Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may include determining a relevance probability distribution of responses and scores as an explanatory diagnostic. A distribution curve may be determined based on a probabilistic graphical network and a result may be audited relative to the distribution curve to determine noise measurements. The distribution curve may be determined based on a distribution of posterior predictions of entities to score ranking entity bias and noisiness of ranking entity feedback.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may include determining a relevance probability distribution of responses and scores as an explanatory diagnostic. A distribution curve may be determined based on a probabilistic graphical network and a result may be audited relative to the distribution curve to determine noise measurements. The distribution curve may be determined based on a distribution of posterior predictions of entities to score ranking entity bias and noisiness of ranking entity feedback.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may include determining a relevance probability distribution of responses and scores as an explanatory diagnostic. A distribution curve may be determined based on a probabilistic graphical network and a result may be audited relative to the distribution curve to determine noise measurements. The distribution curve may be determined based on a distribution of posterior predictions of entities to score ranking entity bias and noisiness of ranking entity feedback.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. Natural language texts may be processed, such as into respective vectors, by a natural language processing model. An output vector of (or intermediate vector within) an example NLP model may include over 500 dimensions, and in many cases 700-800 dimensions. A process may manage and measure semantic coverage by defining geometric characteristics, such as size or a relative distance matrix, of a sematic space corresponding to an evaluation during which the natural language texts are obtained based on the vectors of the natural language texts. A system executing the process may generate a visualization of the semantic space, which may be reduced to or is a latent embedding space, by reducing the dimensionality of vectors while preserving their relative distances between the high and reduced dimensionality forms.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may determine an alignment score of each entity participating in an evaluation of a feature in a knowledge discovery process based on feedback received from the respective entities for the feature. Feedback of an entity that is mapped in the PGN may be processed to determine an alignment score of the entity for the feature, e.g., based on how the entity scored a feature. A plurality of different distributions indicative of alignment scores may be processed for display to visually indicate to a user the alignment of participating entities in their evaluations of the features.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may include determining a relevance probability distribution of responses and scores as an explanatory diagnostic. A distribution curve may be determined based on a probabilistic graphical network and a result may be audited relative to the distribution curve to determine noise measurements. The distribution curve may be determined based on a distribution of posterior predictions of entities to score ranking entity bias and noisiness of ranking entity feedback.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. In an evaluation for which features are structured (e.g., either in a structured evaluation or determined from unstructured data and provided for evaluation) a probabilistic graphical network may graph inputs of machine learning model(s) and outputs of the machine learning model(s) as graphical elements, where one or more edges or nodes, or values associated therewith, may be based on the outputs. For example, as a set of ranking entities engage an expert system during an evaluation, the expert system may determine and update a probabilistic graphical network that represents a state of the evaluation (e.g., at a point in time after one or more ranking events), or (e.g., after completion) a final state and determined scores based the inputs provided by the ranking entities.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may include determining a relevance probability distribution of responses and scores as an explanatory diagnostic. A distribution curve may be determined based on a probabilistic graphical network and a result may be audited relative to the distribution curve to determine noise measurements. The distribution curve may be determined based on a distribution of posterior predictions of entities to score ranking entity bias and noisiness of ranking entity feedback.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. In an evaluation for which features are structured (e.g., either in a structured evaluation or determined from unstructured data and provided for evaluation) a probabilistic graphical network may graph inputs of machine learning model(s) and outputs of the machine learning model(s) as graphical elements, where one or more edges or nodes, or values associated therewith, may be based on the outputs. For example, as a set of ranking entities engage an expert system during an evaluation, the expert system may determine and update a probabilistic graphical network that represents a state of the evaluation (e.g., at a point in time after one or more ranking events), or (e.g., after completion) a final state and determined scores based the inputs provided by the ranking entities.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. Natural language texts may be processed, such as into respective vectors, by a natural language processing model. An output vector of (or intermediate vector within) an example NLP model may include over 500 dimensions, and in many cases 700-800 dimensions. A process may manage and measure semantic coverage by defining geometric characteristics, such as size or a relative distance matrix, of a sematic space corresponding to an evaluation during which the natural language texts are obtained based on the vectors of the natural language texts. A system executing the process may generate a visualization of the semantic space, which may be reduced to or is a latent embedding space, by reducing the dimensionality of vectors while preserving their relative distances between the high and reduced dimensionality forms.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. A process may determine an alignment score of each entity participating in an evaluation of a feature in a knowledge discovery process based on feedback received from the respective entities for the feature. Feedback of an entity that is mapped in the PGN may be processed to determine an alignment score of the entity for the feature, e.g., based on how the entity scored a feature. A plurality of different distributions indicative of alignment scores may be processed for display to visually indicate to a user the alignment of participating entities in their evaluations of the features.

Abstract: Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. Traditional A/B testing protocols when scaled may present, at best, a computationally expensive process (and potentially infeasibly expensive process at larger scales, such as for a thousand or more option) for computing systems or existing data sets. Embodiments of a process may employ a probabilistic model to scale an A/B testing protocol for a set of options including tens, hundreds, thousands or a hundred thousand or more options. The probabilistic model may reduce, by orders of magnitude, the number of tests performed to determine a ranked order of the options based on ranked order among subsets of options selected by sampling techniques that balance explorations and optimization of a semantic space.