Knowledge Graphs

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 InteractionDocument Understanding

0 likes · 23 min read

RAG2.0 Engine Design Challenges and Implementation

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.

Knowledge GraphsSEPTAcommonsense QA

0 likes · 4 min read

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

AntTech

Oct 15, 2024 · Artificial Intelligence

AI Large Model Technology Exploration and Application Forum (CNCC2024)

The AI Large Model Technology Exploration and Application Forum, held on October 24‑26, 2024 in Hengdian, Zhejiang, gathers leading experts from Ant Group, universities and research institutes to discuss challenges, knowledge enhancement, data infrastructure, diffusion models, multimodal and medical large models through a series of keynote talks and panel sessions.

AIConferenceData Infrastructure

0 likes · 12 min read

AI Large Model Technology Exploration and Application Forum (CNCC2024)

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 researchKnowledge GraphsRetrieval-Augmented Generation

0 likes · 16 min read

Research on Domain Large Models by Fudan University Knowledge Workshop Lab

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 Graphsknowledge extraction

0 likes · 12 min read

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

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.

AIData EngineeringKnowledge Graphs

0 likes · 19 min read

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

DataFunTalk

May 7, 2024 · Artificial Intelligence

Large Language Models and Knowledge Graphs: Recent Advances, Synergies, and Future Directions

This article reviews the rapid progress of large language models, compares them with knowledge graphs, explores how LLMs can aid knowledge extraction and completion, discusses how knowledge graphs can evaluate and enhance LLMs, and outlines future interactive integration between the two technologies.

AIInformation ExtractionKnowledge Graphs

0 likes · 12 min read

Large Language Models and Knowledge Graphs: Recent Advances, Synergies, and Future Directions

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.

Knowledge Graphsbilingualdomain applications

0 likes · 5 min read

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

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 IntelligenceGraph Neural NetworksKnowledge Graphs

0 likes · 10 min read

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

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

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 GraphsPrompt Engineering

0 likes · 27 min read

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

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 GraphsModel-Driven Testing

0 likes · 14 min read

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

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.

Discrete ReasoningKnowledge GraphsNeural-Symbolic Reasoning

0 likes · 8 min read

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

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

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.

Artificial IntelligenceKnowledge Graphscausal inference

0 likes · 18 min read

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

DataFunSummit

Sep 1, 2022 · Artificial Intelligence

Temporal Knowledge Graph Question Answering: The TSQA Approach and Experimental Evaluation

This article presents a comprehensive overview of temporal knowledge graphs, outlines the challenges of building question‑answering systems over them, introduces the TSQA method with its three‑step pipeline for time‑sensitive reasoning, and reports experimental results showing significant improvements on complex queries.

Knowledge GraphsTSQATemporal Knowledge Graphs

0 likes · 22 min read

Temporal Knowledge Graph Question Answering: The TSQA Approach and Experimental Evaluation

DataFunTalk

Mar 3, 2022 · Artificial Intelligence

ISWC 2022 Call for Papers – International Semantic Web Conference in Hangzhou, China

The International Semantic Web Conference (ISWC) 2022 will be held in Hangzhou, China from October 23‑27, featuring a call for papers, detailed important dates, and a full list of organizing committee members across research, resource, in‑use, workshop, industry, and doctoral tracks.

Call for PapersConferenceISWC2022

0 likes · 11 min read

ISWC 2022 Call for Papers – International Semantic Web Conference in Hangzhou, China

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 NetworksKnowledge GraphsLarge-Scale Training

0 likes · 15 min read

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

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 NetworksKnowledge Graphsbad case optimization

0 likes · 6 min read

Challenges and Innovations in Category Classification Systems

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 IntelligenceKnowledge GraphsLanguage Processing

0 likes · 22 min read

An Introductory Overview of Natural Language Processing