Context, Perspective, and Generalities in a Knowledge Ontology Ontolog Forum

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1 Context, Perspective, and Generalities in a Knowledge Ontology TM Ontolog Forum Michael K. Bergman December 7, 2016

2 Outline I. Genesis II. What is KBpedia? III. How is it Constructed? IV. Why it Offers New Ontological Choices V. Open Discussion 2

3 I. Genesis TM

4 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 4

A Growing Fascination with Peirce Charles Sanders Peirce ( purse ) (1839-1914) Polymath, philosopher, scientist, logician, mathematician John Sowa s writings Key contributions (much untranscribed):

5 A Growing Fascination with Peirce Charles Sanders Peirce ( purse ) ( ) 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 5

6 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 IBM Watson Cortana Viv etc. Need for common schema Shared foundation Design for AI (features, Fine-grained types (70K +) structure, KR model) 6

7 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 7

8 II. What is KBpedia? TM

9 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 KBs Developing use cases with clients 9

10 KBpedia Knowledge Structure 10

11 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 11

12 KBpedia Design Basis Based on triadic logic of C.S. Peirce Feature-rich KKO structure: Entities Types Attributes Concepts Relations Annotations Events Text Written in OWL2: Reasoning Disjointedness Inference Aggregations SPARQL Restrictions Explicitly structured for AI in: Natural language understanding (NLU) Feature extraction and generation Labeling training sets and corpuses Easily extensible with client data, schema 12

13 KBpedia Statistics Area Knowledge bases Six (6) core 20 extended Domain-specific Value Concepts (classes) Entities Assertions Analyzable text 39 K core reference concepts 138 K in standard Client-specific 32,000 K standard entities Client-specific 3,700,000 K direct 6,500,000 K total (w/ inferred) Full articles Descriptions Titles Semsets Links Categories Infoboxes See also Multiple (200+) languages 13

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 14

15 III. How is it Constructed? TM

16 Cognonto Technology Graph management Tagging Classification Mapping Domain integration See text Build, update scripts Consistency, logic checks Graph expansion scripts Bespoke data structures 16

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 17

18 KKO Top Three Branches (structure) I. Monads II. 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 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. III. Generals 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. 18

19 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: 19

20 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] For complete branch: 20 ComplexEntities [3ns]

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] For complete branch: 21 Symbolic [3ns]

22 KBpedia s Speculative Grammar (1ns) 22

23 KBpedia s Typologies 23

24 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 24

25 An Expandable Typology Design Collapsed Tree Expanded Tree 32+ K entity types presently available 25

26 Extending with Domain Schema Becomes the basis for domain ML 26

27 IV. Why it Offers New Ontological Choices TM

dichotomous: abstract v tangible particulars v universals

28 Context and Perspective Knowledge is change, dynamic, emergent Knowledge is meaning Too many upper ontologies dichotomous: abstract v tangible particulars v universals endurant v perdurant 3D v 4D Perspective, context requires a thirdness 28

29 Treatment of Events Are events: actions? particulars? objects? entities? instances? properties? attributes? facts? perdurants? times? 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? 29

30 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) 30

31 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 31

32 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 32

33 The Process of Categorization 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) Determine if existing category needs splitting: imbalance in size emergences (!) new mappings new knowledge 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 33

34 V. Open Discussion TM

Additional Potentials Mapping to more knowledge bases Exposing more structural features Peircean-based semantic parsers ML using graph structure,

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 35

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 36

37 Ten Writings i. Cognonto is on the Hunt for Big AI Game ii. iii. iv. The Irreducible Truth of Threes A Foundational Mindset: Firstness, Secondness, Thirdness Threes All of the Way Down to Typologies v. A Speculative Grammar for Knowledge Bases vi. vii. How Fine Grained Can Entity Types Get? 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 37

com/cognonto/kko (KKO) http://www.mkbergman.

38 NASCAR Stickers (demo + interactive knowledge graph) (KKO)

Survey of Knowledge Base Content

Survey of Knowledge Base Content Survey of Content Introduction Fundamental Expression Types Top Level Collections Time and Dates Spatial Properties and Relations Event Types Information More Content Areas Copyright 2002 Cycorp This set