What is really Graph RAG?
What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn
What is really Graph RAG?
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering That has been our position from the beginning when we started our research… | 29 comments on LinkedIn
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
Graph technologies outlook in 2025
Ashley Faith hosted Amy Hodler and me on a recent episode about “2025 Predictions” — here are notes I prepared, to review in 2026…
Large Language Models, Knowledge Graphs and Search Engines: A...
Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is...
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…
SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph semantic distance metric
Unlocking universal reasoning across knowledge graphs
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 11 comments on LinkedIn
Unlocking universal reasoning across knowledge graphs.
takeaways from the International Semantic Web Conference #iswc2024
My takeaways from the International Semantic Web Conference #iswc2024 Ioana keynote: Great example of data integration for journalism, highlighting the use of…
takeaways from the International Semantic Web Conference hashtag#iswc2024
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy ⛳ In the realm of agentic systems, a fundamental challenge emerges…
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
Understanding SPARQL Queries: Are We Already There
👉 Our paper "Understanding SPARQL Queries: Are We Already There?", explores the potential of Large Language Models (#LLMs) to generate natural-language…
Understanding SPARQL Queries: Are We Already There
Knowledge Graph Enhanced Language Agents for Recommendation
Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the...
Knowledge Graph In-Context Learning
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 13 comments on LinkedIn
Knowledge Graph In-Context Learning
Graph-constrained Reasoning
🚀 Exciting New Research: "Graph-constrained Reasoning (GCR)" - Enabling Faithful KG-grounded LLM Reasoning with Zero Hallucination! 🧠 🎉 Proud to share our… | 11 comments on LinkedIn
Graph-constrained Reasoning
Fact Finder -- Enhancing Domain Expertise of Large Language Models...
Recent advancements in Large Language Models (LLMs) have showcased their proficiency in answering natural language queries. However, their effectiveness is hindered by limited domain-specific...
Graph Retrieval-Augmented Generation: A Survey
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an...
Medical Graph RAG
LLMs and Knowledge Graphs: A love story 💓 Researchers from University of Oxford recently released MedGraphRAG. At its core, MedGraphRAG is a framework…
Think-on-Graph 2.0: Deep and Interpretable Large Language Model...
Retrieval-augmented generation (RAG) has significantly advanced large language models (LLMs) by enabling dynamic information retrieval to mitigate knowledge gaps and hallucinations in generated...
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
✨ Attention Information Extraction Enthusiasts ✨ I am excited to announce the release of our latest paper and model family, ReLiK, a cutting-edge… | 33 comments on LinkedIn
GNN-RAG: combining LLMs language abilities with GNNs reasoning in RAG style
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a…
𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘪𝘴 𝘢 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘵𝘢𝘪𝘭𝘰𝘳𝘦𝘥 𝘧𝘰𝘳 𝘪𝘯𝘵𝘦𝘯𝘵-𝘣𝘢𝘴𝘦𝘥 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘶𝘢𝘭𝘭𝘺 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭.
𝗥𝗔𝗚 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗮𝗶𝗹 𝗗𝘂𝗲 𝗧𝗼 𝗜𝗻𝘀𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗙𝗼𝗰𝘂𝘀 𝗢𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗻𝘁 𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎… | 12 comments on LinkedIn
𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘪𝘴 𝘢 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘵𝘢𝘪𝘭𝘰𝘳𝘦𝘥 𝘧𝘰𝘳 𝘪𝘯𝘵𝘦𝘯𝘵-𝘣𝘢𝘴𝘦𝘥 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘶𝘢𝘭𝘭𝘺 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭.
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
This is something very cool! 3. GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models "GraphReader addresses the…
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
GitHub - SynaLinks/HybridAGI: The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI
Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific Knowledge) uses vector hashtag#embeddings to find the most relevant papers and an open-source hashtag#LLM to synthesize the answer for you
Ask your (research) question against 76 Million scientific articles: https://ask.orkg.org Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific…
Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific Knowledge) uses vector hashtag#embeddings to find the most relevant papers and an open-source hashtag#LLM to synthesize the answer for you
Synergizing LLMs and KGs in the GenAI Landscape | LinkedIn
Our paper "Are Large Language Models a Good Replacement of Taxonomies?" was just accepted to VLDB'2024! This finished our last stroke of study on how knowledgeable LLMs are and confirmed our recommendation for the next generation of KGs. How knowledgeable are LLMs? 1.
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
RAG + Knowledge Graphs cut customer support resolution time by 29.6%
RAG + Knowledge Graphs cut customer support resolution time by 29.6%. 📉 A case study from LinkedIn. 🤝💼 Conventional RAG methods treat historical issue… | 10 comments on LinkedIn
[2310.01061v1] Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can...
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary...
Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge...
Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models
Introducing Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data ... Did you know that 80% of enterprise data resides in unstructured… | 13 comments on LinkedIn
Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data
An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
💡 How important are learning paths for gaining the skills needed to tackle real-life problems? 🔬Researchers from the University of Siegen (Germany) and Keio…
an approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large...
In applications such as personal assistants, large language models (LLMs) must consider the user's personal information and preferences. However, LLMs lack the inherent ability to learn from user...