Abstract: A device may train a KGE model based on an initial knowledge graph, and may generate an initial embedding matrix based on training the KGE model. The device may receive a new KG representing new information, and may convert the new KG to new KG triples. The device may generate corruption data based on the new KG triples, and may generate embeddings based on the new embedding matrix and the corruption data. The device may process the embeddings, with the KGE model, to generate scores and a loss, and may regularize the embeddings and the scores/loss for seen and unseen concepts. The device may calculate a regularized loss based on regularizing the embeddings, the scores, and the loss, and may calculate an incremental learning loss based on the loss and the regularized loss. The device may train the KGE model based on the scores and the incremental learning loss.

Abstract: This application relates generally to intelligent and explainable link prediction in knowledge graph systems that automatically incorporate user feedback. In one aspect, this application discloses an iterative process for predicting a link set as a group of links in a knowledge graph in an embedding space by expanding the knowledge graph with predicted and validated single links in each iteration such that a final set of links are predicted with each one being added to the set depending on previously added predicted links. In another aspect, this application also discloses automatically extracting rules from user feedback of link predictions and generating a user feedback knowledge graph from the extracted rules, which in combination with an original knowledge graph are used for the generation of the link predictions.

Abstract: This application relates generally to intelligent and explainable link prediction in knowledge graph systems that automatically incorporate user feedback. In one aspect, this application discloses an iterative process for predicting a link set as a group of links in a knowledge graph in an embedding space by expanding the knowledge graph with predicted and validated single links in each iteration such that a final set of links are predicted with each one being added to the set depending on previously added predicted links. In another aspect, this application also discloses automatically extracting rules from user feedback of link predictions and generating a user feedback knowledge graph from the extracted rules, which in combination with an original knowledge graph are used for the generation of the link predictions.

Abstract: The present disclosure describes methods and systems for generating an approximated embedding of an out-of-knowledge-graph entity based on a knowledge graph. The method includes: receiving a target entity, a dataset associated with the target entity, and an embeddings space of a knowledge graph comprising a set of structured data, wherein the target entity is out of the knowledge graph and the embeddings space includes a set of vectors representing the set of structured data in the embeddings space; selecting a set of elements from the knowledge graph, each element being related to the target entity according to the dataset associated with the target entity; constructing a set of descriptory triples based on the target entity and the set of elements; obtaining an embedding matrix based on the descriptory triples and the embeddings space; and generating an approximated embedding for the target entity based on the embedding matrix.

Abstract: The present disclosure describes methods and systems to predict predicate metadata parameters in knowledge graphs via neural networks. The method includes receiving a knowledge graph based on a knowledge base including a graph-based dataset. The knowledge graph includes a predicate between two nodes and a set of predicate metadata.

Abstract: This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.

Abstract: The present disclosure describes methods and systems for generating an explanation for a prediction based on a knowledge graph. The method includes receiving a target triple and a knowledge graph including a set of structured data; converting the knowledge graph to an embeddings space and outputting a plausibility prediction for the target triple; sampling a set of neighbors of elements of the target triple in the embeddings space; obtaining a set of example triples based on the set of neighbors according to the target triple; obtaining a prototype graph based on the set of the example triples according to the target triple; generating an explanation graph based on the prototype graph, the set of example triples, and the target triple; and generating an explanation for the plausibility prediction based on the explanation graph.

Abstract: Methods and systems for estimating causal effects from knowledge graphs are provided. The method includes obtaining intervention application data and subject history data for a candidate group of subjects, and dividing, based on the intervention application data, the candidate group into a reception subgroup that received an intervention and a rejected subgroup that did not receive the intervention. The method includes for each subject within the candidate group, mapping, based on the subject history data, a covariate value set onto a knowledge graph with an embedding neural network; and for each subject in the reception subgroup or the rejection subgroup, translating the covariate value sets for the subjects within the reception subgroup or the rejection subgroup into a reception matrix or a rejection matrix with a feature neural network. The method includes comparing the reception subgroup to the rejection subgroup to determine a differential intervention effect.

Abstract: Implementations for selectively enabling override of an inference result provided by an artificial intelligence (AI) system can include receiving an input case, outputting a first inference result by processing the input case through a machine learning (ML) model, and determining that a confidence score associated with the first inference result fails to meet a threshold, and in response: providing an adapted ML model based on a set of additional cases, outputting a second inference result by processing a current case through the adapted ML model, the current case including the input case, and selectively transmitting instructions to display an override element with the first inference result in a user interface.

Abstract: Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new facts from relational learning applied to the knowledge graph. This discovery process includes converting the knowledge graph into a set of candidate embeddings spaces to apply further analysis to rank the set of candidate embeddings spaces, where the top ranked candidate embeddings spaces are further processed to identify the new facts.

Abstract: A device may receive purchase data identifying purchases by users of user devices and identifying non-temporal data associated with the users, and may preprocess the purchase data to generate sequences of multivariate and multimodal symbols. The device may process the sequences of multivariate and multimodal symbols, with a long short-term memory based encoder-decoder model, to generate sequence embeddings, and may process the non-temporal data associated with the users, with a knowledge graph, to determine knowledge graph embeddings capturing the non-temporal data. The device may process the sequence embeddings and the knowledge graph embeddings, with a knowledge graph embedding model, to generate modified sequence embeddings, and may process the modified sequence embeddings, with a clustering model, to determine clusters of the users in relation to products or services purchased by the users. The device may perform one or more actions based on the clusters of the users.

Abstract: A device may receive data associated with units of a group. A subset of the data, for a unit, may include values for features of the unit and an indication of whether the subset indicates that the unit satisfies a qualification threshold of a qualification model. The device may identify subsets of the data that indicate that a subsets of units do not satisfy the qualification threshold; alter feature values, of the subsets of the units, for a feature to generate revised subsets of the data; and process, based on the qualification model, the revised subsets of the data to obtain counterfactual explanations. The device may determine an impact score associated with the feature based on a quantity of units, of the subset of units, that satisfied the qualification threshold based on the revised subsets of the data; and determine that the impact score satisfies an impact threshold.

Abstract: A method, apparatus and program for completing a knowledge graph from a plurality of predicates and associated entities, the predicates each providing information on a relationship between a pair of entities, the method comprising the steps of: receiving an input comprising the plurality of predicates and associated entities; searching an axiom database and identifying predicates among the plurality of predicates that are equivalent to one another, or inverses of one another; identifying further predicates that are related to one another, using the axiom database and identified predicates; and embedding the identified predicates and associated entities into a vector space to complete the knowledge graph.

Abstract: A hypothesis generation system may determine sets of link types that are respectively associated with a plurality of nodes included in an incomplete knowledge graph to determine a plurality of intersection-over-union scores. The hypothesis generation system may determine, based on a plurality of vectors of an embedding space representation associated with the incomplete knowledge graph, a plurality of similarity scores and may determine, based on the plurality of intersection-over-union scores and the plurality of similarity scores, a plurality of affinity scores. The hypothesis generation system may determine, based on the plurality of affinity scores and the plurality of nodes, one or more node pairs; may generate, for a node pair, of the one or more node pairs, one or more triplet hypothesis candidate templates; and may generate, for a triplet hypothesis candidate template, of the one or more triplet hypothesis candidate templates, a plurality of triplet hypothesis candidates.

Abstract: A device may receive calibration data associated with a plurality of units and receive a set of rules; determine, based on the set of rules, a plurality of groups associated with the plurality of units; and process the calibration data based on a pretrained artificial intelligence (AI) model. The device may determine, based on processing the calibration data, a prediction that is associated with a group of the plurality of groups; and determine, based on the set of rules, a target associated with the group based on the set of rules. The device may generate a calibration model based on the prediction and the target, and aggregate the calibration model with another calibration model that is associated with another group of the plurality of groups to form a calibrated AI model.

Abstract: In some implementations, a system may determine, based on a qualification model, a prediction output of an analysis of user information. The system may determine, based on a generator model, a plurality of counterfactual explanations associated with the prediction output and the user information. The system may cluster, according to a clustering model, the plurality of counterfactual explanations into clusters of counterfactual explanations. The system may select, based on a classification model, a counterfactual explanation from a cluster of the clusters of counterfactual explanations. The system may provide a request for feedback associated with the counterfactual explanation. The system may receive feedback data associated with the request for feedback. The system may update a data structure associated with the clustering model based on the feedback data and the counterfactual explanation to form an updated data structure. The system may perform an action associated with the updated data structure.

Abstract: This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.

Abstract: The present disclosure describes methods and systems to predict predicate metadata parameters in knowledge graphs via neural networks. The method includes receiving a knowledge graph based on a knowledge base including a graph-based dataset. The knowledge graph includes a predicate between two nodes and a set of predicate metadata.

Abstract: Systems and methods enable the discovery of new relationships between diseases and genes by prioritizing the selection of gene targets for a disease using an embedding space generated from a knowledge graph by mapping datasets collected from various data sources using a graph schema, modeling disease and gene associations with link weightings, analyzing the data with several machine learning models, and scoring predictions.

Abstract: Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new facts from relational learning applied to the knowledge graph. This discovery process includes converting the knowledge graph into a set of candidate embeddings spaces to apply further analysis to rank the set of candidate embeddings spaces, where the top ranked candidate embeddings spaces are further processed to identify the new facts.