Systems and methods for multi-dimensional ranking of experts

A system for generating a multi-dimensional ranking of a plurality of experts, the system comprising:

• at least one data storage operable to store evaluation data associated with the plurality of experts;
• and
• at least one processor in electronic communication with the at least one data storage, the at least one processor being operable for:
• receiving one or more evaluation datasets;
• applying one or more of a disambiguation model and a normalization model to at least some of the one or more evaluation datasets, wherein the disambiguation and normalization models are trained machine learning models;
• for each given evaluation dataset, associating the evaluation dataset with one or more of:
• (i) at least one evaluation data category, (ii) at least one taxonomy category, and (iii) at least one expert of the plurality of experts;
• subsequently, receiving one or more search filter criteria for ranking at least a subset of the plurality of experts;
• generating a ranking of the at least subset of the plurality of experts, wherein the ranking is based on the search filter criteria, and is further generated based on the associations determined for each evaluation data set;
• and
• displaying, on a user interface, at least a portion of the plurality of rankings as the multi-dimensional ranking of the plurality of experts.

A non-transitory computer-readable medium storing executable programmable instructions, which when executed by at least one processor, are configurable to cause the at least one processor to perform the method comprising:

• receiving one or more evaluation datasets;
• applying one or more of a disambiguation model and a normalization model to at least some of the one or more evaluation datasets, wherein the disambiguation and normalization models are trained machine learning models;
• for each given evaluation dataset, associating the evaluation dataset with one or more of:
• (i) at least one evaluation data category, (ii) at least one taxonomy category, and (iii) at least one expert of the plurality of experts;
• subsequently, receiving one or more search filter criteria for ranking at least a subset of the plurality of experts;
• generating a ranking of the at least subset of the plurality of experts, wherein the ranking is based on the search filter criteria, and is further generated based on the associations determined for each evaluation data set;
• and
• displaying, on a user interface, at least a portion of the plurality of rankings as the multi-dimensional ranking of the plurality of experts.