# memect/kg-beijing

北京知识图谱学习小组

Repository: https://github.com/memect/kg-beijing
Canonical: https://ross.abutalabs.com/products/kg-beijing
Language: Jupyter Notebook
License Family: other
Last push: 2016-11-01T20:39:43+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3754, "days_push": 3592, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1677, forks 486 (observed 2026-08-28T04:05:20.920548+00:00)

## What it is
A study group repository for the Beijing Knowledge Graph Learning Group, containing a wiki-based curriculum covering knowledge extraction, representation, storage, and retrieval. It is primarily educational material with Jupyter notebooks and curated references rather than usable software.

## Use cases
- learn the basics of knowledge graphs
- find a structured curriculum for knowledge extraction and representation
- study how knowledge graphs are stored and queried
- find references for learning semantic web technologies
- follow a week-by-week knowledge graph study plan

## When to choose
- you want a guided, topic-organized introduction to knowledge graphs
- you prefer curated reading lists and outlines over production code
- you read Chinese and want community study materials

## When to avoid
- you need maintained, production-ready knowledge graph software
- you want up-to-date materials reflecting modern LLM-era knowledge graph tooling
- you need a licensed, reusable codebase

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: nlp, search-engine, database, developer-tools
- domain: artificial-intelligence, databases, education, tutorials
- platform: python, cross-platform
- tags: knowledge-graph, study-group, semantic-web, chinese-language, jupyter-notebooks, natural-language-processing

## Member repositories
- memect/kg-beijing (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:20.920548+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 2026-08-30T03:41:37.022719+00:00, confidence not recorded.
  - readme: https://github.com/memect/kg-beijing (fetched 2026-08-28T04:05:20.920548+00:00, sha 4b0903be6cf4)
- Data as of 2026-08-30T08:39:29.467469+00:00.