Abstract: Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response as the result of a semantic matching process consisting in comparing a natural language query with a plurality of contents, formed of phrases or expressions obtained from a contents' database, and selecting the response as being the contents corresponding to the comparison having a best semantic matching degree. An underlying knowledge base implements the encoding of symmetric meanings between terms. The knowledge base provides a mapping between a single term's usage and disambiguation therein. Inferences are made based on the disambiguation process providing an enhanced search engine.

Abstract: Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response as the result of a semantic matching process consisting in comparing a natural language query with a plurality of contents, formed of phrases or expressions obtained from a contents' database, and selecting the response as being the contents corresponding to the comparison having a best semantic matching degree. An underlying knowledge base implements the encoding of symmetric meanings between terms. The knowledge base provides a mapping between a single term's usage and disambiguation therein. Inferences are made based on the disambiguation process providing an enhanced search engine.

Abstract: Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response (R) as the result of a semantic matching process consisting in comparing a natural language query (Q) with a plurality of contents (C), formed of phrases or expressions obtained from a contents' database (6), and selecting the response (R) as being the contents corresponding to the comparison having a best semantic matching degree. It involves the transformation of the contents (C) and the query in individual words or groups of tokenized words (W1, W2), which are transformed in its turn into semantic representations (LSC1, LSC2) thereof, by applying the rules of Meaning Text Theory and through Lexical Functions, the said semantic representations (LSC1, LSC2) consisting each of a couple formed of a lemma (L) plus a semantic category (SC).

Abstract: Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response (R) as the result of a semantic matching process consisting in comparing a natural language query (Q) with a plurality of contents (C), formed of phrases or expressions obtained from a contents' database (6), and selecting the response (R) as being the contents corresponding to the comparison having a best semantic matching degree. It involves the transformation of the contents (C) and the query in individual words or groups of tokenized words (W1, W2), which are transformed in its turn into semantic representations (LSC1, LSC2) thereof, by applying the rules of Meaning Text Theory and through Lexical Functions, the said semantic representations (LSC1, LSC2) consisting each of a couple formed of a lemma (L) plus a semantic category (SC).

Abstract: Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response (R) as the result of a semantic matching process consisting in comparing a natural language query (Q) with a plurality of contents (C), formed of phrases or expressions obtained from a contents' database (6), and selecting the response (R) as being the contents corresponding to the comparison having a best semantic matching degree. It involves the transformation of the contents (C) and the query in individual words or groups of tokenized words (W1, W2), which are transformed in its turn into semantic representations (LSC1, LSC2) thereof, by applying the rules of Meaning Text Theory and through Lexical Functions, the said semantic representations (LSC1, LSC2) consisting each of a couple formed of a lemma (L) plus a semantic category (SC).

Abstract: Engines, systems, and methods for performing a natural language search are disclosed. The method may include receiving, via a user interface including a virtual assistant, at least one search query. The at least one search query may be converted into at least one first global semantic representation. The contents may be searched and for at least one second global semantic representation that matches the at least one first global semantic representation.