knowledge-graph

This paper proposes DKG-MTI, a dual knowledge graph framework that augments LLM-based inference with structure-aware knowledge alignment to jointly predict aspect ratings and generate user intent statements from online travel reviews.

This article explains how current agent memory systems store and retrieve facts but fail to recognize patterns, and introduces Zep's Observations feature which analyzes knowledge graphs to derive structural insights.

Semantica is an open-source, self-hosted platform that converts data into a knowledge graph, records AI agent decisions, and provides full provenance and deterministic reasoning for explainable, traceable AI systems.

Microsoft Research released PlugMem, a memory plugin for AI agents that distills experience into a knowledge graph, reducing context tokens by 10-100x and improving decision utility without fine-tuning.

Neo4j's Will Lyon presents a recorded session on designing stateful AI agents with memory and context graphs, covering pitfalls and integrations with Salesforce Agentforce and Databricks.

This paper introduces DREAM, a structured memory framework for LLM-based role-playing agents that uses an Event-aware Memory Graph to maintain temporal and causal coherence, and proposes the TCM benchmark for evaluation.

A tweet describing a personal knowledge management system where Claude reads and links markdown notes in a vault, acting as a librarian over a portable, model-agnostic second brain.

It explains the evolution path of RAG technology from basic RAG in 2020 to autonomous agents + reinforcement learning in 2025-2026, covering retrieval precision improvement, reflection and correction, graph enhancement, routing optimization, and Agentic RAG, and summarizes representative works and pain points of each stage.

This IEEE paper presents an AI-powered knowledge graph platform that connects SQL errors to conceptual gaps in database courses, automatically extracting course concepts and linking them to student submissions to provide personalized diagnostic feedback.

This paper introduces PatTree, a graph-based multimodal patient representation that automatically structures heterogeneous clinical data for medical classification tasks. It achieves state-of-the-art performance on the ADNI-1 cohort with 98.5% balanced accuracy for Alzheimer's disease classification.

Presents an evidence-grounded multimodal pipeline that constructs a provenance-rich knowledge graph from lecture videos, combining ASR, OCR, and vision-language models, achieving high retrieval accuracy on neural network lectures.

A free GitHub repo uses Claude to connect notes into a knowledge graph on your own machine, potentially replacing AI note-taking apps. The tweet promotes an article about building a second brain with this method.

RAG-Anything is a multimodal document-processing RAG system built on LightRAG that parses documents, constructs a multimodal knowledge graph, and uses hybrid vector-graph retrieval to answer queries.

COntExt is a framework for context-aware ontology extension that takes structured operational metric definitions as input and suggests how to integrate referenced concepts and properties into existing ontologies. Evaluations across seven ontologies show metric-derived context improves relation type prediction and data property assignment over ontology-context baselines.

NeSyFS is a neuro-symbolic framework for LLM agents under partial observability that uses a knowledge graph to represent belief state, combines fast/slow thinking with uncertainty-aware planning, and reflection, showing gains on ALFWorld, Webshop, and ScienceWorld.

This paper presents a production extraction layer that converts heterogeneous documents into an ontology-aligned knowledge graph using a locally hosted tuned Qwen LLM, with ontology-guided prompts, multi-stage deduplication, and embedding-based resolution. Evaluation on intelligence corpora improved search recall from about 70 to 95 percent with no false merges.

Introducing the open-source project ai-knowledge-graph, which can automatically split long documents into chunks, extract triples, and generate an interactive knowledge graph as HTML. It's compatible with the OpenAI API as well as local models, making it useful for research and document organization.

The article discusses why LLMs cannot learn from user interactions and lack a deterministic truth layer, proposing that a dynamic knowledge graph could reduce hallucinations and improve performance in high-stakes fields.

Graphify is an open-source tool that turns codebases, docs, SQL schemas, configs, and PDFs into a queryable knowledge graph for AI coding assistants like Claude Code, Cursor, Codex, and Gemini CLI, using local deterministic AST parsing with no vector store.

This paper proposes GGC, a Generator–Gate–Corrector framework that selectively corrects LLM-generated SPARQL queries to improve reliability and accuracy, achieving 98.33% query-level accuracy on MCQA while reducing inference overhead by 45%.