A Triple Store RAG Retriever
How SP used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models
A Triple Store RAG Retriever
How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models
Matching skills and candidates with Graph RAG
At Semantic Partners, we wanted to build our informed opinion over the strengths and weaknesses of graph RAG for RDF triple stores. We considered a simple use case: matching a job opening with Curriculum Vitae. We show how we used Ontotext GraphDB to build a simple graph RAG retriever using open, offline LLM models – the graph acting like a domain expert for improving search accuracy.
GQL and Cypher
A short introduction what a GQL query looks like relative to Cypher
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of...
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
GitHub - zazuko/knowledge-graph-forum: Knowledge Graph Forum, Switzerland
Knowledge Graph Forum, Switzerland. Contribute to zazuko/knowledge-graph-forum development by creating an account on GitHub.
SPARQL CDTs: Representing and Querying Lists and Maps as RDF Literals
This specification defines an approach to represent generic forms of composite values (lists and maps, in particular) as literals in RDF, and corresponding extensions of the SPARQL language. These extensions include an aggregation function to produce such composite values, functions to operate on such composite values in expressions, and a new operator to transform such composite values into their individual components.
Liberating Cohesion via RDF
RDF combines universal ways to name, structure and give meaning to data using only open standards. Naming is done with URIs; the structure is always the subject-predicate-object triple, and the meaning is provided by extending RDF with shared vocabularies. These three ways, individually and in combination, enable autonomy and cohesion. Let's see how.
Enterprise Knowledge Graphs: The Importance of Semantics - Enterprise Knowledge
In this presentation, Hedden describes the components of an enterprise knowledge graph and provides further insight into the semantic layer.
When building GraphRAG, you may want to explicitly define the graph yourself, or use the LLM automatically extract the graph
When building GraphRAG, you may want to explicitly define the graph yourself, or use the LLM automatically extract the graph. Both have tradeoffs: the former… | 17 comments on LinkedIn
When building GraphRAG, you may want to explicitly define the graph yourself, or use the LLM automatically extract the graph
Knowledge Graphs: RAG is NOT all you need
Over the past few weeks I’ve been researching, and building a framework that combines the power of Large Language Models for text parsing and transformation with the precision of structur…
Neo4j and Snowflake Bring Graph Data Science Into the AI Data Cloud - Graph Database & Analytics
Neo4j Graph Data Science (GDS) now fully integrates with the Snowflake AI Data Cloud for advanced AI insights and predictive analytics.
Semantic Layer as the Data Interface for LLMs
A new benchmark for natural language questions against databases dropped last week. What does it mean and how does the dbt Semantic Layer stack up?
Are our novel Spatio-Spectral GNNs (S²GNNs) the better alternative to graph transformers?
🤔 Are our novel Spatio-Spectral GNNs (S²GNNs) the better alternative to graph transformers? ➕ S²GNNs (https://lnkd.in/dbrCTuxH, joint work with Simon…
Are our novel Spatio-Spectral GNNs (S²GNNs) the better alternative to graph transformers?
Graphs, Logic, and Lambda Calculus
Here are my slides from KGC 2024: https://lnkd.in/gAhMRE_U. In a nutshell, I made the point that when it comes to adding advanced features to property graphs…
Graphs, Logic, and Lambda Calculus
LOT - Linked Open Terms
Understanding the Knowledge Graph & RAG Opportunity
Case Studies, how Public companies are using KGs today, and where to apply KGs in RAG
MLSea
GitHub - RoderickvanderWeerdt/OfficeGraph: OfficeGraph is a knowledge graph of IoT device measurements.
OfficeGraph is a knowledge graph of IoT device measurements. - RoderickvanderWeerdt/OfficeGraph
Trip Report: ESWC 2024
Last week, I attended the 21st Extended (European) Semantic Web Conference. The conference was well organised by Dr. Albert Meroño Peñuela from King’s College London. He seemed surprisingly c…
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
RAG meets GNNs: Integrating graphs into a modern workflow. Knowledge Graphs (KGs) are a powerful way to represent factual knowledge, but querying them with…
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
Introducing WhyHow.AI Open-Source Knowledge Graph Schema Library — Start Experimenting Faster
We are excited to announce WhyHow.AI’s Open Source Knowledge Graph schema library.
𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗼𝗻 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀
"𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗼𝗻 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀" 📑 -- a Paper from a long-term project in my PhD has finally been released!…
𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗼𝗻 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀
Low-latency automotive vision with event cameras - Nature
Use of a 20 frames per second (fps) RGB camera plus an event camera can achieve the same latency as a 5,000-fps camera with the bandwidth of a 45-fps camera without compromising accuracy.
LlamaIndex on LinkedIn: Build-your-own Graph RAG
Build-your-own Graph RAG 🕸️ There are two prepackaged ways to do RAG with knowledge graphs: vector/keyword search with graph traversal, and text-to-cypher.… | 15 comments on LinkedIn
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 relevant…
Building a Knowledge Graph for Enhanced Podcast SEO
Discover how adding structured data to your podcast site can boost your SEO and enhance search engine visibility with our guide.
GitHub - dtai-kg/SCOOP: SCOOP: Shape Integration Framework
SCOOP: Shape Integration Framework. Contribute to dtai-kg/SCOOP development by creating an account on GitHub.
Understanding Transformer Reasoning Capabilities via Graph Algorithms
🎉 Check out our new work on Transformer theory! (out today on arxiv) Key takeaways: 1️⃣ We show how 9 different algorithmic tasks map into a complexity… | 10 comments on LinkedIn