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In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Graph databases portray the data as it is viewed conceptually. This is accomplished by transferring the data into nodes and its relationships into edges. A graph database is a database that is based on graph theory. It consists of a set of objects, which can be a node or an edge.
Google's Knowledge Graph is powered in part by Freebase. [5] During its existence, Freebase data was available for commercial and non-commercial use under a Creative Commons Attribution License, and an open API, RDF endpoint, and a database dump is provided for programmers.
A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. [1] As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions (e.g., "Bob is 35", or "Bob knows John").
Microsoft Academic Knowledge Graph: Multidisciplinary Provides an RDF data set about scientific publications and related entities, such as authors, institutions, journals, and fields of study. The data set is based on the Microsoft Academic Graph. [105] [106] Free University of Freiburg: MyScienceWork: Science
ArangoDB is a transactional native multi-model database supporting two major NoSQL data models (graph and document [1]) with one query language. Written in C++ and optimized for in-memory computing. In addition ArangoDB integrated RocksDB for persistent storage. ArangoDB supports Java, JavaScript, Python, PHP, NodeJS, C++ and Elixir.
The Google Knowledge Graph is a knowledge base from which Google serves relevant information in an infobox beside its search results. This allows the user to see the answer in a glance, as an instant answer. The data is generated automatically from a variety of sources, covering places, people, businesses, and more. [1] [2]
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.