Distributional–relational database

A distributional–relational database, or word-vector database, is a database management system (DBMS) that uses distributional word-vector representations to enrich the semantics of structured data. As distributional word-vectors can be built automatically from large-scale corpora, this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of schema-agnostic databases (databases in which users can query the data without being aware of its schema), semantic search, schema-integration and inductive and abductive reasoning as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantag

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enA distributional–relational database, or word-vector database, is a database management system (DBMS) that uses distributional word-vector representations to enrich the semantics of structured data. As distributional word-vectors can be built automatically from large-scale corpora, this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of schema-agnostic databases (databases in which users can query the data without being aware of its schema), semantic search, schema-integration and inductive and abductive reasoning as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantag
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enA distributional–relational database, or word-vector database, is a database management system (DBMS) that uses distributional word-vector representations to enrich the semantics of structured data. As distributional word-vectors can be built automatically from large-scale corpora, this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of schema-agnostic databases (databases in which users can query the data without being aware of its schema), semantic search, schema-integration and inductive and abductive reasoning as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantage of distributional–relational models over purely logical / semantic web models is the fact that the core semantic associations can be automatically captured from corpora in contrast to the definition of manually curated ontologies and rule knowledge bases.
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Distributional–relational database
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enDistributional–relational database
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Abductive reasoning
Category:Artificial intelligence
Category:Computational linguistics
Category:Computer data
Category:Database management systems
Category:Heuristics
Category:Language modeling
Category:Natural language processing software
Data
Database designer
Database Management System
Database schema
Data model
Distributional semantics
Geometry
Heuristic
Inductive reasoning
Ontology
Schema-agnostic databases
Semantic search
Semantic space
Semantic Web
Text corpus
Word embedding
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2qwu5
Q30672411
Subject
Category:Artificial intelligence
Category:Computational linguistics
Category:Computer data
Category:Database management systems
Category:Heuristics
Category:Language modeling
Category:Natural language processing software
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Distributional–relational database?oldid=1123733453&ns=0
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Wikipage page ID
53644736
Wikipage revision ID
1123733453