Patent Document ID: 20160147878
Application ID: 14550082
Patent Status: 0

Claim One:
1. Semantic search engine, that outputs a responses (R) as the result of a semantic matching process comprising in detecting the meanings of a query (Q) and comparing it with detected meanings of contents (C), formed of phrases or expressions obtained from a contents' database ( 6 ), and selecting responses (R) as being contents corresponding to the comparison having semantic matches, comprising the following steps: for the query (Q): detecting and formalizing all the meanings of the query (Q) into a global semantic representation (LSCS 1 ) that gives the full meaning of the query (Q), by transforming individual or groups of words (W 1 ) of the query (Q) into semantic representations consisting of pairs of lemma (L) plus a semantic category SC (LSC 1 ), retrieved from the lexicon and Lexical Functions assignments and rules (LSCLF) database ( 5 ), weighting semantic representations LSC 1 in the basis of their category index and their frequency (LSC 1 +FSW 1 ) generating global weighted semantic representations (LSCS 1 +FSWS 1 ) of the query Q, and for every contents (C): detecting and formalizing all the meanings of the contents (C) into a global semantic representation (LSCS 2 ) that gives the full meaning of the content (C), by transforming individual or groups of words (W 2 ) of the contents C into semantic representations consisting of pairs of lemma (L) plus a semantic category SC (LSC 2 ), retrieved from the lexicon and Lexical Functions assignments and rules (LSCLF) database ( 5 ). weighting semantic representations LSC 2 in the basis of their category index and their frequency (LSC 2 +FSW 2 ) generating global weighted semantic representations (LSCS 2 +FSWS 2 ) of the contents C, alculating a semantic matching degree in a matching process, between a global weighted semantic representation (LSCS 1 +FSWS 1 ) of the query (Q) and a global weighted semantic representation (LSCS 2 +FSWS 2 ) of the indexed contents (C), assigning a score and retrieving the contents (C) which have the best matches (score) between their global weighted semantic representation (LSCS 2 +FSWS 2 ) and the query (Q) global weighted semantic representation (LSCS 1 +FSWS 1 ) from the database ( 6 ), and allocate them to respective responses (R).