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Organizations have invested heavily in warehouses, lakehouses, dashboards, knowledge graphs, and vector databases. These technologies are useful, but technology alone does not create a dependable business capability. Data products combine data, context, ownership, quality, governance, and access around a defined consumer need. They give people and AI systems information they can find, understand, trust, and reuse.Data & Infrastructure · Sep 8, 2026

Data Products: 5 Powerful Ways They Strengthen Enterprise AI

Organizations have invested heavily in warehouses, lakehouses, dashboards, knowledge graphs, and vector databases. These technologies are useful, but technology alone does not create a dependable business capability. Data products combine data, context, ownership, quality, governance, and access around a defined consumer need. They give people and AI systems information they can find, understand, trust, and reuse.

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Large language models struggle with one-shot SPARQL generation for multi-hop knowledge graph questions, but training them as agentic systems with reinforcement learning enables reliable, iterative query refinement using execution feedback. A compact 3B-parameter model trained purely via outcome-driven RL learns to recover from errors and significantly outperforms zero-shot baselines, demonstrating a scalable blueprint for teaching AI agents to use formal symbolic tools effectively.AI & Data Science · Dec 22, 2025

Agentic Reinforcement Learning for Improving Knowledge Graph Question Answering Reliability

Large language models struggle with one-shot SPARQL generation for multi-hop knowledge graph questions, but training them as agentic systems with reinforcement learning enables reliable, iterative query refinement using execution feedback. A compact 3B-parameter model trained purely via outcome-driven RL learns to recover from errors and significantly outperforms zero-shot baselines, demonstrating a scalable blueprint for teaching AI agents to use formal symbolic tools effectively.

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SPARQL-LLM: From Natural Language to Executable Knowledge Graph QueriesAI & Data Science · Dec 19, 2025

SPARQL-LLM: From Natural Language to Executable Knowledge Graph Queries

Translating natural language questions into executable SPARQL queries remains a major barrier to accessing knowledge graphs at scale. While large language models have shown promise in this area, many existing approaches such as Graph-RAG struggle with reliability, cost, and production readiness, especially when applied to complex or federated datasets. This post presents a high-level, executive-friendly overview of the SPARQL-LLM architecture published in ACM Transactions on the Web (2025).

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Building the Enterprise Intelligence Core with Knowledge GraphsAI & Data Science · Dec 11, 2025

Building the Enterprise Intelligence Core with Knowledge Graphs

Executive Takeaways Expanded Insights Organizations today sit on massive amounts of data, spreadsheets, databases, manufacturing systems, quality documents, SOPs, emails, tech transfer packages, PDFs, and more. The challenge isn’t data collection; it’s fragmentation. Each system knows a little, but none of them know enough. By consolidating these sources into a Knowledge Graph, companies create a […]

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