GraphNews

To all the knowledge graph enthusiasts who've felt for a while that "graphs are the way to go" when it comes to enabling "intelligence," it was interesting to read Anthropic's "Tracing the thoughts of a large language model" - if you believe that LLMs need graphs to reason, you are right and now you have evidence: Claude answers questions by building and traversing a graph (in latent space) before it translates it back to language: https://lnkd.in/eWFWwfN4 | 20 comments on LinkedIn

A second usecase Thomas wrote for Veronika Heimsbakk’s SHACL for the Practitioner upcoming book is about Sparna’s work for the European Parliament. From validation of the data in the knowledge graph to further projects of data integration and dissemination, many different usages of SHACL specifications were explored… … and more exploratory usages of SHACL are foreseen ! “…

Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven agents face...

What if your LLM is… a graph? A few days ago, Petar Veličković from Google DeepMind gave one of the most interesting and thought provoking conference I've seen in a while, "Large Language Models as Graph Neural Networks". Once you start seeing LLM as graph neural network, many structural oddities suddenly falls into place. For instance, OpenAI currently recommends to put the instructions at the top of a long prompt. Why is that so? Because due to the geometry of attention graphs, LLM are counter-intuitively biased in favors of the first tokens: they travel constinously through each generation steps, are internally repeated a lot and end up "over-squashing" the latter ones. Models then use a variety of internal metrics/transforms like softmax to moderate this bias and better ponderate distribution, but this is a late patch that cannot solve long time attention deficiencies, even more so for long context. The most interesting aspect of the conference from an applied perspective: graph/geometric representations directly affect accuracy and robustness. As the generated sequence grow and deal with sequences of complex reasoning steps, cannot build solid expert system when attention graphs have single point of failures. Or at least, without extrapolating this information in the first place and providing more detailed accuracy metrics. I do believe LLM explainability research is largely underexploited right now, despite being accordingly a key component of LLM devops in big labs. If anything, this is literal "prompt engineering", seeing models as nearly physical structure under stress and providing the right feedback loops to make them more reliable. | 30 comments on LinkedIn

In case you missed the Spanner Graph session at Google Cloud Next’25, the recording is now available: • Introduction of the graph space at 00:00 (https://lnkd.in/gsBFuDbt) • Spanner Graph overview at 07:24 (https://lnkd.in/ggxrzFrU) • How Snapchat builds its Identity Graph at 20:32 (https://lnkd.in/gFauYj-9) • Quick demo of an recommendation engine at 26:27 (https://lnkd.in/gvH4AbRF) • Recent launches at 32:00 (https://lnkd.in/gyCPq97t) • Vision: unified Google Cloud Graph solution with BigQuery Graph at 35:09 (https://lnkd.in/gRdbSMeu) I hope you like it! You can get started with Spanner Graph today! https://lnkd.in/gkwbGFbS Pratibha Suryadevara, Spoorthi Ravi, Sailesh Krishnamurthy, Andi Gutmans, Christopher Taylor, Girish Baliga, Tomas Talius, Candice Chen, Yun Zhang, Weidong Yang, Matthieu Besozzi, Giulia Rotondo, Leo Meyerovich, Thomas Cook, Arthur Bigeard #googlecloud #googlecloudnext25 #graphdatabases #spannergraph

In this post, we’ll dive into Kuzu’s core query capabilities and demonstrate how it pairs nicely with G.V()’s rich visualization tools.

An open-source Python framework for real-time, graph-based knowledge in Neo4j

How LLMs can used in a knowledge extraction and visualization pipeline

"I'm Ukrainian and I'm wearing a suit, so no complaints about me from the Oval Office" - that's the start of my lecture about building Artificial Intelligence with Croissant ML in the Dataverse data platform, for the Bio x AI Hackathon kick-off event in Berlin. https://lnkd.in/ePYHCfJt * 750,000+ FAIR datasets across the world forcing the innovation of the whole data landscape. * A knowledge graph with 50M+ triples. * AI-ready metadata exports. * Qdrant as a vector storage, Google Meta Mistral AI as LLM model providers. * Adrian Gschwend Qlever as fastest triple store for Dataverse knowledge graphs Multilingual, machine-readable, queryable scientific data at scale. If you're interested, you can also apply for the 2-month #BioAgentHack online hackathon: • $125K+ prizes • Mentorship from Biotech and AI leaders • Build alongside top open-science researchers & devs More info: https://lnkd.in/eGhvaKdH

We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.

The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.

Choosing the Right Format: How Knowledge Graph Layouts Impact AI Reasoning ... 👉 Why This Matters Most AI systems blend knowledge graphs (structured data) with large language models (flexible reasoning). But there’s a hidden variable: "how" you translate the graph into text for the AI. Researchers discovered that the formatting choice alone can swing performance by up to "17.5%" on reasoning tasks. Imagine solving 1 in 5 more problems correctly just by adjusting how you present data. 👉 What They Built KG-LLM-Bench is a new benchmark to test how language models reason with knowledge graphs. It includes five tasks: - Triple verification (“Does this fact exist?”) - Shortest path finding (“How are two concepts connected?”) - Aggregation (“How many entities meet X condition?”) - Multi-hop reasoning (“Which entities linked to A also have property B?”) - Global analysis (“Which node is most central?”) The team tested seven models (Claude, GPT-4o, Gemini, Llama, Nova) with five ways to “textualize” graphs, from simple edge lists to structured JSON and semantic web formats like RDF Turtle. 👉 Key Insights 1. Format matters more than assumed:   - Structured JSON and edge lists performed best overall, but results varied by task.   - For example, JSON excels at aggregation tasks (data is grouped by entity), while edge lists help identify central nodes (repeated mentions highlight connections). 2. Models don’t cheat: Replacing real entity names with fake ones (e.g., “France” → “Verdania”) caused only a 0.2% performance drop, proving models rely on context, not memorized knowledge. 3. Token efficiency:   - Edge lists used ~2,600 tokens vs. JSON-LD’s ~13,500. Shorter formats free up context space for complex reasoning.   - But concise ≠ always better: structured formats improved accuracy for tasks requiring grouped data. 4. Models struggle with directionality:   Counting outgoing edges (e.g., “Which countries does France border?”) is easier than incoming ones (“Which countries border France?”), likely due to formatting biases. 👉 Practical Takeaways - Optimize for your task: Use JSON for aggregation, edge lists for centrality. - Test your model: The best format depends on the LLM—Claude thrived with RDF Turtle, while Gemini preferred edge lists. - Don’t fear pseudonyms: Masking real names minimally impacts performance, useful for sensitive data. The benchmark is openly available, inviting researchers to add new tasks, graphs, and models. As AI handles larger knowledge bases, choosing the right “data language” becomes as critical as the reasoning logic itself. Paper: [KG-LLM-Bench: A Scalable Benchmark for Evaluating LLM Reasoning on Textualized Knowledge Graphs] Authors: Elan Markowitz, Krupa Galiya, Greg Ver Steeg, Aram Galstyan

Transform UML into a formal OWL ontology and SHACL shapes - OP-TED/model2owl

Root Github Pages.

Our first attempts at mechanistic interpretability of Transformers from the perspective of network science and graph theory! Check out our preprint: arxiv.org/abs/2502.12352 A wonderful collaboration with superstar MPhil students Batu El, Deepro Choudhury, as well as Pietro Lio' as part of the Geometric Deep Learning class last year at University of Cambridge Department of Computer Science and Technology We were motivated by Demis Hassabis calling AlphaFold and other AI systems for scientific discovery as ‘engineering artifacts’. We need new tools to interpret the underlying mechanisms and advance our scientific understanding. Graph Transformers are a good place to start. The key ideas are: - Attention across multi-heads and layers can be seen as a heterogenous, dynamically evolving graph. - Attention graphs are complex systems represent information flow in Transformers. - We can use network science to extract mechanistic insights from them! More to come on the network science perspective to understanding LLMs next! | 13 comments on LinkedIn

🚀 Thrilled to share our latest work published in Nature Machine Intelligence! 📄 "A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research" In this study, we constructed iKraph, one of the most comprehensive biomedical knowledge graphs to date, using a human-level information extraction pipeline that won both the LitCoin NLP Challenge and the BioCreative Challenge. iKraph integrates insights from over 34 million PubMed abstracts and 40 public databases, enabling unprecedented scale and precision in automated knowledge discovery (AKD). 💡 What sets our work apart? We developed a causal knowledge graph and a probabilistic semantic reasoning (PSR) algorithm to infer indirect entity relationships, such as drug-disease relationships. This time-aware framework allowed us to retrospectively and prospectively validate drug repurposing and drug target predictions, something rarely done in prior work. ✅ For COVID-19, we predicted hundreds of drug candidates in real-time, one-third of which were later supported by clinical trials or publications. ✅ For cystic fibrosis, we demonstrated our predictions were often validated up to a decade later, suggesting our method could significantly accelerate the drug discovery pipeline. ✅ Across diverse diseases and common drugs, we achieved benchmark-setting recall and positive predictive rates, pushing the boundaries of what's possible in drug repurposing. We believe this study sets a new frontier in biomedical discovery and demonstrates the power of structured knowledge and interpretability in real-world applications. 📚 Read the full paper: https://lnkd.in/egYgbYT4? 📌 Access the platform: https://lnkd.in/ecxwHBK7 📂 Access the data and code: https://lnkd.in/eBp2GEnH LitCoin NLP Challenge: https://lnkd.in/e-cBc6eR Kudos to our incredible team and collaborators who made this possible! #DrugDiscovery #AI #KnowledgeGraph #Bioinformatics #MachineLearning #NatureMachineIntelligence #DrugRepurposing #LLM #BiomedicalAI #NLP #COVID19 #Insilicom #NIH #NCI #NSF #ARPA-H | 10 comments on LinkedIn

Uncover data's hidden connections using graph analytics in BigQuery. This session shows how to use BigQuery's scalable infrastructure for graph analysis directly in your data warehouse. Identify patterns, connections, and influences for fraud detection, drug discovery, social network analysis, and recommendation engines. Join us to explore the latest innovations in graphs and see real-world examples. Transform your data into actionable insights with BigQuery's powerful graph capabilities.

Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent

Graph Data Modeling Without Graph Databases: PostgreSQL and Hybrid Approaches for Agentic Systems 🖇️ Organizations implementing AI systems today face a practical challenge: maintaining multiple specialized databases (vector stores, graph databases, relational systems) creates significant operational complexity, increases costs, and introduces synchronization headaches. Companies like Writer (insight from a recent Waseem Alshikh interview with Harrison Chase) have tackled this problem by implementing graph-like structures directly within PostgreSQL, eliminating the need for separate graph databases while maintaining the necessary functionality. This approach dramatically simplifies infrastructure management, reduces the number of systems to monitor, and eliminates error-prone synchronization processes that can cost thousands of dollars in wasted resources. For enterprises focused on delivering business value rather than managing technical complexity, these PostgreSQL-based implementations offer a pragmatic path forward, though with important trade-offs when considering more sophisticated agentic systems. Writer implemented a subject-predicate-object triple structure directly in PostgreSQL tables rather than using dedicated graph databases. This approach maintains the semantic richness of knowledge graphs while leveraging PostgreSQL's maturity and scalability. Writer kept the conceptual structure of triples that underpin knowledge graphs implemented through a relational schema design. Instead of relying on native graph traversals, Writer developed a fusion decoder that reconstructs graph-like relationships at query time. This component serves as the bridge between the storage layer (PostgreSQL with its triple-inspired structure) and the language model, enabling sophisticated information retrieval without requiring a dedicated graph database's traversal capabilities. The approach focuses on query translation and result combination rather than storage structure optimization. Complementing the triple-based approach, PostgreSQL with extensions (PG Vector and PG Vector Scale) can function effectively as a vector database. This challenges the notion that specialized vector databases are necessary, Treating embeddings as derived data leads to a more natural and maintainable architecture. This reframes the database's role from storing independent vector embeddings to managing derived data that automatically synchronizes with its source. But a critical distinction between retrieval systems and agentic systems need to be made. While PostgreSQL-based approaches excel at knowledge retrieval tasks where the focus is on precision and relevance, agentic systems operate in dynamic environments where context evolves over time, previous actions influence future decisions, and contradictions need to be resolved. This distinction drives different architectural requirements and suggests potential complementary roles for different database approaches. | 15 comments on LinkedIn

Why Labeled Property Graphs Break Reasoning — Even with RDF Interop At first glance, it seems like LPGs can handle reasoning over RDF data if you just install… | 14 comments on LinkedIn

Join our slack and come to the next Graph Learning on Wednesdays (GLOW) session.https://sites.google.com/view/graph-learning-on-wedsOn March 26th, 2025, we h...

It seems the answer on whether an LMM would be able to replace the whole text-to-ontology pipeline is a resounding ‘no’. If you’re one of those who think that should be (or even is?) a ‘yes’: why, and did you do the experiments that show it’s as good as the alternatives (with the results available)? And I mean a proper ontology, not a knowledge graph with numerous duplications and contradictions and lacking constraints. For a few gentle considerations (and pointers to longer arguments) and a summary figure of processes the LLM supposedly would be replacing: see https://lnkd.in/dG_Xsv_6 | 43 comments on LinkedIn

Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.

Learn about different types of graphs and their applications in data management and AI, as well as common misconceptions, in this article by Lulit Tesfaye.

Researchers studying Alzheimer’s disease are using artificial intelligence-powered databases to accelerate the drug discovery process by making it easier to sift through vast amounts of biomedical data.

Deep stuff! We uncovered a startling link between #entropy, a bedrock concept in #physics, and how #AI can discover new ideas without stagnating. In an era… | 41 comments on LinkedIn

What’s So Special About Apache Iceberg? Apache Iceberg is one of the most fascinating technologies when it comes to standardized access to large analytic tables. And Apache Iceberg combines very well with the idea of virtual knowledge graphs.

Unpack what semantics is, walking through the benefits of a semantic approach to your data across search, usability, and standardization.

The Need for a Structured Approach, Elements of the Ontology Pipeline, the Pipeline as a Framework for Developing Knowledge Management Systems, and More!