知识图谱(Knowledge Graph),在图书情报界称为知识域可视化或知识领域映射地图,是显示知识发展进程与结构关系的一系列各种不同的图形,用可视化技术描述知识资源及其载体,挖掘、分析、构建、绘制和显示知识及它们之间的相互联系。

知识图谱是通过将应用数学、图形学、信息可视化技术、信息科学等学科的理论与方法与计量学引文分析、共现分析等方法结合,并利用可视化的图谱形象地展示学科的核心结构、发展历史、前沿领域以及整体知识架构达到多学科融合目的的现代理论。它能为学科研究提供切实的、有价值的参考。

本资源整理了知识图谱相关开源工具,涉及知识图谱公开数据集、知识图谱存储、可视化、知识融合、知识图谱计算相关工具、资料。

目录

基础架构

o图数据库工具

o三元组存储工具

o图计算框架工具

o图可视化工具

o图处理相关编程语言

o图应用服务工具

知识工程

o知识融合

知识图谱数据集

o一般数据集

o语义网络数据集

o学术数据集

学习资料

o官方文档

o学习社区资料

基础架构

图数据库工具

AgensGraph - multi-model graph database with SQL and Cypher support based on PostgreSQL

ArangoDB - highly available Multi-Model NoSQL database

Blazegraph - GPU accelerated graph database

Cayley - open source database written in Go

CosmosDB - cloud-based multi-model database with support for TinkerPop3

Dgraph - Fast, Transactional, Distributed Graph Database (open source, written in Go)

DSE Graph - Graph layer on top of DataStax Enterprise (Cassandra, SolR, Spark)

Grakn.AI - a distributed hyper-relational database for knowledge-oriented systems, i.e. a distributed knowledge base

Graphd - the Metaweb/Freebase Graph Repository

JanusGraph - an open-source, distributed graph database with pluggable storage and indexing backends

Memgraph - High Performance, In-Memory, Transactional Graph Database

Neo4j - OLTP graph database

Sparksee - makes space and performance compatible with a small footprint and a fast analysis of large networks

Stardog - RDF graph database with OLTP and OLAP support

OrientDB - Distributed Multi-Model NoSQL Database with a Graph Database Engine

TigerGraph - a complete, distributed, parallel graph computing platform for enterprise, supporting web-scale data analytics in real-time.

Nebula Graph - A truly distributed, linear scalable, lightning-fast graph database, using SQL-like query language.

HugeGraph - An open source TinkerPop 3 compliant OLTP Graph Database with pluggable storage bakcend which is similar to JanusGraph. It also supports OLAP through Spark GraphX.

  三元组存储工具

AllegroGraph - high-performance, persistent graph database that scales to billions of quads

Apache Jena - open source Java framework for building Semantic Web and Linked Data applications

Eclipse RDF4J - (formerly known as Sesame) is an open source Java framework for processing RDF data. This includes parsing, storing, inferencing and querying of/over such data. It offers an easy-to-use API that can be connected to all leading RDF storage solutions. It allows you to connect with SPARQL endpoints and create applications that leverage the power of linked data and Semantic Web.

GraphDB - enterprise ready Semantic Graph Database, compliant with W3C Standards

Virtuoso - a "Data Junction Box" that drives enterprise and individual agility by deriving a Semantic Web of Linked Data from existing data silos

Hoply - explore bigger than RAM relational data in the comfort of Python.

   图计算框架工具

Apache Giraph - an iterative graph processing system built for high scalability

Apache TinkerPop - a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP)

Apache Spark - GraphX - Apache Spark's API for graphs and graph-parallel computation

Tencent Plato - a fast distributed graph computation and machine learning framework used by WeChat.

图可视化工具

AntV G6 - Simple, easy and complete high performance graph visualization engine written in JavaScript, from Ant Financial

Gephi - Graph visualization platform software runs on Windows, Mac and Linux.

KeyLines & ReGraph - Graph visualization tookits for JavaScript and React developer from Cambridge Intelligence.

Linkurious - Linkurious is an enterprise ready on-premises graph visualization and analysis platform.

图处理相关编程语言

Cypher

Gremlin

SPARQL

GraphQL+- - The query language of Dgraph, which is based on Facebook's GraphQL

GQL - An initiative to create a standard query language for property graph database, just like SQL for relational database.

图应用服务工具

CosmosDB @ Microsoft - Azure Cosmos DB is Microsoft's globally distributed, multi-model (Key-value, Document, Column, Graph) database service.

JanusGraph @ IBM Compose

JanusGraph @ Google Cloud Platform - JanusGraph on Google Kubernetes Engine backed by Google Cloud Bigtable

JanusGraph @ Amazon Web Services Labs - The Amazon DynamoDB Storage Backend for JanusGraph

Neo4j @ Graphene

Neo4j @ Graph Story - End-to-end Graph Database hosting for Community and Enterprise Neo4j with expert help for development

Neptune @ Amazon Web Services - a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets

Graph Engine Service @ Huawei Cloud - Fully-managed, distributed, at-scale graph query and analysis service that provides a visualized interactive analytics platform.

Graph Database (beta) @ Aliyun (Alibaba Cloud) - highly reliable and available property graph database that supports ACID and TinkerPop Gremlin query language.

Tencent Knowledge Graph @ Tencent Cloud - One stop platform for Graph database, computing and visualization. Currently available in beta test and only in Chinese.

知识工程

YAGA-NAGA - Harvesting, Searching, and Ranking Knowledge from the Web

知识融合

Dedupe - dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data.

LIMES - Link Discovery Framework for Metric Spaces.

知识图谱数据集

    一般数据集

BabelNet - Both a multilingual encyclopedic dictionary, with lexicographic and encyclopedic coverage of terms, and a semantic network which connects concepts and named entities in a very large network of semantic relations, made up of about 16 million entries, called Babel synsets. Each Babel synset represents a given meaning and contains all the synonyms which express that meaning in a range of different languages.

Wikidata - Wikidata is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia, Wikimedia Commons, the other wikis of the Wikimedia movement, and to anyone in the world.

Google Knowledge Graph - Google’s Knowledge Graph has millions of entries that describe real-world entities like people, places, and things.

DBpedia - DBpedia is a crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects.

XLore - A large-scale English-Chinese bilingual knowledge graph by structuring and integrating Chinese Wikipedia, English Wikipedia, French Wikipedia, and Baidu Baike.

The GDELT Project - The GDELT Project monitors the world's broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events driving our global society every second of every day, creating a free open platform for computing on the entire world.

YAGO - A huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames. Currently, YAGO has knowledge of more than 10 million entities (like persons, organizations, cities, etc.) and contains more than 120 million facts about these entities. The source code of YAGO is in this Github repo.

Zhishi.me - Knowledge Graph data extracted from the largest Chinese encyclopedias, Baidu Baike, Hudong Baike and Chinese Wikipedia.

语义网络数据集

ConceptNet - ConceptNet is a freely-available semantic network, designed to help computers understand the meanings of words that people use.

Microsoft Concept Graph - For Short Text Understanding

OpenHowNet - An Open Sememe-based Lexical Knowledge Base in Chinese.

WordNet - A free large lexical database of English from Princeton University.

学术数据集

AMiner - Aminer aims to provide comprehensive search and mining services for researcher social networks.

Microsoft Academic - Microsoft Academic (MA) employs advances in machine learning, semantic inference and knowledge discovery to help you explore scholarly information in more powerful ways than ever before.

AceMap - Academic search engine based on knowledge graph which includes entities like paper, author, institution and etc.

学习资料

官方文档

Cypher - reference documentation

Gremlin - reference documentation

学习社区资料

Graph Book - TinkerPop3 centric book written by Kelvin R. Lawrence

SQL2Gremlin - transition from SQL to Gremlin by Daniel Kuppitz

The Gremlin Compendium - minimum survival kit for any Gremlin user, 10 blog post series by Doan DuyHai

相关会议

Graph Connect - powered by Neo4j

Graph Day - an Independent Graph Conference from the Data Day folks

知识图谱(KG)存储、可视化、公开数据集、图计算、图编程工具分享相关推荐

  1. 【知识图谱】(task3)知识图谱的存储和查询

    note 用图数据库的场景: 高性能关系查询:需要快速遍历许多复杂关系的任何用例,如欺诈检测,社交网络分析,网络和数据库基础设施等: 模型的灵活性:任何依赖于添加新数据而不会中断现有查询池的用例.模型 ...

  2. 基于Neo4j中医方剂药材知识图谱大数据可视化分析系统的设计与开发

    基于Neo4j中医方剂药材知识图谱大数据可视化分析系统的设计与开发 设计背景 这个系统的开发初衷是笔者希望通过这个系统来学习一下Neo4j的相关技术,包括与python.java的对接.可视化等方面, ...

  3. 浅析图数据库市场/图数据库/图计算/图引擎/图神经网络/知识图谱.

    欢迎大家一起交流,本人对于图方面的做过一些市场洞察以及Mapping. 图论的历史 **第一阶段:**从1736年到19世纪中叶1736年,欧拉(L·Euler)研究哥尼斯堡城(Koni gsberg ...

  4. 综述 | 358 篇论文, 最新知识图谱KG综述

    进NLP群->加入NLP交流群 来自:图神经网络与推荐系统 获取结构化的人类知识是设计高级人工智能的重要基础.为此,早期研究者做了大量工作以从不同数据源中自动提取可以提供有用信息(事实)的数据模 ...

  5. 358 篇论文, 最新知识图谱KG综述!

    获取结构化的人类知识是设计高级人工智能的重要基础.为此,早期研究者做了大量工作以从不同数据源中自动提取可以提供有用信息(事实)的数据模式:进一步地,学者的研究兴趣转向自动构建概念化的结构良好的知识图谱 ...

  6. 从知识图谱到文本:结合局部和全局图信息生成更高质量的文本

    论文标题: Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs 论文作者: Leonar ...

  7. 【知识图谱】什么是知识图谱?知识图谱的应用。知识图谱的数据模型(三元组 模型、属性图模型)。西游记中的知识图谱。

    目录 一.什么是知识图谱?知识.图谱 二.举例.知识图谱的应用 三.知识图谱的数据模型 四.通过<西游记>人物关系图谱,认识知识图谱本体 一.什么是知识图谱?知识.图谱 有些同学理解,知识 ...

  8. 大数据智能洞察、知识图谱、数据可视化技术

    智能五大技术方向 知识工程 面向垂直行业,结合专家知识.多源异构的碎片化知识和组织智能,引领从大数据分析到大知识工程进而大智慧系统的研发和落地应用.构建行业知识图谱,实现智能推理与知识服务,推进多机多 ...

  9. 农业知识图谱(KG):农业领域的信息检索,命名实体识别,关系抽取,分类树构建,数据挖掘...

    向AI转型的程序员都关注了这个号

最新文章

  1. <论文阅读>CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and...
  2. 走出回归测试困境,爱奇艺精准测试体系建设
  3. 【MySQL】数据安全性讨论思维导图
  4. Linux预备知识(二):进程空间地划分-用户空间/内核空间
  5. 手势识别之平移、缩放、长按、旋转、滑动
  6. 容器监控实践—Heapster
  7. star-cd linux安装,linux 使用PXE方式,kickstar网络安装系统
  8. 400. 第 N 位数字
  9. 【笔记】MySQL的基础学习(二)
  10. 酷狗直播联手腾讯游戏破圈“组团” 游戏直播正版化杀入重量级玩家
  11. python周志_python第一周总结
  12. jpeg图片转换成word
  13. python中33个保留字的含义_Python的保留字。这是什么意思?
  14. 【入门必看-算法基础知识讲解】小白都也能看得懂
  15. 论文常用图表三:盒图 Boxplot【MATLAB】
  16. deepinIDEA快捷方式无法启动解决
  17. DataGrip使用技巧
  18. win10企业版跟win10专业版有什么区别,win10系统版本区别
  19. 我就是那个错过了乔布斯的变态老码农
  20. Visual Studio版本汇总比较

热门文章

  1. 项目管理的十大知识领域
  2. PMP知识点:项目管理十大知识领域和47个过程
  3. 怎么才能把windows里的文件复制到虚拟机的linux中去
  4. IPv6:不发展才是最大的不安全
  5. 郑州大学计算机硕士分数线,2021郑州大学考研复试线:热门专业超过400分,机械类专业仅263分...
  6. 【大数据竞赛】2022MathorCup大数据挑战赛 B题 北京移动用户体验影响因素研究 题目分析
  7. 跟着鸟哥学linux【一】
  8. ADF单位根检验方法
  9. 【Linux】Linux指令串讲
  10. Linux虚拟机配置网络代理配置yum源