Ontological Prisms : Knowledge Engineering for Enterprise and Agentic Systems.

Format:

Book

Author/Creator:

Fannader, Remy.

Language:

English

Subjects (All):

Ontologies (Information retrieval).

Physical Description:

1 online resource (258 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

Ontological Prisms The expansion of generative AI has highlighted the limitations of purely neural approaches and the benefits of coupling them with symbolic counterparts epitomized by ontologies.

Contents:

Cover

Half Title Page

Title Page

Copyright

Contents

Preamble

About the Author

Part I

Chapter 1: Current Approaches and Paradigm Shift

1.1 Ontologies and Artificial Intelligence

1.1.1 A Brief and Personal Perspective on Artificial Intelligence Past and Present

1.1.1.1 Mechanical Brains

1.1.1.2 Symbolic vs Connectionist Tracks

1.1.1.3 Language and Common Sense

1.1.1.4 Mimicry and Rote Learning

1.1.1.5 Agents in No Mind's Land

1.1.2 An Ontological Manifesto

1.1.2.1 Facts and Data

1.1.2.1.1 Facts Are Not Information

1.1.2.1.2 Neural Connectors Represent Facts, Not Meanings

1.1.2.2 Language and Reason

1.1.2.2.1 Information Is Not Knowledge

1.1.2.2.2 Language Is Not Knowledge

1.1.2.2.3 There Is No Truth in Languages

1.1.2.2.4 There Is No Reason in Data

1.1.2.3 Judgment and Computation

1.1.2.3.1 Reason Is Not Judgment

1.1.2.3.2 Judgments Begin Where Algorithms End

1.1.2.3.3 Knowledge Graphs Are Not Knowledge

1.1.2.3.4 Intelligence Cannot Emerge from Canned Words

1.2 Ontologies and Knowledge

1.2.1 Foundational Ontologies

1.2.1.1 Unified Foundational Ontology

1.2.1.2 Descriptive Ontology for Linguistic and Cognitive Engineering

1.2.1.3 Typical Extensions

1.2.1.3.1 Unified Foundational Ontology Extensions

1.2.1.3.2 Basic Formal Ontology

1.2.1.3.3 Tropos

1.2.1.3.4 Common Core Ontology

1.2.1.4 Limits of Foundational Ontologies

1.2.1.4.1 Foundational Blind Spot

1.2.1.4.2 From Dimensions to Concepts

1.2.1.4.3 Ontologies Are Not Models

1.2.2 Empirical Approaches and Meta-models

1.2.2.1 Domain-specific Ontologies

1.2.2.2 Abstractions, Reuse, and the Limits of Meta-models

1.2.3 What to Expect

1.3 The Ontological Prisms Paradigm

1.3.1 Ontologies and Semiotics

1.3.2 Ontologies and Philosophy

1.3.3 Ontologies at Work.

1.3.4 Process and Case Study

1.3.4.1 Physical

1.3.4.2 Functional

1.3.4.3 Organizational

1.3.5 M*R/K and Ontological Prisms

Key Points

Chapter 2: Ontological Realms

Overview

2.1 Extensional Realm: Facts

2.1.1 From Things to Facts

2.1.1.1 Collecting Facts

2.1.1.2 The Naming Game

2.1.1.3 The Semantics of Connectors

2.1.2 Facts, Data, Modalities

2.1.2.1 Facts and Data

2.1.2.2 Facts and Modalities

2.1.3 Facts and Models

2.1.3.1 Data Models

2.1.3.2 Generative Language Models

2.2 Intensional Realm: Concepts

2.2.1 Relinquishing Aristotelian Ur-elements

2.2.2 Concepts Modalities

2.2.3 Concepts and Models

2.2.3.1 Entity-Relationship Models

2.2.3.2 Conceptual Graphs

2.2.3.3 Knowledge Graphs

2.3 Logical Realm: Categories

2.3.1 Anchors and Connectors

2.3.1.1 Anchors and References

2.3.1.2 Logical Connectors

2.3.1.3 Powertypes

2.3.2 Aspects

2.3.2.1 Objectives

2.3.2.2 Taxonomy and Syntax

2.3.2.3 Ontological Constraints

2.3.2.4 Aspects and Relational Schemas

2.4 Ontological Modalities

2.4.1 Meta-models vs Modalities

2.4.1.1 Objective

2.4.1.2 The Underlying Flaw of Meta-models

2.4.2 A Taxonomy of Ontological Modalities

2.4.2.1 Intrinsic Modalities

2.4.2.1.1 Temporality

2.4.2.1.2 Instantiation

2.4.2.1.3 Realm

2.4.2.1.4 Identity

2.4.2.1.5 Veracity

2.4.2.2 Functional Modalities

2.4.2.2.1 Structures

2.4.2.2.2 Agency and Communication

2.4.2.3 Modalities and Ontological Commitments

Chapter 3: Ontological Prisms and Interoperability

3.1 Alignment and Integration

3.1.1 Homogeneous Alignment

3.1.1.1 Alignment Principles

3.1.1.2 Limits of Alignment and Integration

3.1.2 Integration of Heterogeneous Ontologies

3.1.2.1 Ontological Gears

3.1.2.1.1 Thesauri

3.1.2.1.2 Taxonomies.

3.1.2.1.3 Domains

3.1.2.2 Semantic Layers and Interoperability

3.2 Interoperability of Heterogeneous Ontologies

3.2.1 Interoperability and Reuse

3.2.2 Facts, Concepts, Meanings

3.2.2.1 Abstraction: Entity Resolution

3.2.2.2 Realization: The Matter of Names

3.2.3 Facts, Categories, Digital Twins

3.2.3.1 Interoperability with Engineering Solutions

3.2.3.1.1 From Categories to Facts: Unified Modeling Language

3.2.3.1.2 From Facts to Categories: Object-role Modeling

3.2.3.2 Abstraction and Artifacts

3.2.3.2.1 The Matter of Time

3.2.3.2.2 Schemas and Reverse Engineering

3.2.3.3 Realization of Artifacts

3.2.3.4 Viable Systems and Digital Twins

3.2.4 Concepts, Categories, Organization

3.2.4.1 Abstraction: Alignment of Emerging Changes

3.2.4.2 Realization: Alignment of Designed Changes

3.2.4.3 Architectures: Alignment of Time Frames

3.2.4.4 Transparency, Traceability, Accountability

3.3 Interoperability and Collective Intelligence

3.3.1 Pushing the Edges, Moving the Lines

3.3.1.1 Perceptions

3.3.1.2 Projections

3.3.1.3 Experience

3.3.2 Collective Intelligence and Holistic Knowledge

3.3.2.1 Interoperability and Holistic Knowledge

3.3.2.1.1 Nonseparability: Symbolic vs Connectionist Knowledge

3.3.2.1.2 Causality: Semantic Layers and Reasoning Tiers

3.3.2.1.3 Maturity: Knowledge Modalities

3.3.2.2 Mechanical Brains vs Collective Minds

Chapter 4: Ontological Prisms and Governance

4.1 Environments and Organizations

4.1.1 Environments: Data and Documents

4.1.2 Enterprise Architecture: Information

4.1.2.1 Technical Integration

4.1.2.2 Functional Integration

4.1.3 Organization: Knowledge

4.1.3.1 Enterprise Level

4.1.3.2 System Level

4.1.3.3 Learning Organization

4.2 Data and Value Chains

4.2.1 Data and Metadata.

4.2.1.1 Datasets

4.2.1.2 Documents

4.2.1.3 Metadata

4.2.1.3.1 Objectives

4.2.1.3.2 Representation

4.2.1.3.3 Data Products

4.2.2 Knowledge and Value Chains

4.2.2.1 Added Value

4.2.2.1.1 Perception and Added Value

4.2.2.1.2 Projection and Added Value

4.2.2.1.3 Experience and Added Value

4.2.2.2 Transaction Costs and Value Chains

4.2.2.2.1 Transaction Costs

4.2.2.2.2 Outsourcing Knowledge-intensive Activities

4.2.2.3 Documents and Blockchains

4.2.2.3.1 Documents as Currency

4.2.2.3.2 Blockchains

4.3 Data Protection and Intellectual Property

4.3.1 Data Protection

4.3.1.1 Principles

4.3.1.2 Institutional Approach: General Data Protection Regulation

4.3.1.3 Ontological Approach

4.3.2 Intellectual Property

4.3.2.1 Creative Works and Copyrights

4.3.2.2 The Quagmire of Patents

4.3.2.3 Intellectual Property and Generative Artificial Intelligence

4.4 Complexity Management

4.4.1 The Fabric of Complexity

4.4.1.1 2D Complexity

4.4.1.2 3D Complexity

4.4.2 Organization and Complexity

4.4.2.1 Organization and Complexity Management

4.4.2.2 Ontological Complexity and Systems Entropy

4.4.3 Complexity and Evolution

4.4.3.1 Natural Evolution

4.4.3.2 Evolution of Organizations

4.4.3.3 Sustainability and Resilience Through Ontological Prisms

Part II

Chapter 5: Ontologies and Language

5.1 Anatomy of Languages

5.1.1 Communication vs Representation

5.1.2 Communication: Signs and Symbols

5.1.3 Representation: Grammars and Semantics

5.2 Generative Language Models

5.2.1 A Functional Perspective

5.2.2 Limits of Generative Language Models

5.2.2.1 Functional Limits of Generative Language Models

5.2.2.2 Inherent Limits of Pure Generative Language Models

5.2.3 Agents and Collaborative Architectures.

5.2.3.1 Graph-enhanced Retrieval-augmented Generations

5.2.3.2 Language Models and Ontological Prisms

5.2.3.3 Knowledge-driven Architectures

5.3 Languages and Knowledge

5.3.1 Direct and Mediated Communication

5.3.2 Language of Thought and the Computation Paradigm

5.3.3 Conversational vs Contextual Semantics

5.3.3.1 Extensional Semantics

5.3.3.2 Intensional Semantics

5.3.3.3 Semantic Layers Revisited

5.3.4 Language at the Edges of Knowledge

5.3.4.1 Meanings and Semantic Entropy

5.3.4.2 Languages and Emerging Concepts

Chapter 6: Reasoning with Ontological Prisms

6.1 Ontologies and Reason

6.1.1 The Scope of Reason

6.1.2 Reasoning Paths

6.2 Formal Reasoning

6.2.1 Reason and Logic

6.2.1.1 Nominal Logic

6.2.1.2 Propositions and Predicates

6.2.1.3 Predicates and Categories

6.2.2 Explanation and Comprehension

6.2.2.1 Correlations

6.2.2.2 Causations

6.2.2.3 Interpretations

6.2.2.4 Explanation vs Comprehension

6.3 Language Models and Logic

6.3.1 Semantic Fault Lines and Fuzzy Reasoning

6.3.1.1 Lightweight Ontologies

6.3.1.2 Language Concept Models and Fuzzy Reasoning

6.3.2 Hallucinations and Nominal Reasoning

6.3.2.1 Hallucinations

6.3.2.2 Nominal Embeddings and Vector Semantics

6.4 Empirical Reasoning

6.4.1 Data Analytics

6.4.1.1 Reality and Causal Chains

6.4.1.2 Shared Vocabularies and Aspects

6.4.2 Arguing with Facts

6.4.2.1 Empirical Challenges

6.4.2.1.1 Unreliable Data

6.4.2.1.2 Misleading Information

6.4.2.1.3 Subjective Beliefs and Intents

6.4.2.2 Argumentation Profiles

6.4.2.2.1 Evidence Reasoning

6.4.2.2.2 Interpretative Reasoning

6.4.2.2.3 Controversial Reasoning

6.5 Reason and Knowledge

6.5.1 Actionable Knowledge

6.5.1.1 Declarative Knowledge Representation

6.5.1.2 Heterogeneous Reasoning.

6.5.1.3 Rules Representation.

Notes:

Electronic book.

Description based on publisher supplied metadata and other sources.

ISBN:

9781394352630

OCLC:

1601989968