1. A method for information gap filling, comprising the steps of: collecting respective amounts of details concerning members of a set of persons in a social network, wherein additional details are desired regarding the members of the set of persons; identifying online participants in a gap-filling game; defining for the online participants respective sets of candidates, the sets of candidates being subsets of the set of persons, wherein defining comprises computing a candidate score for each member of the set of persons, the candidate score being computed as an increasing function of a relationship strength between the member of the set of persons and the online participants, respectively and a decreasing function of the collected amount of details of the member of the set of persons; presenting the sets of candidates to other online participants, respectively; receiving information items regarding members of the presented sets of candidates from the other online participants, respectively, the information items comprising relationships between the candidates and other entities in the social network; and evaluating the online participants by identifying respective validated information items that are contributed by the online participants, the validated information items being identified with a predefined number of corresponding information items that are contributed by the other online participants; and computing a participant score, wherein the participant score increases according to a number of the validated information items, and for each of the validated information items, the participant score decreases according to a total number of occurrences of the corresponding information items.
1. A method for information gap filling, comprising the steps of: collecting respective amounts of details concerning members of a set of persons in a social network, wherein additional details are desired regarding the members of the set of persons; identifying online participants in a gap-filling game; defining for the online participants respective sets of candidates, the sets of candidates being subsets of the set of persons, wherein defining comprises computing a candidate score for each member of the set of persons, the candidate score being computed as an increasing function of a relationship strength between the member of the set of persons and the online participants, respectively and a decreasing function of the collected amount of details of the member of the set of persons; presenting the sets of candidates to other online participants, respectively; receiving information items regarding members of the presented sets of candidates from the other online participants, respectively, the information items comprising relationships between the candidates and other entities in the social network; and evaluating the online participants by identifying respective validated information items that are contributed by the online participants, the validated information items being identified with a predefined number of corresponding information items that are contributed by the other online participants; and computing a participant score, wherein the participant score increases according to a number of the validated information items, and for each of the validated information items, the participant score decreases according to a total number of occurrences of the corresponding information items. 5. The method according to claim 1 , wherein computing a participant score of one of the online participants comprises computing a function S=S val +(1−S rare ), which is accumulated for all the information items contributed by the one online participant, wherein S val is a first function, that determines a number of the information items that were contributed by the one online participant and which were validated by more than three other online participants; and S rare is a second function that retrieves a total number of times that one of the information items was mentioned by the online participants.