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An interface schema is created as part of the logical index container that provides the API hooks used to return search results with additional features integrated into Azure Search. Azure Search provides two different indexing engines: Microsofts own proprietary natural language processing technology or Apache Lucene analyzers. [3]
A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. [2] Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail ...
There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features: [14] Flexible relations among knowledge in topical domains: A knowledge graph (i) defines abstract classes and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph ...
The vector representation of the entities and relations can be used for different machine learning applications. In representation learning , knowledge graph embedding ( KGE ), also called knowledge representation learning ( KRL ), or multi-relation learning , [ 1 ] is a machine learning task of learning a low-dimensional representation of a ...
semantic data integration, and; taxonomies/classification. Given a question, semantic technologies can directly search topics, concepts, associations that span a vast number of sources. Semantic technologies provide an abstraction layer above existing IT technologies that enables bridging and interconnection of data, content, and processes.
Semantic queries work on named graphs, linked data or triples. This enables the query to process the actual relationships between information and infer the answers from the network of data. This is in contrast to semantic search, which uses semantics (meaning of language constructs) in unstructured text to produce a
Automatic vectorization, in parallel computing, is a special case of automatic parallelization, where a computer program is converted from a scalar implementation, which processes a single pair of operands at a time, to a vector implementation, which processes one operation on multiple pairs of operands at once.