Entity resolution (also known as data matching, data linkage, record linkage, and many other terms) is the task of finding entities in a dataset that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Entity resolution is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.
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Autonomous recursive language-model investigation agent.
Your agents forget. Neotoma makes them remember.
Scalable identity resolution, entity resolution, data mastering and deduplication using ML
DuckDB community extension for locality-sensitive hashing (LSH)
Canonical football entity register — 475k+ players, coaches, and teams mapped across 40+ providers. Pure Clojure, sub-microsecond lookups, zero mutable state.
Curated list of awesome software and resources for Senzing, The First Real-Time AI for Entity Resolution.
Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends
The SQL/Ibis powered sklearn of record linkage
Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. This produces a Streamlit app, with MLOps instrumentation.
🟡 Zero-config entity resolution for Python & TypeScript. Dedupe, match, and build golden records — self-verifying auto-config, MCP/REST/A2A servers, edge-safe core, 97% F1 out of the box.
ReCiter: an enterprise open source author disambiguation system for academic institutions
Multilingual supplier deduplication and merge pipeline using blocking, fuzzy matching, and embeddings
Reproducibility experiments for Generalized Supervised Meta-blocking
Open-source OSINT methodology for accountability journalism: multi-source verification gate, entity resolution (Splink 4), ACH framework, red team adversarial review. CC BY-SA 4.0
On-device Speech-to-Intent engine powered by deep learning
CJK-native master data matching engine — multi-signal phonetic, visual, and normalization matching for Chinese/Japanese/Korean records. Built in Rust, runs in the browser via WASM.
Open product-intelligence engine that turns messy retail and manufacturer page data into clean, canonical, comparable product records.
A production-grade Entity Resolution and Intelligence Reporting pipeline built with FastAPI, PostgreSQL, Celery, and OpenSearch. Features fuzzy matching, sanctions screening, and JWT-secured analytics.
Hosted MCP server for identity resolution and write guardrails for AI agents. Read-only proxy to the Anchord API.
Created by Halbert L. Dunn
Released 1946