1. A method, comprising: executing instructions on a computing platform, the instructions comprising: receiving search query inputs to search a database of data items, the database having categories and an associated data dictionary of terms to add to the search query inputs for searching the database; generating a set of token pairs based on the search query inputs, each token pair comprising a keyword from at least one search query input and a token, the token being a synonym of the keyword; validating the set of token pairs by: calculating a bi-directional divergence measure for the each of the token pairs, the bi-directional divergence being a predictability indicator for the token pairs for use in a search query; and calculation of the bi-directional divergence measure further comprising: performing a search using a keyword of a token pair as a search criteria; determining a first distribution (P) of search results for the keyword, the first distribution over a plurality of categories; performing a search using a token of the token pair as a search criteria; determining a second distribution (Q) of search results for the token, the second distribution over the plurality of categories; calculating the bi-directional divergence measure as an Information Radius (IRad) value as: 
 IRad=0.5[ D ( P ∥avg( P,Q ))+ D ( Q ∥avg( P,Q ))], wherein avg(P,Q) is an average of the first distribution and the second distribution, wherein D is Kullback-Liebler divergence calculated as: 
 D ( P∥Q )=Σ P ( i )log( P ( i )/ Q ( i )), wherein an index i=1, 2, . . . N, and N is the number of categories in the plurality of categories; comparing the bi-directional divergence measure for each of the token pairs to a first limit, wherein a token pair having a bi-directional divergence measure below the first limit is a valid token pair for the data dictionary; entering validated token pairs into the data dictionary to form an updated data dictionary; and applying the updated data dictionary to search queries.
1. A method, comprising: executing instructions on a computing platform, the instructions comprising: receiving search query inputs to search a database of data items, the database having categories and an associated data dictionary of terms to add to the search query inputs for searching the database; generating a set of token pairs based on the search query inputs, each token pair comprising a keyword from at least one search query input and a token, the token being a synonym of the keyword; validating the set of token pairs by: calculating a bi-directional divergence measure for the each of the token pairs, the bi-directional divergence being a predictability indicator for the token pairs for use in a search query; and calculation of the bi-directional divergence measure further comprising: performing a search using a keyword of a token pair as a search criteria; determining a first distribution (P) of search results for the keyword, the first distribution over a plurality of categories; performing a search using a token of the token pair as a search criteria; determining a second distribution (Q) of search results for the token, the second distribution over the plurality of categories; calculating the bi-directional divergence measure as an Information Radius (IRad) value as: 
 IRad=0.5[ D ( P ∥avg( P,Q ))+ D ( Q ∥avg( P,Q ))], wherein avg(P,Q) is an average of the first distribution and the second distribution, wherein D is Kullback-Liebler divergence calculated as: 
 D ( P∥Q )=Σ P ( i )log( P ( i )/ Q ( i )), wherein an index i=1, 2, . . . N, and N is the number of categories in the plurality of categories; comparing the bi-directional divergence measure for each of the token pairs to a first limit, wherein a token pair having a bi-directional divergence measure below the first limit is a valid token pair for the data dictionary; entering validated token pairs into the data dictionary to form an updated data dictionary; and applying the updated data dictionary to search queries. 2. The method of claim 1 , wherein generating a set of token pairs results in a plurality of token pairs having a same keyword.