Tagged articles

Multi-hop Reasoning

9 articles · Page 1 of 1

Oct 9, 2026 · Artificial Intelligence

GraphRAG: Knowledge Graph-Enhanced Retrieval for Multi-Hop Reasoning

This article explains GraphRAG, which combines knowledge graphs with RAG to solve multi-hop reasoning and global query limitations of traditional RAG, detailing Microsoft's GraphRAG approach, Java implementation with Neo4j for entity extraction and graph storage, retrieval processes, community summarization, cost trade-offs, and when to choose GraphRAG over standard RAG.

Community DetectionEntity ExtractionGraphRAG

0 likes · 11 min read

GraphRAG: Knowledge Graph-Enhanced Retrieval for Multi-Hop Reasoning

dbaplus Community

Sep 23, 2026 · Artificial Intelligence

How Ontology Engineering Gives AI Agents a Business Cognitive Layer

This article details a four-layer ontology engineering system that transforms heterogeneous business data into a real-time ontology, enabling read-only AI agents to perform multi-hop reasoning over stable object identities, class relationships, and natural-language rules — demonstrated through a traffic accident investigation case study.

AI AgentKnowledge GraphMulti-hop Reasoning

0 likes · 25 min read

How Ontology Engineering Gives AI Agents a Business Cognitive Layer

PaperAgent

Jul 10, 2026 · Artificial Intelligence

A Deep Dive into QC‑MHM: Boosting Accuracy in Temporal Knowledge Graph Question Answering

The article analyzes the challenges of temporal KGQA, explains why prior models miss time constraints and multi‑hop reasoning, details the four‑module QC‑MHM framework that integrates time‑aware embeddings, question calibration, multi‑hop modeling, and dual‑channel answer prediction, and shows its state‑of‑the‑art performance and interpretability on benchmark datasets.

AAAI 2024Knowledge GraphMulti-hop Reasoning

0 likes · 9 min read

A Deep Dive into QC‑MHM: Boosting Accuracy in Temporal Knowledge Graph Question Answering

Architecture and Beyond

Jun 7, 2026 · Artificial Intelligence

From Fragmented Retrieval to Deep Reasoning: Reshaping AI Agent Knowledge Engines

The article analyzes why traditional RAG fails on complex, multi‑step enterprise queries, explains how GraphRAG introduces explicit entity‑relationship graphs to enable multi‑hop navigation, explainability, and temporal reasoning, and outlines practical architectures, lightweight and dynamic graph strategies, and trade‑offs for real‑world deployment.

AI AgentsGraphRAGKnowledge Graph

0 likes · 26 min read

From Fragmented Retrieval to Deep Reasoning: Reshaping AI Agent Knowledge Engines

James' Growth Diary

May 22, 2026 · Artificial Intelligence

Advanced Graph RAG with Neo4j: When Multi‑Hop Reasoning Beats Vector Search

This article explains why vector retrieval fails on multi‑hop reasoning, shows how Neo4j’s Cypher path traversal enables precise Graph RAG queries, outlines modeling best‑practices, demonstrates hybrid graph‑vector retrieval, compares Graph RAG with vector RAG, and lists common pitfalls to avoid.

CypherKnowledge GraphMulti-hop Reasoning

0 likes · 21 min read

Advanced Graph RAG with Neo4j: When Multi‑Hop Reasoning Beats Vector Search

James' Growth Diary

May 12, 2026 · Artificial Intelligence

GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs

This article explains why traditional vector RAG loses relational information, how GraphRAG reconstructs entity‑relationship triples into a knowledge graph, and provides step‑by‑step code, performance benchmarks, retrieval modes, and practical tips that raise multi‑hop reasoning accuracy from around 50% to 85%.

GraphRAGKnowledge GraphLangChain

0 likes · 14 min read

GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs

PaperAgent

Jan 27, 2026 · Artificial Intelligence

How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points

This article analyzes the Agentic‑R framework, which upgrades traditional single‑hop Retrieval‑Augmented Generation by introducing dual‑perspective scoring and a bidirectional flywheel, resulting in 2–3 absolute EM improvements across seven QA datasets and a 10–15% reduction in search rounds.

Agentic SearchContrastive LearningLLM

0 likes · 6 min read

How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points

DataFunSummit

Jul 26, 2022 · Artificial Intelligence

Multi-step Reasoning over Large-scale Knowledge Graphs: Query2Box and SMORE Framework

This talk presents recent advances in multi-step reasoning over large-scale, noisy knowledge graphs, introducing the Query2Box model that uses box embeddings for complex queries and the SMORE framework that enables efficient multi-hop inference on massive graphs through scalable query generation, embedding computation, and training pipelines.

AIKnowledge GraphMulti-hop Reasoning

0 likes · 14 min read

Multi-step Reasoning over Large-scale Knowledge Graphs: Query2Box and SMORE Framework

DataFunSummit

Feb 25, 2022 · Artificial Intelligence

Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022

The DataFun Summit 2022 Knowledge Graph Forum, held on March 12, presents cutting‑edge research on knowledge graph representation learning, multi‑hop reasoning, temporal KG question answering, and their applications in finance and retail, featuring talks by leading experts from JD, Fourth Paradigm, Stanford, and Meituan.

AI applicationsKnowledge GraphMulti-hop Reasoning

0 likes · 9 min read

Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022