Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
LLMs are taking Graph Neural Networks to the next level:
While we've been discussing LLMs for natural language, they're quietly changing how we represent…
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large
Agentic Paranets just landed on the origin_trail DKG. A major paranet feature upgrade built for AI agents with enhanced knowledge graph read/write access control
Knowledge graphs for LLM grounding and avoiding hallucination
This blog post is part of a series that dives into various aspects of SAP’s approach to Generative AI, and its technical underpinnings. In previous blog posts of this series, you learned about how to use large language models (LLMs) for developing AI applications in a trustworthy and reliable manner...
Enabling LLM development through knowledge graph visualization
Discover how to empower LLM development through effective knowledge graph visualization. Learn to leverage yFiles for intuitive, interactive diagrams that simplify debugging and optimization in AI applications.
"Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
🎉🎉 🎉 "Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
Four years ago, we embarked on writing "Knowledge Graphs Applied" with a clear mission: to guide practitioners in implementing production-ready knowledge graph solutions. Drawing from our extensive field experience across multiple domains, we aimed to share battle-tested best practices that transcend basic use cases.
Like fine wine, ideas, and concepts need time to mature. During these four years of careful development, we witnessed a seismic shift in the technological landscape. Large Language Models (LLMs) emerged not just as a buzzword, but as a transformative force that naturally converged with knowledge graphs.
This synergy unlocked new possibilities, particularly in simplifying complex tasks like unstructured data ingestion and knowledge graph-based question-answering.
We couldn't ignore this technological disruption. Instead, we embraced it, incorporating our hands-on experience in combining LLMs with graph technologies. The result is "Knowledge Graphs and LLMs in Action" – a thoroughly revised work with new chapters and an expanded scope.
Yet our fundamental goal remains unchanged: to empower you to harness the full potential of knowledge graphs, now enhanced by their increasingly natural companion, LLMs. This book represents the culmination of a journey that evolved alongside the technology itself. It delivers practical, production-focused guidance for the modern era, in which knowledge graphs and LLMs work in concert.
Now available in MEAP, with new LLMs-focused chapters ready to be published.
#llms #knowledgegraph #graphdatascience
"Knowledge Graphs Applied" becomes "Knowledge Graphs and LLMs in Action"
The SECI model for knowledge creation, collection, and distribution within the organization
💫 An 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝘆 is just a means, not an end.
👉 Transforming 𝘁𝗮𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 into 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 through an enterprise ontology is a self-contained exercise if not framed within a broader process of knowledge creation, collection, and distribution within the organization.
👇 The 𝗦𝗘𝗖𝗜 𝗠𝗼𝗱𝗲𝗹 effectively describes the various steps of this process, going beyond mere collection and formalization. The SECI model outlines the following four phases that must be executed iteratively and continuously to properly manage organizational knowledge:
1️⃣ 𝗦𝗼𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, tacit knowledge is shared through direct interaction, observation, or experiences. It emphasizes the transfer of personal knowledge between individuals and fosters mutual understanding through collaboration (tacit ➡️ tacit).
2️⃣ 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, tacit knowledge is articulated into explicit forms, such as an enterprise ontology. It helps to codify and communicate the personal knowledge that might otherwise remain unspoken or difficult to share (tacit ➡️ explicit).
3️⃣ 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻: In this phase, explicit knowledge is gathered from different sources, categorized, and synthesized to form new sets of knowledge. It involves the aggregation and reorganization of existing knowledge to create more structured and accessible forms (explicit ➡️ explicit).
4️⃣ 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, individuals internalize explicit knowledge, turning it back into tacit knowledge through practice, experience, and learning. It emphasizes the transformation of formalized knowledge into personal, actionable knowledge (explicit ➡️ tacit).
🎯 In a world where the only constant is change, it is no longer enough for an organization to know something; what matters most is how fast it learns by creating and redistributing new knowledge internally.
🧑🎓 To quote Nadella, organizations and the people within them should not be 𝘒𝘯𝘰𝘸-𝘐𝘵-𝘈𝘭𝘭𝘴 but rather 𝘓𝘦𝘢𝘳𝘯-𝘐𝘵-𝘈𝘭𝘭𝘴.
#TheDataJoy #KnowledgeMesh #KnowledgeManagement #Ontologies
Transforming 𝘁𝗮𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 into 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 through an enterprise ontology is a self-contained exercise if not framed within a broader process of knowledge creation, collection, and distribution within the organization.
From Ontology to Domain Objects: Bridging Knowledge Graphs and AI driven Application Development
When implementing graph databases in modern software development, we often face a significant challenge: bridging the conceptual gap between ontology-focused knowledge representation and…
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems 🛜
At the most fundamental level, all approaches rely… | 11 comments on LinkedIn
Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal
Introducing CyVer: Schema-Aware Cypher Query Validation for Neo4j
🚀 Introducing 𝗖𝘆𝗩𝗲𝗿: Schema-Aware Cypher Query Validation for Neo4j!
We’re excited to share 𝗖𝘆𝗩𝗲𝗿, the Python library we developed to validate… | 12 comments on LinkedIn
Introducing 𝗖𝘆𝗩𝗲𝗿: Schema-Aware Cypher Query Validation for Neo4j
Knowledge graphs: the missing link in enterprise AI
To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren't a good fit to train large language models.
Synalinks is an open-source framework designed to streamline the creation, evaluation, training, and deployment of industry-standard Language Models (LMs) applications
🎉 We're thrilled to unveil Synalinks (🧠🔗), an open-source framework designed to streamline the creation, evaluation, training, and deployment of…
Synalinks (🧠🔗), an open-source framework designed to streamline the creation, evaluation, training, and deployment of industry-standard Language Models (LMs) applications
GiGL: Large-Scale Graph Neural Networks at Snapchat
Recent advances in graph machine learning (ML) with the introduction of Graph Neural Networks (GNNs) have led to a widespread interest in applying these approaches to business applications at...
GiGL: Large-Scale Graph Neural Networks at Snapchat
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graph…
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
Announcing QLeverize: The Future of Open-Source Knowledge Graphs at Unlimited Scale | LinkedIn
Biel/Bienne, Switzerland – February 24, 2025 – Knowledge graphs are becoming critical infrastructure for enterprises handling large-scale, interconnected data. Yet, many existing solutions struggle with scalability, performance, and cost—forcing organizations into proprietary ecosystems with high op
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
I love Markus J. Buehler's work, and his latest paper "Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks" does not disappoint, revealing… | 19 comments on LinkedIn
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage.
Achieving that by Semantic-Aware Heterogeneous Graph…
MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage
What makes an ontology fail? 9 reasons.
At the inauguration of SCOR (Swiss Center for Ontological Research), I had the opportunity to speak alongside Barry… | 154 comments on LinkedIn
🌟 Calling all teachers, students and practitioners in the Semantic Web and knowledge graph community! 🌟
Looking for a fresh, engaging dataset to build… | 18 comments on LinkedIn
#StarWars facts in the hashtag#Wikidata Knowledge Graph
KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs
Breaking LLM Hallucinations in a Smarter Way!
(It’s not about feeding more data)
Large Language Models (LLMs) still struggle with factual inaccuracies, but…
