19. A system for implementing entity relation extraction comprising: memory and one or more processors; an initial seed and/or model input module, stored in the memory and executable on at least one of the one or more processors, configured to receive a relationship seed and an initial model describing an entity relationship; a model learning module, stored in the memory and executable on at least one of the one or more processors, configured to learn a new model describing an additional entity relationship based on input from the initial seed and/or model input module or based on a pattern from a pattern selection module; a relation tuple extraction module, stored in the memory and executable on at least one of the one or more processors, configured to extract a relation tuple from a data corpus by applying the new model; a pattern generation module, stored in the memory and executable on at least one of the one or more processors, configured to generate one or more patterns based on the extracted relation tuples; and the pattern selection module, stored in the memory and executable on at least one of the one or more processors, configured to assign a weight to the one or more patterns generated by the pattern generation module and select at least one of the one or more patterns based on the assigned weight to be iteratively input into the model learning module.
20. The system of claim 19 , wherein the model learning module, the relation tuple extraction module, and the pattern selection module are configured to use a discriminative MLN model, the system further comprising: a relationship clustering module, stored in the memory and executable on at least one of the one or more processors, configured to cluster the extracted relation tuples to connect same-type relation tuples; and an output module, stored in the memory and executable on at least one of the one or more processors, configured to output the clustered relation tuples.