Displaying 1 to 20 from 25 results

Awesome-Coder - :books: Interactive MindMap, RoadMap(Learning Path/Interview Questions), xCompass, Weekly for Developer, to Learn Everything in ITCS :dizzy: 程序员的技术视野、知识管理与职业规划,提高个人与团队的研发效能

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:books: Interactive MindMap, RoadMap(Learning Path/Interview Questions), xCompass, Weekly for Developer, to Learn Everything in ITCS :dizzy: 程序员的技术视野、知识管理与职业规划,提高个人与团队的研发效能

Coder-Roadmap - :books: Interactive MindMap, RoadMap(Learning Path/Interview Questions), xCompass, Weekly for Developer, to Learn Everything in ITCS :dizzy: 程序员的技术视野、知识管理与职业规划,提高个人与团队的研发效能

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:books: Interactive MindMap, RoadMap(Learning Path/Interview Questions), xCompass, Weekly for Developer, to Learn Everything in ITCS :dizzy: 程序员的技术视野、知识管理与职业规划,提高个人与团队的研发效能

awesome-network-embedding - A curated list of network embedding techniques.

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Also called network representation learning, graph embedding, knowledge embedding, etc. The task is to learn the representations of the vertices from a given network.




Geist - A personal knowledge base with a focus on connections

  •    Javascript

A personal knowledge base with a focus on connections. The personal knowledge base (PKB) is basically a wiki system for personal use. Unlike a public wiki which most often focuses on representing facts, the PKB can contain subjective material relevant to the person or persons using the PKB. Therefore the desired properties of a PKB are equivalent to those of the personal wiki.

atomspace - The OpenCog hypergraph database, query system and rule engine

  •    C++

The OpenCog AtomSpace is a knowledge representation (KR) database and the associated query/reasoning engine to fetch and manipulate that data, and perform reasoning on it. Data is represented in the form of graphs, and more generally, as hypergraphs; thus the AtomSpace is a kind of graph database, the query engine is a general graph re-writing system, and the rule-engine is a generalized rule-driven inferencing system. The vertices and edges of a graph, known as "Atoms", are used to represent not only "data", but also "procedures"; thus, many graphs are executable programs as well as data structures. The AtomSpace is a platform for building Artificial General Intelligence (AGI) systems. It provides the central knowledge representation component for OpenCog. As such, it is a fairly mature component, on which a lot of other systems are built, and which depend on it for stable, correct operation in a day-to-day production environment.

graph-pattern-learner - Evolutionary Graph Pattern Learner that learns SPARQL queries for a given set of source-target-pairs from an endpoint

  •    Python

In this repository you find the code for a graph pattern learner. Given a list of source-target-pairs and a SPARQL endpoint, it will try to learn SPARQL patterns. Given a source, the learned patterns will try to lead you to the right target. As you can immediately see, associations don't only follow a single pattern. Our algorithm is designed to be able to deal with this. It will try to learn several patterns, which in combination model your input list of source-target-pairs. If your list of source-target-pairs is less complicated, the algorithm will happily terminate earlier.


deepamehta - Platform for collaboration and knowledge management

  •    Java

DeepaMehta 4 is a platform for collaboration and knowledge management. The vision of DeepaMehta is a Post-Desktop Metaphor user interface that abolishes applications, windows, files, and folders in favor of stable personal views of contextual content. The goal of DeepaMehta is to provide knowledge workers of all kind a cognitive adequate work environment, right after your desktop computer or laptop has booted up. Technically DeepaMehta 4 is made of Server-side: Java, Neo4j, Lucene, Apache Felix (OSGi), Jetty, Jersey, Thymeleaf (optional), Neo4j Spatial (optional), Jetty WebSocket (optional), Karaf (optional), Pax Web (optional). Client-side: Javascript, jQuery, jQuery-UI, HTML5 Canvas, CKEditor, OpenLayers (optional), D3.js (optional).

KBox - 📦 The Knowledge Box

  •    Java

KBox is an abbreviation for Knowledge Box. The rationale behind KBox is to allow users to have a single place to share resources and knowledge among different applications as well as instances. Moreover, working on top of RDF model, KBox is a natural extension of the Web on your computer. Systems usually deal with resources and knowledge that are often duplicated among several instances. For instance, when using the Stanford NLP library the resources and knowledge inside the library are duplicated among different applications. The idea is to have a common repository where users can share resources without duplication. In order to do that, we bring the RDF concept to bridge the gap among reource publishig, storing and locating.

nexus - Blue Brain Nexus - A knowledge graph for data-driven science

  •    Jupyter

The Blue Brain Nexus is a provenance based, semantic enabled data management platform enabling the definition of an arbitrary domain of application for which there is a need to create and manage entities as well as their relations (e.g. provenance). For example, the domain of application managed by the Nexus platform deployed at Blue Brain is to digitally reconstruct and simulate the brain. Register and manage neuroscience relevant entity types through schemas that can reuse or extend community defined schemas (e.g. schema.org, bioschema.org, W3C-PROV) and ontologies (e.g. brain parcellation schemes, cell types, taxonomy).

Piggydb - Piggydb is a Web notebook application that provides you with a platform to build your knowledge personally or collaboratively

  •    Java

Piggydb is a Web notebook application that provides you with a platform to build your knowledge personally or collaboratively.

DataReused - Get Data Reused

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Data reuse is the process of leveraging an existing dataset to fulfill a valuable mission with positive reward. The valuation of dataset closely depends on how well it gets reused. A dataset, regardless how hard it was produced and how easy it could be reused, will perish if it is not used by anyone. Data reuse with positive reward increases social welfare and keeps data reuse ecosystem healthy. There are a lot of ways to get data reused, e.g. data visualization, data sharing, data conversion, data linking, data fusion, and data cleaning. The issue tracker https://github.com/lidingpku/DataReused/issues is repurposed to host my thoughts and dicussions on relevant topics.

Knowledge-Graph-Analysis-Programming-Exercises - Exercises for the Analysis of Knowledge Graphs

  •    Jupyter

This is a repository, which allows interested students and researchers to perform hands-on analysis of knowledge graphs. It is primarily developed as part of the knowledge graph analysis lecture of the SDA Group at the University of Bonn. However, the material itself is also useful for anyone else. Knowledge graphs represent knowledge in terms of entities and their relationships as shown in the figure below. The nodes of a knowledge graph are the objects which are relevant in your domain and have a unique identifier (so they represent real world "things" rather than just a string label). The edges are the connections between those objects. Since knowledge graphs are intuitive and enjoy a number of benefits, they became very popular over the past decade. Some of the most well known knowledge graphs are the Google Knowledge Graph (a major component of Google Search and other services), DBpedia (a knowledge graph extracted from Wikipedia), Wikidata, YAGO, the Facebook Social Graph, Satori (Microsoft Knowledge Graph) and the LinkedIn Knowledge Graph.

KBE - Node

  •    Web

This is a node.js application that aims at extracting the knowledge represented in the Google infoboxes (aka Google Knowlege Graph Panel). Moreover, you can always check the corresponding CSS class name selectors for the Google Knowledge Panel and edit them if needed in the same options.json file.

stardog.js - Stardog JavaScript Framework for node.js and the browser

  •    Javascript

Universal Javascript fetch wrapper for communicating with the Stardog HTTP server. This framework wraps all the functionality of a client for the Stardog DBMS, and provides access to a full set of functions such as executing SPARQL queries, administrative tasks on Stardog, and the use of the Reasoning API.

query-knowledge-base-with-domain-specific-documents - Extract entities and relations from a word document that contains information in tables and text to build a knowledge graph

  •    Jupyter

This code pattern is in continuation of the composite pattern - build knowledge-base with domain-specific documents. We saw how we can extract entities and relations from a word document that contains information in tables and text to build a knowledge graph. The problem lies in finding the context of the entities in the text string used for search, resolve the ambiguity of the text, formulating the query accordingly and provide the relevant query results fetched from the domain-specific Knowledge base. For instance, In a new york times article there is a mention of former US President Barack Obama as just 'OBAMA', how to make the algorithm understand it’s referring to the US president and not any other person.

schema-dts - JSON-LD TypeScript types for Schema.org vocabulary

  •    TypeScript

JSON-LD TypeScript types for Schema.org vocabulary. schema-dts provides TypeScript definitions for Schema.org vocabulary in JSON-LD format. The typings are exposed as complete sets of discriminated type unions, allowing for easy completions and stricter validation.

proNet-core - A general-purpose network embedding framework: pair-wise representations optimization Network

  •    C++

In the near future, we will redesign the framework making some solid APIs for fast development on different network embedding techniques. This shell script will help obtain the representations of the Youtube links in Youtube-links dataset.

weaviate - The Decentralised Knowledge Graph

  •    Go

Weaviate is a knowledge graph which meshes all your data and makes it available as one seamless source for contextualized research, reporting, and re-use. Our aim is to transform static (big-)data into a natural language queryable knowledge base which you can access directly or over a peer-to-peer network. Weaviate comes with a variety of features and is based on specific design principles.