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This paper presents a production extraction layer that converts heterogeneous documents into an ontology-aligned knowledge graph using a locally hosted tuned Qwen LLM, with ontology-guided prompts, multi-stage deduplication, and embedding-based resolution. Evaluation on intelligence corpora improved search recall from about 70 to 95 percent with no false merges.
This paper shares three practical lessons from building and evaluating a self-serve entity resolution pipeline on six benchmarks: no single matching algorithm wins everywhere, precision and recall require separate fixes, and false-positive links can chain-merge unrelated entities.
A tutorial explaining programmatic advertising and how to model physical advertising using Python, covering entity resolution, audience data, availability, and dynamic pricing.
The article argues that the default approach to agent memory (saving all messages and using RAG) fails due to lack of suppression, identity, and relation tracking, and proposes using a lightweight temporal knowledge graph to model the user's world instead.
This paper presents a two-phase entity resolution pipeline to link narrator names from the Sanadset corpus to two biographical databases, enabling construction of a large transmission graph enriched with cross-source metadata.
A detailed guide on optimizing knowledge graph ingestion for AI agents, presenting a five-step pipeline (extraction, resolution, embedding, deduplication, routing) to prevent graph corruption and improve retrieval quality.
This paper presents Structure-Guided Entity Resolution (SGER), a framework that fine-tunes LLMs through curriculum learning for robust person name matching in linguistically diverse contexts, achieving 99.02% accuracy on Indian identity data and deployed at Dream11.
This thread discusses best practices for building unified memory layers with knowledge graphs, emphasizing the separation of entity resolution (naming) from deduplication (identity) to avoid graph corruption. It also highlights using orchestration tools like PrefectIO to manage expensive LLM extraction pipelines with checkpointing and caching.
A developer shares an architecture using Neo4j knowledge graphs with typed entities and deduplication to solve the problem of AI agents forgetting entity identity across sessions, moving beyond flat files and vector stores.