Abstract: A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.

Abstract: A method for dynamically scoring aspects of a data object includes receiving a first data object indicating and determining a first sum of a product of a first value and a first weight value plus a product of a second value and a second weight value and generating a first score based on a result of the first sum. The method also includes determining a second sum of a product of a third value and a third weight value plus a product of a fourth value and a fourth weight value and generating a second score based on a result of the second sum. The method also includes determining a first data object score for the first data object based on, at least, the first score, the second score, and a fifth value.

Abstract: A method includes identifying all possible data object combinations for at least some of a plurality of data objects and determining a total score value for each data object combination. The method also includes applying a mutation variable to a first set of data object combinations. The method also includes selecting at least two data object combinations of the first set of data object combinations after application of the mutation variable and identifying data objects that appear in each of the at least two data object combinations. The method also includes generating a second set of data object combinations using the data objects that appear in each of the at least two data object combinations and each possible combination of data objects that do not appear in at least one data object combination of the at least two data object combinations.

Abstract: A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.