10. A method for building a knowledge base containing entailment relations, comprising the steps of: a) providing at least one input pattern (p) with N pattern slots (N>1), said input pattern (p) expressing a specific semantic relation between N entities that fill the N pattern slots of the input pattern (p) as slot fillers, b) providing at least one cluster (c) of articles, said articles of said cluster (c) relating to a common main topic; c) processing, by a computer device, said articles with respect to the input pattern (p) and identifying the identities which match the semantic type of the N pattern slots; d) if said at least one input pattern matches a portion of an article (a) of said at least one cluster (c): i) storing, by the computer device, the N slot fillers (s 1 , s 2 , . . . , s N ), which match the slots of the pattern (p), and a cluster identifier I c of the cluster (c) into a first table S, wherein the N-tuple (s 1 , s 2 , . . . , s N ) and the cluster identifier I c of the associated cluster (c) form one element of said table S; ii) for each element of table S, identifying, by the computer device, appearances of the slot fillers (s 1 , s 2 , . . . , s N ) in a plurality of articles of cluster (c) and for each appearance so identified, storing the slot fillers (s 1 , s 2 , . . . , s N ) together with the sentence in which they occur into a second table C 0 ; iii) from the sentences stored in table C 0 , extracting, by the computer device, patterns which span over the corresponding N slot fillers (s 1 , s 2 , . . . , s N ), said extracted pattern expressing a semantic relation between said N slot fillers; and iv) storing, by the computer device, said extracted patterns together with said input pattern as entailment relation into said knowledge base.
10. A method for building a knowledge base containing entailment relations, comprising the steps of: a) providing at least one input pattern (p) with N pattern slots (N>1), said input pattern (p) expressing a specific semantic relation between N entities that fill the N pattern slots of the input pattern (p) as slot fillers, b) providing at least one cluster (c) of articles, said articles of said cluster (c) relating to a common main topic; c) processing, by a computer device, said articles with respect to the input pattern (p) and identifying the identities which match the semantic type of the N pattern slots; d) if said at least one input pattern matches a portion of an article (a) of said at least one cluster (c): i) storing, by the computer device, the N slot fillers (s 1 , s 2 , . . . , s N ), which match the slots of the pattern (p), and a cluster identifier I c of the cluster (c) into a first table S, wherein the N-tuple (s 1 , s 2 , . . . , s N ) and the cluster identifier I c of the associated cluster (c) form one element of said table S; ii) for each element of table S, identifying, by the computer device, appearances of the slot fillers (s 1 , s 2 , . . . , s N ) in a plurality of articles of cluster (c) and for each appearance so identified, storing the slot fillers (s 1 , s 2 , . . . , s N ) together with the sentence in which they occur into a second table C 0 ; iii) from the sentences stored in table C 0 , extracting, by the computer device, patterns which span over the corresponding N slot fillers (s 1 , s 2 , . . . , s N ), said extracted pattern expressing a semantic relation between said N slot fillers; and iv) storing, by the computer device, said extracted patterns together with said input pattern as entailment relation into said knowledge base. 21. A computer program product stored on a non-transitory computer usable medium, comprising instructions operable to cause a programmable processor to carry out a method for building a knowledge base containing entailment relations according to claim 10 .