METHODS AND SYSTEM FOR SEMANTIC SEARCH IN LARGE DATABASES

A computer-implemented method of performing a semantic search in a source document database containing documents that are identified by a unique document identifier, including: reading a text component of a text-containing query; generating a set of query features from the text component of the query using a predefined feature extraction model; generating a set of training features based on the plurality of query features; training a trainable classifier with the training features and a set of document features obtained from at least a portion of the source documents using a predefined feature extraction model; selecting a number of source documents for classification according to a predefined selection scheme; obtaining features of the selected documents; classifying the selected source documents into different classes of relevance by using features of the selected documents, where at least one value of relevance is associated with each selected document; ranking the classified documents in an ordered list based on their at least one associated value of relevance; and storing the ordered list of the identifiers of the ranked documents in a computer-readable memory.