Graphs + Transformers = the best of both worlds ๐ค The same models powering breakthroughs in natural language processing are now being adapted for graphsโฆ
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 ofโฆ | 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 ofโฆ
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 โณ In the realm of agentic systems, a fundamental challenge emergesโฆ
Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy
Understanding SPARQL Queries: Are We Already There
๐ Our paperย "Understanding SPARQL Queries: Are We Already There?", explores the potential of Large Language Models (#LLMs) to generate natural-languageโฆ
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...
More Graph, More Agents: Scaling Graph Reasoning with LLMs
More Graph, More Agents: Scaling Graph Reasoning with LLMs Graph reasoning tasks have proven to be a tough nut to crack for Large Language Models (LLMs).โฆ
More Graph, More Agents: Scaling Graph Reasoning with LLMs
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts ofโฆ | 13 comments on LinkedIn
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...
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an...
LLMs and Knowledge Graphs: A love story ๐ Researchers from University of Oxford recently released MedGraphRAG. At its core, MedGraphRAG is a frameworkโฆ
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
โจ Attention Information Extraction Enthusiasts โจ I am excited to announce the release of our latest paper and model family, ReLiK, a cutting-edgeโฆ | 33 comments on LinkedIn
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 theโฆ
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 Scientificโฆ
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 moreโฆ
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%. ๐ A case study from LinkedIn. ๐ค๐ผ Conventional RAG methods treat historical issueโฆ | 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 unstructuredโฆ | 13 comments on LinkedIn
Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
GNN-RAG Combines the language understanding abilities of LLMs with the reasoning abilities of GNNs in a RAG style. The GNN extracts useful and relevantโฆ
An approach for designing learning path recommendations using GPT-4 and Knowledge Graphs
๐กย How important are learning paths for gaining the skills needed to tackle real-life problems? ๐ฌResearchers from the University of Siegen (Germany) and Keioโฆ
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...
Revolutionizing Document Reranking with G-RAG: A Graph-Based Approach
Revolutionizing Document Reranking with G-RAG: A Graph-Based Approach ... Discover how a novel graph-based reranker is transforming the way we retrieveโฆ
Revolutionizing Document Reranking with G-RAG: A Graph-Based Approach
