{"id":28413560,"url":"https://github.com/myhhub/knowledgegraph","last_synced_at":"2025-06-24T23:31:04.305Z","repository":{"id":41389807,"uuid":"215725173","full_name":"myhhub/KnowledgeGraph","owner":"myhhub","description":"knowledge graph知识图谱,从零开始构建知识图谱","archived":false,"fork":false,"pushed_at":"2023-09-18T07:13:59.000Z","size":2681,"stargazers_count":1368,"open_issues_count":0,"forks_count":154,"subscribers_count":47,"default_branch":"master","last_synced_at":"2025-06-03T15:28:51.964Z","etag":null,"topics":["knowledge-graph","knowledge-management","named-entity-recognition","question-answering","relation-extraction"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/myhhub.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-10-17T07:02:55.000Z","updated_at":"2025-06-03T03:22:57.000Z","dependencies_parsed_at":"2025-05-18T12:11:41.185Z","dependency_job_id":"65ceb39a-891e-4875-9954-224c4911c69f","html_url":"https://github.com/myhhub/KnowledgeGraph","commit_stats":null,"previous_names":["myhhub/zero_knowledge_graph"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/myhhub/KnowledgeGraph","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/myhhub%2FKnowledgeGraph","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/myhhub%2FKnowledgeGraph/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/myhhub%2FKnowledgeGraph/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/myhhub%2FKnowledgeGraph/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/myhhub","download_url":"https://codeload.github.com/myhhub/KnowledgeGraph/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/myhhub%2FKnowledgeGraph/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261774587,"owners_count":23207763,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["knowledge-graph","knowledge-management","named-entity-recognition","question-answering","relation-extraction"],"created_at":"2025-06-03T04:35:36.956Z","updated_at":"2025-06-24T23:31:04.300Z","avatar_url":"https://github.com/myhhub.png","language":"Python","readme":"\n**knowledge graph,从零开始构建知识图谱，涵盖基础知识、构建理论、构建实战，从理论到实现。**\n\n## 一、基础知识\n1. [知识图谱基础 之 一.知识图谱基本概念](https://www.ljjyy.com/archives/2019/11/100629.html)\n2. [知识图谱基础 之 二.知识表示与知识建模](https://www.ljjyy.com/archives/2019/11/100605.html)\n3. [知识图谱基础 之 三.知识抽取](https://www.ljjyy.com/archives/2019/11/100606.html)\n4. [知识图谱基础 之 四.知识挖掘](https://www.ljjyy.com/archives/2019/11/100607.html)\n5. [知识图谱基础 之 五.知识存储](https://www.ljjyy.com/archives/2019/11/100608.html)\n6. [知识图谱基础 之 六.知识融合](https://www.ljjyy.com/archives/2019/11/100609.html)\n7. [知识图谱基础 之 七.知识推理](https://www.ljjyy.com/archives/2019/11/100610.html)\n8. [知识图谱基础 之 八.语义搜索](https://www.ljjyy.com/archives/2019/11/100611.html)\n9. [知识图谱基础 之 九.知识问答](https://www.ljjyy.com/archives/2019/11/100612.html)\n\n## 二、论文方面(构建理论)\n\n论文主要推荐两篇文章\n\n1. 清华大学杨玉基的“[一种准确而高效的领域知识图谱构建方法](http://www.doc88.com/p-9979131856838.html)”。讲述了怎么通过4步进行半自动话的构建领域知识图谱，参考价值极大，步骤清晰。\n\n2. 华东理工大学胡芳槐的博士论文“[基于多种数据源的中文知识图谱构建方法研究](http://www.doc88.com/p-0784652186719.html)”，这篇文章讲了怎么通过多数据源去构建通用知识图谱和行业知识图谱，比较详细的介绍了一些构建技术，具备一定参考价值。\n\n## 三、博客方面(构建实战)\n\n《从零开始学习知识图谱》系列文章，通过实战码代码，一步一步教你怎么构建一个电影领域知识图谱及百科知识图谱。\n1. [从零开始学习知识图谱（一）：电影知识图谱构建 1.半结构化数据的获取](https://www.ljjyy.com/archives/2019/10/100591.html)\n2. [从零开始学习知识图谱（二）：电影知识图谱构建 2.结构化数据到RDF以及基于Apache jena交互](https://www.ljjyy.com/archives/2019/10/100592.html)\n3. [从零开始学习知识图谱（三）：电影知识图谱构建 3.基于REfO的简单知识问答](https://www.ljjyy.com/archives/2019/10/100593.html)\n4. [从零开始学习知识图谱（四）：电影知识图谱构建 4.基于ElasticSearch的简单语义搜索](https://www.ljjyy.com/archives/2019/10/100594.html)\n5. [从零开始学习知识图谱（五）：电影知识图谱构建 5.基于Deepdive非结构化文本关系抽取](https://www.ljjyy.com/archives/2019/10/100595.html)\n6. [从零开始学习知识图谱（六）：电影知识图谱构建 6.将关系型数据存入图数据库Neo4j](https://www.ljjyy.com/archives/2019/10/100596.html)\n7. [从零开始学习知识图谱（七）：百科知识图谱构建 1.百科类知识抽取](https://www.ljjyy.com/archives/2019/10/100597.html)\n8. [从零开始学习知识图谱（八）：百科知识图谱构建 2.数据清洗及存入图数据库Neo4j](https://www.ljjyy.com/archives/2019/10/100598.html)\n9. [从零开始学习知识图谱（九）：百科知识图谱构建 3.基于TensorFlow神经网络关系抽取的数据集构建(使用OpenNRE)](https://www.ljjyy.com/archives/2019/10/100599.html)\n10. [从零开始学习知识图谱（十）：百科知识图谱构建 4.结构化数据到RDF](https://www.ljjyy.com/archives/2019/10/100600.html)\n11. [从零开始学习知识图谱（十一）：百科知识图谱构建 5.Jena使用及SPARQL查询](https://www.ljjyy.com/archives/2019/10/100601.html)\n12. [从零开始学习知识图谱（十二）：百科知识图谱构建 6.基于Silk知识融合](https://www.ljjyy.com/archives/2019/10/100602.html)\n13. [从零开始学习知识图谱（十三）：百科知识图谱构建 7.基于Silk批量知识融合](https://www.ljjyy.com/archives/2019/10/100603.html)\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmyhhub%2Fknowledgegraph","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmyhhub%2Fknowledgegraph","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmyhhub%2Fknowledgegraph/lists"}