Abstract: Methods, systems, and computer program products for low-resource entity resolution with transfer learning are provided herein. A computer-implemented method includes processing input data via a first entity resolution model, wherein the input data comprise labeled input data and unlabeled input data; identifying one or more portions of the unlabeled input data to be used in training a neural network entity resolution model, wherein said identifying comprises applying one or more active learning algorithms to the first entity resolution model; training, using (i) the one or more portions of the unlabeled input data and (ii) one or more deep learning techniques, the neural network entity resolution model; and performing one or more entity resolution tasks by applying the trained neural network entity resolution model to one or more datasets.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate inter-operator backpropagation in AutoML frameworks are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a selection component that selects a subset of deep learning and non-deep learning operators. The computer executable components further comprise a training component which trains the subset of deep learning and non-deep learning operators, wherein deep learning operators in the subset of deep learning and non-deep learning operators are trained using backpropagation across at least two deep learning operators of the subset of deep learning and non-deep learning operators.

Abstract: A system, computer program product, and method are provided for entity linking in a logical neural network (LNN). A set of features are generated for one or more entity-mention pairs in an annotated dataset. The generated set of features is evaluated against an entity linking LNN rule template having one or more logically connected rules and corresponding connective weights organized in a tree structure. An artificial neural network is leveraged along with a corresponding machine learning algorithm to learn the connective weights. The connective weights associated with the logically connected rules are selectively updated and a learned model is generated with learned thresholds and the learned weights for the logically connected rules.

Abstract: A method, system, and computer program product for learning entity resolution rules for determining whether entities are matching. The method may include receiving historical pairs of entities. The method may also include determining a set of rules for determining whether a pair of entities are matching, where the set of rules comprises a plurality of conditions. The method may also include developing, using a deep neural network, an entity resolution model based on the historical pairs of entities. The method may also include receiving a new pair of entities. The method may also include applying the entity resolution model to the new pair of entities. The method may also include determining whether one or more rules from the set of rules are satisfied for the new pair of entities. The method may also include categorizing the new pair of entities as matching or not matching.

Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.

Abstract: Methods, systems, and computer program products for low-resource entity resolution with transfer learning are provided herein. A computer-implemented method includes processing input data via a first entity resolution model, wherein the input data comprise labeled input data and unlabeled input data; identifying one or more portions of the unlabeled input data to be used in training a neural network entity resolution model, wherein said identifying comprises applying one or more active learning algorithms to the first entity resolution model; training, using (i) the one or more portions of the unlabeled input data and (ii) one or more deep learning techniques, the neural network entity resolution model; and performing one or more entity resolution tasks by applying the trained neural network entity resolution model to one or more datasets.