1. A semantic search system using a semantic ranking scheme comprising: an ontology analyzer using a computer to analyze ontology data related to a search target to determine a weight value of each property wherein the ontology data comprises DBLP and IMDB data sets; a semantic path extractor extracting all the semantic paths between resources and query keywords and determining a weight value of each extracted semantic path according to a semantic path weight value determination scheme by using the weight value of each property; a relevant resource searcher traversing an instance graph of ontology based on a semantic path having a pre-set length and weight value of more than an expectation level to search resources that have a semantic relationship with the query keywords and are declared as a type presented in the query; and a semantic relevance ranker selecting a top-k results having the highest rank from among the candidate results extracted by the relevant resource researcher by using a relevance scoring function; wherein the relevance scoring function reflects the number of meaningful semantic paths between resources and keywords, the coverage of keywords and the distinguishability of keywords related to the resources; wherein the relevance scoring function is represented by: Rank ⁡ ( a , K ) = 1 - [ ∑ 1 ≤ i ≤  K  ⁢ ( D ⁡ ( k i ) · ( 1 - NR ⁡ ( a , k i ) ) ) p ∑ 1 ≤ i ≤  K  ⁢ D ⁡ ( k i ) p ] 1 p wherein Rank(a,K) is a relevance scoring function with respect to a set of query keywords K of a resource ‘a’, wherein NR(a,k) is a normalized relation degree of resource ‘a’ with respect to a keyword k i which has a value greater than zero and less than or equal to 1, and wherein D(k i ) is subject discriminative power of the keyword k i and p is a tuning parameter for controlling AND-semantics; wherein the NR(a,k i ) is calculated by: NR ⁡ ( a , k i ) = R ⁡ ( a , k i ) max a m ∈ A ⁢ R ⁡ ( a m , k i ) ⁢ ⁢ for ⁢ ⁢ k i ∈ K wherein R(a,k i ) is a relation degree of resource ‘a’ with respect to the keyword k i max R(α m ,k i ) is a maximum value of a relation degree, a m is a resource which makes R(α m ,k i ) a maximum value and ‘A’ is a set of resources; and R(α,k i ) is calculated by equation shown below: NR ⁡ ( a , k i ) = R ⁡ ( a , k i ) max a m ∈ A ⁢ R ⁡ ( a m , k i ) ⁢ ⁢ for ⁢ ⁢ k i ∈ K wherein R(A,k i ) is a relation degree of resource ‘a’ with respect to the keyword k i max R(a m ,k i ), is a maximum value of a relation degree, a m is a resource which makes R(a m , k i ) a maximum value and ‘A’ a set of resources; and R(a,k i ) is calculated by: R ⁡ ( a , k i ) = ∑ ip ∈ IP ⁡ ( a , k i ) ⁢ ⁢ W ⁡ ( ip ) , for ⁢ ⁢ k i ∈ K wherein IP(a,k i ) is a set of all the semantic path instances existing between the resource ‘a’ and the keyword k i , ‘ip’ is an element of the set ‘IP(a, ki)’ and W(ip) is a weight value of a semantic path instance ‘ip’; wherein D(k i ) is calculated by: D ⁡ ( k i ) = irf ⁡ ( k i ) max k m ∈ K ⁢ irf ⁡ ( k m ) where k m is an element of the K and km makes irf(k m ) a maximum value where irf(k i ) is calculated by: irf ⁡ ( k i ) = log ⁢  DV   DV k i  where DV comprises a set of data values on an ontology instance graph, and DV ki comprises a set of data values including a keyword k i .