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knowledge graphs

36 articles · Page 1 of 1
DataFunTalk
DataFunTalk
Sep 29, 2026 · Artificial Intelligence

Ontology: The Only Blueprint for Enterprise AI Agents — Forbes & China's Convergence

Forbes Technology Council article argues ontology is the sole blueprint for enterprise AI agents, citing Palantir, Databricks, Microsoft, and Glean convergence; Chinese firms across healthcare, industry, and tech independently hit the same wall, with 30+ case studies at DACon 2026 Beijing demonstrating ontology-driven implementations.

China AI implementationDACon 2026Ontology
0 likes · 19 min read
Ontology: The Only Blueprint for Enterprise AI Agents — Forbes & China's Convergence
DataFunTalk
DataFunTalk
Sep 25, 2026 · Artificial Intelligence

Ontology-Driven Agent Control: Semantic Foundations for Harness Engineering

This article explores how ontology-driven architecture provides a semantic foundation for controllable AI agents, detailing the Knora platform's three-layer design that replaces external prompt-based constraints with internalized business rules, enabling precise context retrieval, verifiable feedback loops, and measurable efficiency gains in industrial deployments.

AI AgentsEnterprise AIHarness Engineering
0 likes · 26 min read
Ontology-Driven Agent Control: Semantic Foundations for Harness Engineering
Data Bricklaying Diary
Data Bricklaying Diary
Sep 15, 2026 · R&D Management

Evaluating Ontology Intelligence Projects: 6 Dimensions Beyond Concept Counts

This article argues that ontology intelligence projects must be evaluated on end-to-end business outcomes rather than concept or triple counts, proposing a six-dimension framework—business value, semantic quality, decision reliability, action closure, security governance, and unit economics—with mandatory safety thresholds and phase-gated decisions to expand, optimize, restrict, or stop projects.

KnowledgeOpsaction reliabilitybusiness value
0 likes · 37 min read
Evaluating Ontology Intelligence Projects: 6 Dimensions Beyond Concept Counts
Data Bricklaying Diary
Data Bricklaying Diary
Sep 14, 2026 · Artificial Intelligence

Can LLMs Auto-Build Ontologies? AI Finds Candidates, Business Defines Reality

This article explains how large language models accelerate ontology engineering through candidate discovery, formalization, and validation, but cannot replace human responsibility for defining business objects, rules, and risk boundaries, proposing a seven-step human-AI collaboration process with three-zone isolation to prevent AI from polluting production baselines.

OWLSemantic Webbusiness rules
0 likes · 25 min read
Can LLMs Auto-Build Ontologies? AI Finds Candidates, Business Defines Reality
Data Bricklaying Diary
Data Bricklaying Diary
Sep 10, 2026 · Backend Development

Terminology Normalization Beyond Similarity: Coverage, Routing, Drift & Regression Testing

This article explains why terminology normalization requires a continuous governance framework covering candidate recall, risk-based routing, manual review, drift attribution, and regression testing — not just similarity scores — and details metrics, routing rules, drift types, acceptance criteria, and a minimum viable loop for production systems.

candidate recalldata governancedrift detection
0 likes · 18 min read
Terminology Normalization Beyond Similarity: Coverage, Routing, Drift & Regression Testing
AI Engineer Programming
AI Engineer Programming
Sep 3, 2026 · Artificial Intelligence

Your AI Agent Doesn't Need to Traverse Graphs: Lookup vs. Path

The article argues that most enterprise AI agents don't need to traverse graph databases; instead, they need curated context (definitions, join keys, governance rules) to write SQL directly. It distinguishes Lookup questions (known paths) from Path questions (where the path is the answer), showing that Lookup dominates and graph traversal adds latency and cost without benefit.

AI AgentsGraph DatabasesGraphRAG
0 likes · 20 min read
Your AI Agent Doesn't Need to Traverse Graphs: Lookup vs. Path
Data Bricklaying Diary
Data Bricklaying Diary
Aug 24, 2026 · Fundamentals

Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily

This article explains that ontology modeling uses familiar business objects, attributes, relationships, and constraints from daily data and process work, but adds cross-system unified semantics, explicit computable constraints, machine verification, and continuous operations to enable data, rules, systems, and AI agents to collaborate on a shared business world.

AI readinessData ModelingOWL
0 likes · 20 min read
Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily
DataFunSummit
DataFunSummit
Jul 17, 2026 · Artificial Intelligence

Ontology: The Semantic OS for Large‑Model AI, Not a Repackaged Knowledge Graph

At a closed‑door OpenKG × DataFun session the authors argued that enterprises now lack a unified, computable, evolvable semantic layer—not model capability—and that ontology, re‑imagined as a semantic operating system, can bridge business, data and AI, though organizational and open‑source hurdles remain.

Enterprise AIOntologyknowledge graphs
0 likes · 16 min read
Ontology: The Semantic OS for Large‑Model AI, Not a Repackaged Knowledge Graph
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Jul 13, 2026 · Artificial Intelligence

From QA to Task‑Oriented Agents: Recent Trends in Large Language Models

The article surveys the latest advances in large language model agents, covering multi‑agent collaboration, long‑horizon planning, self‑evolution, trust and safety, test‑time scaling techniques, new foundation and multimodal models, open‑source and closed‑source breakthroughs, world‑model integration, and emerging vertical applications.

Foundation ModelsLLM agentsTest-Time Scaling
0 likes · 12 min read
From QA to Task‑Oriented Agents: Recent Trends in Large Language Models
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 5, 2026 · Artificial Intelligence

Why Even 10× Smarter AI Scientists Won’t Accelerate Science: The 300‑Year‑Old Paper Bottleneck

The article argues that despite rapid advances in AI scientists, scientific progress remains limited by the centuries‑old paper format, peer‑review constraints, and incentive structures, and proposes an Agent‑Native Research Artifact to make research forkable and preserve failed experiments, dramatically improving reproducibility and understanding.

AI for ScienceAgent‑Native Research Artifactknowledge graphs
0 likes · 12 min read
Why Even 10× Smarter AI Scientists Won’t Accelerate Science: The 300‑Year‑Old Paper Bottleneck
AI Frontier Lectures
AI Frontier Lectures
Nov 13, 2025 · Artificial Intelligence

How Graphs Empower LLM Agents: A Deep Dive into GLA

This article reviews the IEEE Intelligent Systems survey that introduces Graph‑augmented LLM Agents (GLA), explains how representing plans, memory, tools and multi‑agent interactions as graphs improves reliability, efficiency, interpretability and flexibility, and outlines five key research directions for future development.

Agent CoordinationLLM agentsknowledge graphs
0 likes · 8 min read
How Graphs Empower LLM Agents: A Deep Dive into GLA
Architect
Architect
Jul 6, 2025 · Artificial Intelligence

How Graphs Empower AI Agents: Taxonomy, Advances, and Future Opportunities

An extensive review introduces a taxonomy for integrating graph techniques with AI agents, detailing how graphs enhance core functions such as planning, execution, memory, and multi‑agent coordination, and discusses representative applications, challenges, and future research directions.

AI AgentsGraph Neural Networksknowledge graphs
0 likes · 9 min read
How Graphs Empower AI Agents: Taxonomy, Advances, and Future Opportunities
DataFunSummit
DataFunSummit
Jul 4, 2025 · Artificial Intelligence

Multimodal GraphRAG: Knowledge Graphs, Large Models, and Industry Use Cases

This guide introduces a series of cutting‑edge topics, including multimodal GraphRAG, the dual‑driven synergy of knowledge graphs and large models across finance, traditional Chinese medicine, private‑domain Q&A, knowledge management, and automotive manufacturing, plus trends and standards for large‑model‑based knowledge management.

AI applicationsGraphRAGknowledge graphs
0 likes · 2 min read
Multimodal GraphRAG: Knowledge Graphs, Large Models, and Industry Use Cases
HyperAI Super Neural
HyperAI Super Neural
Nov 20, 2024 · Artificial Intelligence

From Computer Vision to Medical AI: Prof. Xie's Work Hits Nature, NeurIPS, CVPR

Professor Xie's team at Shanghai Jiao Tong University reports rapid progress in AI for Science, detailing multimodal medical AI models, large open datasets, language and vision‑language models, and knowledge‑enhanced representations that outperform existing baselines across multiple benchmarks.

Medical AIOpen Datasetsknowledge graphs
0 likes · 14 min read
From Computer Vision to Medical AI: Prof. Xie's Work Hits Nature, NeurIPS, CVPR
Sohu Tech Products
Sohu Tech Products
Nov 6, 2024 · Artificial Intelligence

RAG2.0 Engine Design Challenges and Implementation

The talk outlines RAG2.0’s design challenges—low vector recall, complex documents, semantic gaps—and presents a two‑stage architecture using deep multimodal understanding and knowledge‑graph‑enhanced retrieval, detailing advanced chunking, multi‑index and multi‑path retrieval, efficient sorting models like ColBERT, and future multi‑modal and memory‑augmented agent directions.

ColBERTDelayed InteractionEnterprise AI
0 likes · 23 min read
RAG2.0 Engine Design Challenges and Implementation
AntTech
AntTech
Oct 16, 2024 · Artificial Intelligence

Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering (SEPTA Framework)

The paper introduces the SEPTA framework, which converts knowledge graphs into a subgraph vector database and employs graph‑text alignment via bidirectional contrastive learning to improve subgraph retrieval and knowledge fusion for commonsense question answering, demonstrating strong performance across five benchmark datasets.

SEPTAcommonsense QAgraph-text alignment
0 likes · 4 min read
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering (SEPTA Framework)
DataFunSummit
DataFunSummit
Sep 13, 2024 · Artificial Intelligence

Research on Domain Large Models by Fudan University Knowledge Workshop Lab

This article presents the Fudan University Knowledge Workshop Lab's comprehensive research on domain large models, covering background, domain adaptation, capability enhancement, collaborative workflows, challenges such as inference cost and alignment, and proposed solutions including source‑enhanced training, self‑correction mechanisms, and hybrid retrieval‑augmented generation.

AI researchDomain AdaptationSelf-Correction
0 likes · 16 min read
Research on Domain Large Models by Fudan University Knowledge Workshop Lab
DataFunSummit
DataFunSummit
Jul 6, 2024 · Artificial Intelligence

Synergy Between Large Language Models and Knowledge Graphs: Recent Advances, Evaluation, and Future Integration

This article reviews the rapid progress of large language models and their complementary relationship with knowledge graphs, covering comparative strengths, knowledge extraction and completion, evaluation benchmarks, deployment benefits, complex reasoning support, and prospects for interactive fusion toward more reliable and explainable AI systems.

AI evaluationKnowledge ExtractionSemantic Reasoning
0 likes · 12 min read
Synergy Between Large Language Models and Knowledge Graphs: Recent Advances, Evaluation, and Future Integration
DataFunSummit
DataFunSummit
Jun 15, 2024 · Artificial Intelligence

Large‑Model‑Driven Data Governance: Technical Outlook and Research Highlights

This article reviews the rising importance of data quality for large models, explores data‑centric AI, large‑model pre‑training data engineering, and presents recent Fudan University research on using large models to improve data governance across multiple domains such as attribute normalization, geographic cleaning, compliance checking, and multimodal retrieval.

AIMultimodal Datadata engineering
0 likes · 19 min read
Large‑Model‑Driven Data Governance: Technical Outlook and Research Highlights
NewBeeNLP
NewBeeNLP
May 15, 2024 · Artificial Intelligence

How Large Language Models and Knowledge Graphs Can Boost Each Other

This talk reviews recent advances in large language models, compares them with knowledge graphs, explores how LLMs enhance knowledge extraction and completion, examines how knowledge graphs aid LLM evaluation and safe deployment, and outlines future interactive integration between the two technologies.

AI researchKnowledge Extractionknowledge graphs
0 likes · 13 min read
How Large Language Models and Knowledge Graphs Can Boost Each Other
AntTech
AntTech
Apr 19, 2024 · Artificial Intelligence

OneKE: Open-Source Bilingual Knowledge Extraction Framework for Large Language Models

OneKE, an open‑source bilingual (Chinese‑English) knowledge extraction framework jointly developed by Ant Group and Zhejiang University, enables efficient extraction of entities, relations, and events to build domain knowledge graphs that enhance large language models’ reasoning, reduce hallucinations, and support applications in medical, financial, and governmental sectors.

Artificial IntelligenceKnowledge Extractionbilingual
0 likes · 5 min read
OneKE: Open-Source Bilingual Knowledge Extraction Framework for Large Language Models
NewBeeNLP
NewBeeNLP
Mar 1, 2024 · Artificial Intelligence

How Knowledge Graphs Are Transforming Multi‑Modal AI: A Deep Survey

This comprehensive survey examines over 300 recent papers on knowledge‑graph‑driven multi‑modal learning and multi‑modal knowledge graphs, outlining key tasks, datasets, benchmarks, challenges, and future directions, while highlighting the impact of large language models and multimodal pre‑training techniques.

KG4MMMMKGknowledge graphs
0 likes · 10 min read
How Knowledge Graphs Are Transforming Multi‑Modal AI: A Deep Survey
Baidu Tech Salon
Baidu Tech Salon
Dec 14, 2023 · Artificial Intelligence

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

The Baidu Research Institute’s 2023 Paper Sharing Session featured eight cutting‑edge papers—from semi‑supervised web‑search ranking and hierarchical reinforcement learning for autonomous intersections to spatial‑heterophily graph networks, a unified XAI benchmark, differentiable neuro‑symbolic KG reasoning, and novel stochastic‑gradient and neural‑field loss analyses—showcasing advances across AI, data mining, and computer vision.

Artificial IntelligenceAutonomous VehiclesNeural Fields
0 likes · 10 min read
Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 7, 2023 · Artificial Intelligence

How Alibaba Cloud’s PAI Breakthroughs Are Shaping AI at EMNLP 2023

Alibaba Cloud’s AI platform PAI had four papers accepted at EMNLP 2023, presenting advances in automatic prompt engineering for text‑to‑image, domain‑specific knowledge‑enhanced language models, cognitive‑tree reasoning with small LLMs, and cross‑lingual machine reading comprehension, all demonstrating cutting‑edge AI research and product integration.

Artificial Intelligenceknowledge graphsprompt engineering
0 likes · 9 min read
How Alibaba Cloud’s PAI Breakthroughs Are Shaping AI at EMNLP 2023
DataFunSummit
DataFunSummit
Oct 30, 2023 · Artificial Intelligence

Exploring General AI, Large Language Models, Knowledge Graphs, and Reinforcement Learning – Insights from DataFun

This article presents a comprehensive overview of DaGuan Data's explorations in general artificial intelligence, large language models, knowledge graphs, reinforcement learning, compute and data requirements, and the emerging concept of Human‑Centric AGI, supplemented by a detailed Q&A session.

AGIArtificial Intelligenceknowledge graphs
0 likes · 18 min read
Exploring General AI, Large Language Models, Knowledge Graphs, and Reinforcement Learning – Insights from DataFun
DataFunTalk
DataFunTalk
Sep 8, 2023 · Artificial Intelligence

Knowledge Processing in the Era of Large Models: New Opportunities and New Challenges

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.

AIknowledge graphsknowledge representation
0 likes · 27 min read
Knowledge Processing in the Era of Large Models: New Opportunities and New Challenges
FunTester
FunTester
Aug 22, 2023 · Artificial Intelligence

The Current State and Future Outlook of AI‑Driven Software Testing

The article examines how large‑language models, test‑case generation technologies, and model‑driven testing are reshaping software testing, discusses the challenges of applying AI to testing, and outlines future directions and skill sets for professionals seeking to leverage AI in quality assurance.

AIknowledge graphslarge language models
0 likes · 14 min read
The Current State and Future Outlook of AI‑Driven Software Testing
DataFunTalk
DataFunTalk
May 26, 2023 · Artificial Intelligence

Knowledge‑Based Neural‑Symbolic Discrete Reasoning: OPERA, UniRPG‑2, and Large‑Model Inference

The presentation reviews recent research on knowledge‑driven neural‑symbolic discrete reasoning, including the OPERA lightweight‑operator model for text reasoning, the UniRPG‑2 program‑generation framework for heterogeneous knowledge, the state of zero‑ and few‑shot large‑model inference, and future directions.

Neural-Symbolic ReasoningProgram GenerationZero-shot Inference
0 likes · 8 min read
Knowledge‑Based Neural‑Symbolic Discrete Reasoning: OPERA, UniRPG‑2, and Large‑Model Inference
DataFunSummit
DataFunSummit
May 19, 2023 · Artificial Intelligence

Expert Roundtable on the Impact of GPT‑4 and Large Models on Knowledge Graphs

In this expert roundtable, leading AI researchers discuss GPT‑4’s multimodal breakthroughs, the future convergence of large models with knowledge graphs, practical integration strategies, and the evolving relevance of traditional NLP tasks, offering deep insights into the direction of artificial intelligence research.

Artificial IntelligenceGPT-4knowledge graphs
0 likes · 44 min read
Expert Roundtable on the Impact of GPT‑4 and Large Models on Knowledge Graphs
DataFunSummit
DataFunSummit
Dec 6, 2022 · Artificial Intelligence

Multimodal Reasoning, Logic Inference, and Machine Learning: An Integrated Survey

This article surveys the development of artificial intelligence from symbolic and connectionist perspectives, covering deductive and inductive reasoning, multimodal and cross‑modal inference, knowledge‑graph reasoning, text and visual understanding, and their applications in causal inference, dialogue consistency, and security vulnerability analysis.

Causal InferenceMultimodal Reasoningdialogue consistency
0 likes · 18 min read
Multimodal Reasoning, Logic Inference, and Machine Learning: An Integrated Survey
DataFunSummit
DataFunSummit
Feb 10, 2022 · Artificial Intelligence

Baidu's PGL2.2: A Graph Neural Network Framework, Techniques, and Real‑World Applications

This article introduces Baidu's PGL2.2 graph learning platform, explains graph modeling and message‑passing GNN techniques, details training strategies for small, medium and large graphs, showcases node classification and link‑prediction methods, and describes how the framework is applied in search, recommendation, risk control, and knowledge‑graph competitions.

Graph Neural NetworksLarge‑Scale TrainingPGL2.2
0 likes · 15 min read
Baidu's PGL2.2: A Graph Neural Network Framework, Techniques, and Real‑World Applications
FunTester
FunTester
Nov 11, 2020 · Artificial Intelligence

Unlocking NLP: From the Turing Test to Word Embeddings and Beyond

This article provides a comprehensive overview of natural language processing, tracing its origins from Turing's seminal test to modern techniques like regular expressions, word order importance, word embeddings, Word2vec, GloVe, and knowledge‑ and retrieval‑based chatbot methods.

GloVeNLPWord2Vec
0 likes · 15 min read
Unlocking NLP: From the Turing Test to Word Embeddings and Beyond
JD Retail Technology
JD Retail Technology
Nov 22, 2018 · Artificial Intelligence

Challenges and Innovations in Category Classification Systems

This article discusses the limitations of algorithm-based classification models, including the need for large labeled datasets, limited sample coverage, frequent category changes requiring retraining, and complex optimization issues, while exploring knowledge graph-based approaches and generative adversarial networks for more flexible and accurate classification.

Generative Adversarial Networksbad case optimizationcategory optimization
0 likes · 6 min read
Challenges and Innovations in Category Classification Systems
Qunar Tech Salon
Qunar Tech Salon
Jan 10, 2018 · Artificial Intelligence

An Introductory Overview of Natural Language Processing

Natural Language Processing, a branch of AI, is traced from its Turing origins through early rule‑based methods, statistical and deep‑learning paradigms, covering lexical analysis, syntax, semantics, knowledge graphs, and current applications, highlighting historical shifts, challenges, and future research directions.

Artificial IntelligenceLanguage ProcessingNLP
0 likes · 22 min read
An Introductory Overview of Natural Language Processing