www.mkbergman.com/1984/the-irreducible-truth-of-threes/

http://www.mkbergman.com/1932/a-foundational-mindset-firstness-secondness-thirdness/

http://www.mkbergman.com/1985/threes-all-of-the-way-down-to-typologies/

http://www.mkbergman.com/1958/a-speculative-grammar-for-knowledge-bases/

http://www.mkbergman.com/1927/how-fine-grained-can-entity-types-get/

http://www.mkbergman.com/1952/rationales-for-typology-designs-in-knowledge-bases/

http://www.mkbergman.com/1905/a-partial-taxonomy-of-machine-learning-features/

http://www.mkbergman.com/1964/gold-standards-in-enterprise-knowledge-projects/

http://www.mkbergman.com/1876/natural-classes-in-the-knowledge-web/


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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

http://cognonto.com/

https://github.com/Cognonto/kko

http://www.mkbergman.com/category/kbai

http://structureddynamics.com/

context, perspective, and generalities in a knowledge ontology · 2016-12-07 · context,...

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