OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large...
This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies....
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...
Background: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by...
GraphAgent — An innovative AI agent that efficiently integrates structured and unstructured data
🚀 Excited to Share Our Recent Work! 🌟 GraphAgent — An innovative AI agent that efficiently integrates structured and unstructured data! 📚 👉 Paper link:…
GraphAgent — An innovative AI agent that efficiently integrates structured and unstructured data
PG-Schema: Schemas for Property Graphs | Proceedings of the ACM on Management of Data
Property graphs have reached a high level of maturity, witnessed by multiple robust
graph database systems as well as the ongoing ISO standardization effort aiming at
creating a new standard Graph Query Language (GQL). Yet, despite documented demand,
...
What if creating Linked Open Data was less like coding and more like writing? Could anyone extend the Semantic Web by sharing a document? Publish a knowledge… | 13 comments on LinkedIn
Want to catch up on Graph Neural Networks? Now's the time! Graph Neural Networks (GNNs) have become a popular solution for problems that include network data,…
❓How Can Graph Neural Networks Enhance Recommendation Systems by Incorporating Contextual Information? Traditional recommendation systems often leverage a…
Can Graph Learning Improve Planning in LLM-based Agents?
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural...
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
ICYMI, here are the slides from our standing room only talk at NeurIPS yesterday! Concepts we discuss include: ➡️ Quantifying how much Transformer you need to… | 18 comments on LinkedIn
Graphs + Transformers = the best of both worlds 🤝 The same models powering breakthroughs in natural language processing are now being adapted for graphs…
GNN: Graph Neural Network and Large Language Model Based for Data Discovery
Our algorithm GNN: Graph Neural Network and Large Language Model Based for Data Discovery inherits the benefits of [Hoang(2024b)] (PLOD: Predictive Learning Opt
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
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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...
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
🌟 TGB 2.0 @NeurIPS 2024 🌟 We are very happy to share that our paper TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs… | 11 comments on LinkedIn
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
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).…
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 of… | 13 comments on LinkedIn
UltraQuery: going beyond simple one-hop link prediction to answering more complex queries on any graph in the zero-shot fashion better than trainable SOTA
📣 Foundation models for graph reasoning become even stronger - in our new NeurIPS 2024 work we introduce UltraQuery: going beyond simple one-hop link…
UltraQuery: going beyond simple one-hop link prediction to answering more complex queries on any graph in the zero-shot fashion better than trainable SOTA
Recently, knowledge-graph-enhanced recommendation systems have attracted much attention, since knowledge graph (KG) can help improving the dataset quality and offering rich semantics for explainable recommendation. However, current KG-enhanced solutions focus on analyzing user behaviors on the product level and lack effective approaches to extract user preference towards product category, which is essential for better recommendation because users shopping online normally have strong preference towards distinctive product categories, not merely on products, according to various user studies. Moreover, the existing pure embedding-based recommendation methods can only utilize KGs with a limited size, which is not adaptable to many real-world applications. In this paper, we generalize the recommendation problem with preference mining as a compound knowledge reasoning task and propose a novel multi-agent system, called Mcore, which can promote model performance by mining users’ high-level interests and is adaptable to large KGs. Specifically, we split the overall problem and allocate sub-task to each agent: Coordinate Agent takes charge of recognizing the product-category preference of current user, while Relation Agent and Entity Agent perform KG reasoning cooperatively from a user node towards the preferred categories and terminate at a product node as recommendation. To train this heterogeneous multi-agent system, where agents own various functionalities, we propose an asynchronous reinforcement training pipeline, called Multi-agent Collaborative Learning. The extensive experiments on real datasets demonstrate the effectiveness and adaptability of Mcore on recommendation tasks.
