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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 ...
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 ...
Natural language processing, machine comprehension 2013 [87] [88] M. Richardson et al. The Penn Treebank Project Naturally occurring text annotated for linguistic structure. Text is parsed into semantic trees. ~ 1M words Text Natural language processing, summarization 1995 [89] [90] M. Marcus et al. DEXTER Dataset
When Pinecone announced a vector database at the beginning of last year, it was building something that was specifically designed for machine learning and aimed at data scientists.
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
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
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