The article explains that ontology is not merely a list of terms but a formal model defining concepts, relationships, and constraints, outlines three quality standards, shows how it enables data integration and reasoning, compares it with simple taxonomies, and warns of common misconceptions.
This article explains how ontologies serve as a semantic business map for AI agents, clarifies common misconceptions, shows how to embed an ontology into an agent system, and provides detailed code examples and two practical use‑cases for rule reasoning and multi‑dimensional product classification.
This article breaks down the core of large‑model training by showing that training optimizes neural‑network parameters, that attention is a mechanism realized by those parameters, and that knowledge is encoded implicitly within the weight matrices, providing a clear hierarchy for interview or presentation use.
This article explains how representing multi‑component scientific knowledge as hyperedges, rather than traditional triples, enables large language models to traverse complex material interactions, reduce hallucinations, and generate verifiable experimental designs, demonstrated through a large hypergraph built from thousands of scaffold papers.
This article traces the seventy‑year evolution of Symbolic AI, explains its core physical symbol system hypothesis, contrasts it with connectionist approaches, examines historic milestones such as the Logic Theorist, MYCIN and XCON, discusses the symbol‑grounding problem, and shows how modern neural‑symbolic systems are reviving its relevance in high‑stakes domains requiring accuracy, interpretability and safety.
This article analyzes how combining ontology with large language models can create a new intelligent application development paradigm that unites semantic understanding and executable behavior, proposing a three‑layer architecture, a Model Control Protocol, and real‑world case studies to illustrate its potential and challenges.
This article explores the three stages of AI knowledge representation—vector embeddings, graph‑based structures, and the emerging hybrid approach that combines vectors, graphs, and large language models—to illustrate how modern Retrieval‑Augmented Generation systems achieve both semantic similarity and precise relational reasoning.
This article explores the DIKW hierarchy, explains how data evolves into information, knowledge, and wisdom, examines traditional data models and products, critiques existing multi‑system architectures, and proposes a new distributed Data Warebase that unifies structured, semi‑structured, and vectorized knowledge to enable intelligent data-driven applications.
This article examines how large language models and knowledge graphs complement each other, discussing their respective strengths, integration techniques such as prompt engineering and knowledge editing, and outlining future research directions for building large knowledge models that combine linguistic understanding with structured knowledge representation.
This article introduces the basic concepts of knowledge graphs, explores their research dimensions across knowledge engineering, natural language processing, databases and machine learning, discusses graph database storage models and their integration with artificial intelligence and big data, and presents related projects and real‑world case studies.
This teaching plan outlines a comprehensive Knowledge Graph course covering fundamentals, representation, storage, extraction, reasoning, fusion, question answering, graph algorithms, and emerging technologies across nine detailed chapters, including language integration, ontology matching, and multimodal extensions.
This article outlines the evolution of knowledge graphs, describes typical enterprise application scenarios, compares graph database options such as Neo4j, Cayley and Dgraph, and presents a six‑step methodology for building, storing, and applying knowledge graphs in large‑scale business environments.
The article reviews symbolic knowledge bases such as WordNet, ConceptNet and FrameNet, explains how deep learning replaces them with vector‑based semantic representations, and discusses encoder‑decoder RNNs, attention mechanisms, and future directions for truly understanding language through experiential learning.
