Just released a new notebook exploring Semantic Entity Resolution & Extraction using DSPy (Community) and Google's new LangExtract library. Inspired by Russell Jurney’s excellent work on semantic entity resolution, this demo follows his approach of combining: ✅ embeddings, ✅ kNN blocking, ✅ and LLM matching with DSPy (Community). On top of that, I added a general extraction layer to test-drive LangExtract, a Gemini-powered, open-source Python library for reliable structured information extraction. The goal? Detect and merge mentions of the same real-world entities across text. It’s an end-to-end flow tackling one of the most persistent data challenges. Check it out, experiment with your own data, 𝐞𝐧𝐣𝐨𝐲 𝐭𝐡𝐞 𝐬𝐮𝐦𝐦𝐞𝐫 and let me know your thoughts! cc Paco Nathan you might like this 😉 https://wor.ai/8kQ2qa

Building Knowledge Graphs that Power Autonomous Agents

I've spent long, hard years learning how to talk about knowledge graphs and semantics with software engineers who have little training in linguistics. I feel quite fluent at this point, after investing huge amounts of effort into understanding statistics (I was a humanities undergrad) and into unpac

𝙏𝙝𝙤𝙪𝙜𝙝𝙩 𝙛𝙤𝙧 𝙩𝙝𝙚 𝙙𝙖𝙮: I've been mulling over how both OWL and SHACL can be employed during the decision-making phase for AI Agents when using a knowledge graph instead of relying on an LLM that hallucinates. In this way, the LLM can still be used for assessment and sensory feedback, but it augments the graph, not the other way around. OWL and SHACL serve different roles. SHACL is not just a preprocessing validator; it can play an active role in constraining, guiding, or triggering decisions, especially when integrated into AI pipelines. However, OWL is typically more central to inferencing and reasoning tasks. SHACL can actively participate in decision-making, especially when decisions require data integrity, constraint enforcement, or trigger-based logic. In complex agents, OWL provides the inferencing engine, while SHACL acts as the constraint gatekeeper and occasionally contributes to rule-based decision-making. For example, an AI agent processes RDF data describing an applicant's skills, degree, and experience. SHACL validates the data's structure, ensuring required fields are present and correctly formatted. OWL reasoning infers that the applicant is qualified for a technical role and matches the profile of a backend developer. SHACL is then used again to check policy compliance. With all checks passed, the applicant is shortlisted, and a follow-up email is triggered. In AI agent decision-making, OWL and SHACL often work together in complementary ways. SHACL is commonly used as a preprocessing step to validate incoming RDF data. If the data fails validation, it's flagged or excluded, ensuring only clean, structurally sound data reaches the OWL reasoner. In this role, SHACL acts as a gatekeeper. They can also operate in parallel or in an interleaved manner within a pipeline. As decisions evolve, SHACL shapes may be checked mid-process. Some AI agents even use SHACL as a rule engine—to trigger alerts, detect actionable patterns, or constrain reasoning paths—while OWL continues to handle more complex semantic inferences, such as class hierarchies or property logic. Finally, SHACL can augment decision-making by confirming whether OWL-inferred actions comply with specific constraints. OWL may infer that “A is a type of B, so do X,” and SHACL then determines whether doing X adheres to a policy or requirement. Because SHACL supports closed-world assumptions (which OWL does not), it plays a valuable role in enforcing policies or compliance rules during decision execution. Illustrated:

Part 6 of 6 in the series “LLMs Need Knowledge Graphs. Use RDF or End Up Rebuilding It.”

I've been working on autonomous AI systems, and wanted to share some thoughts on what I believe makes them effective. The challenge isn't just making AI that follows instructions well, but creating systems that can reason, and act independently.

LLMs already contain overlapping world models. You just have to ask them right. Ontologists reply to an LLM output, “That’s not a real ontology—it’s not a formal conceptualization.” But that’s just the No True Scotsman fallacy dressed up in OWL. Boring. Not growth-oriented. Look forward, angel. A foundation model is a compression of human knowledge. The real problem isn't that we "lack a conceptualization". The real problem with an FM is that they contain too many. FMs contain conceptualizations—plural. Messy? Sure. But usable. At Stardog, we’re turning this latent structure into real ontologies using symbolic knowledge distillation. Prompt orchestration → structure extraction → formal encoding. OWL, SHACL, and friends. Shake till mixed. Rinse. Repeat. Secret sauce simmered and reduced. This isn't theoretical hard. We avoid that. It’s merely engineering hard. We LTF into that! But the payoff means bootstrapping rich, new ontologies at scale: faster, cheaper, with lineage. It's the intersection of FM latent space, formal ontology, and user intent expressed via CQs. We call it the Symbolic Latent Layer (SLL). Cute eh? The future of enterprise AI isn’t just documents. It’s distilling structured symbolic knowledge from LLMs and plugging it into agents, workflows, and reasoning engines. You don’t need a priesthood to get a formal ontology anymore. You need a good prompt and a smarter pipeline and the right EKG platform. There's a lot more to say about this so I said it at Stardog Labs https://lnkd.in/eY5Sibed | 17 comments on LinkedIn

Want to Fix LLM Hallucination? Neurosymbolic Alone Won’t Cut It The Conversation’s new piece makes a clear case for neurosymbolic AI—integrating symbolic logic with statistical learning—as the long-term fix for LLM hallucinations. It’s a timely and necessary argument: “No matter how large a language model gets, it can’t escape its fundamental lack of grounding in rules, logic, or real-world structure. Hallucination isn’t a bug, it’s the default.” But what’s crucial—and often glossed over—is that symbolic logic alone isn’t enough. The real leap comes from adding formal ontologies and semantic constraints that make meaning machine-computable. OWL, Shapes Constraint Language (SHACL), and frameworks like BFO, Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), the Suggested Upper Merged Ontology (SUMO), and the Common Core Ontologies (CCO) don’t just “represent rules”—they define what exists, what can relate, and under what conditions inference is valid. That’s the difference between “decorating” a knowledge graph and engineering one that can detect, explain, and prevent hallucinations in practice. I’d go further: • Most enterprise LLM hallucinations are just semantic errors—mislabeling, misattribution, or class confusion that only formal ontologies can prevent. • Neurosymbolic systems only deliver if their symbolic half is grounded in ontological reality, not just handcrafted rules or taxonomies. The upshot: We need to move beyond mere integration of symbols and neurons. We need semantic scaffolding—ontologies as infrastructure—to ensure AI isn’t just fluent, but actually right. Curious if others are layering formal ontologies (BFO, DOLCE, SUMO) into their AI stacks yet? Or are we still hoping that more compute and prompt engineering will do the trick? #NeuroSymbolicAI #SemanticAI #Ontology #LLMs #AIHallucination #KnowledgeGraphs #AITrust #AIReasoning

I'm happy to share the draft of the "Semantically Composable Architectures" mini-paper. It is the culmination of approximately four years' work, which began with Coreless Architectures and has now evolved into something much bigger. LLMs are impressive, but a real breakthrough will occur once we surpass the cognitive capabilities of a single human brain. Enabling autonomous large-scale system reverse engineering and large-scale autonomous transformation with minimal to no human involvement, while still making it understandable to humans if they choose to, is a central pillar of making truly groundbreaking changes. We hope the ideas we shared will be beneficial to humanity and advance our civilization further. It is not final and will require some clarification and improvements, but the key concepts are present. Happy to hear your thoughts and feedback. Some of these concepts underpin the design of the Product X system. Part of the core team + external contribution: Andrew Barsukov Andrey Kolodnitsky Sapta Girisa N Keith E. Glendon Gurpreet Sachdeva Saurav Chandra Mike Diachenko Oleh Sinkevych | 13 comments on LinkedIn

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