context, perspective, and generalities in a knowledge ontology

Context, Perspective, and Generalitiesin a Knowledge Ontology

TM

Ontolog Forum

Michael K. BergmanDecember 7, 2016

© Copyright 2016. Cognonto LLC2

OutlineI. GenesisII. What is KBpedia?III. How is it Constructed?IV. Why it Offers New Ontological ChoicesV. Open Discussion

I. Genesis

TM


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


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


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)


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


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


KBpedia Knowledge Structure


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


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


KBpedia StatisticsArea 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


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/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/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


Cognonto Technology

Graph management Tagging Classification Mapping Domain integration

Build, update scripts Consistency, logic checks Graph expansion scripts Bespoke data structures

See text


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


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.


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/


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/



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/



KBpedia’s Speculative Grammar (1ns)


KBpedia’s Typologies


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


An Expandable Typology Design

Collapsed Tree Expanded Tree

32+ K entity types presently available


Extending with Domain Schema

Becomes the basis for domain ML

IV. Why it Offers New Ontological Choices

TM


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


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/


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)


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


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


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

Firstness2. Determine the particular real things and events for the space

Secondness3. Determine the laws, regularities, generalities for the new space

Thirdness4. 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


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


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


Ten Writingsi. ‘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/


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/