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
- Label
- 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
- SameAs
- 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
- WasDerivedFrom
- Distributional–relational database?oldid=1123733453&ns=0
- WikiPageLength
- 4056
- Wikipage page ID
- 53644736
- Wikipage revision ID
- 1123733453