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Despite the graph databases' advantages and recent popularity over [citation needed] relational databases, it is recommended the graph model itself should not be the sole reason to replace an existing relational database. A graph database may become relevant if there is an evidence for performance improvement by orders of magnitude and lower ...
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 ...
TerminusDB is an open source knowledge graph and document store. It is used to build versioned data products. It is a native revision control database that is architecturally similar to Git. It is listed on DB-Engines. TerminusDB provides a document API for building via the JSON exchange format. It implements both GraphQL and a datalog variant ...
In-memory database and application server (data grid) TerminusDB: TerminusDB (formerly DataChemist) 2019 JavaScript, Python, Prolog, Rust, JSON-LD: Open Source (Apache 2.0) Open source in-memory graph database designed for knowledge graph representation [13] TimesTen: now Oracle Corporation: 1997 Java, JDBC, ODBC, SQL, PLSQL, C Proprietary
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.
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
A knowledge graph is a knowledge base that uses a graph-structured data model. Common applications are for gathering lightly-structured associations between topic-specific knowledge in a range of disciplines, which each have their own more detailed data shapes and schemas .
It is possible to use the task of link prediction to infer a new connection between an already existing drug and a disease by using a biomedical knowledge graph built leveraging the availability of massive literature and biomedical databases. [14] Knowledge graph embedding can also be used in the domain of social politics. [4]