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This paper presents a unified semantic modeling framework using a fine-tuned small language model with a multi-adapter architecture for large-scale job understanding at LinkedIn, improving performance and reducing operational complexity.
An Anthropic lead engineer's 8-step pipeline transforms chaotic data into a self-updating graph, boosting productivity by 42% and winning a $1.2M bonus.
This paper proposes a novel data-driven pipeline using LLMs to extract semantic triples from unstructured text and construct knowledge graphs for monitoring technological convergence. The method is validated on arXiv preprints and USPTO patent applications, demonstrating scalability for technology forecasting.
The author details their journey from a flat vector store to a graph database (FalkorDB) for AI memory, enabling multi-hop reasoning, temporal queries, and provenance tracking in their LocalClaw project.
This paper presents a hybrid neural-symbolic pipeline for extracting follow-up instructions from clinical notes, using BioBERT and deterministic date arithmetic. It achieves high performance (Pair F1 ~0.99) compared to generative baselines.
Graphiti is an open-source tool that builds human-like memory for AI agents using a continuously evolving, temporally-aware knowledge graph, achieving up to 18.5% higher accuracy and 90% lower latency compared to MemGPT.
RAGA is an LLM-driven autonomous agent that constructs knowledge graphs via a read-search-verify-construct cognitive loop and integrates hybrid symbolic-vector retrieval for retrieval-augmented generation, with experimental gains on scientific QA datasets.
OpenMed is an open-source local-first healthcare AI toolkit that provides entity extraction, PII de-identification, and over 1,000 specialized medical models, all running on-device with no cloud dependency.