# HazyResearch/deepdive

DeepDive

Repository: https://github.com/HazyResearch/deepdive
Canonical: https://ross.abutalabs.com/products/deepdive
Language: Shell
License Family: other
Last push: 2022-06-09T05:49:44+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4550, "days_push": 1546, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1979, forks 533 (observed 2026-08-28T04:06:01.628170+00:00)

## What it is
DeepDive is a Stanford-developed system for extracting structured data from unstructured sources and building knowledge bases using distant supervision and probabilistic inference. It lets users declare extraction and inference rules over databases rather than writing feature engineering code.

## Use cases
- build a knowledge base from scientific papers
- extract entities and relations from text documents
- populate a database from unstructured text
- train relation extractors without labeled data
- run probabilistic inference over extracted facts
- perform information extraction over dark data

## When to choose
- you need to construct structured knowledge bases from large unstructured corpora
- you want distant-supervision-based extraction without hand-labeling training data
- you work with domain-specific text like scientific or legal documents

## When to avoid
- you need actively maintained software with new features or guaranteed support
- you want a modern weak-supervision pipeline - consider the Snorkel project instead
- you need a lightweight library rather than a full system with database dependencies

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, nlp, database, etl, data-science
- domain: data-science, databases, big-data
- platform: cli
- tags: information-extraction, knowledge-base-construction, distant-supervision, probabilistic-inference, stanford, dark-data, natural-language-processing, linux, macos

## Member repositories
- HazyResearch/deepdive (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.628170+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:04:40.992206+00:00, confidence not recorded.
  - readme: https://github.com/HazyResearch/deepdive (fetched 2026-08-28T04:06:01.628170+00:00, sha e346826cd46e)
- Data as of 2026-08-30T08:39:29.467469+00:00.