Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process for matching a word subset to a database entity. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an identification component that outputs a word subset based on a word-based input file, and a mapping component that, based on a rules-based process employing soft matching, maps the word subset to a category comprising a value for being correlated to the word subset. The rules-based process employed by the mapping component can comprise word vector matching or fuzzy string matching.

Abstract: A modular two-stage neural architecture is used in translating a natural language question into a logic form such as a SPARQL Protocol and RDF Query Language (SPARQL) query. In a first stage, a neural machine translation (NMT)-based sequence-to-sequence (Seq2Seq) model translates a question into a sketch of the desired SPARQL query called a SPARQL silhouette. In a second stage a neural graph search module predicts the correct relations in the underlying knowledge graph.

Abstract: An embodiment includes decomposing a natural language assertion into a natural language question and answer pair that includes an initial question and an initial answer. The embodiment translates the initial question into a structured knowledge graph query and then performs an iterative process comprising iterative querying of a knowledge graph and evaluating of corresponding query responses resulting in respective confidence scores. A first iteration of the iterative process comprises querying of the knowledge graph to retrieve a first predicted answer, then determining whether a degree of similarity between the initial answer and the first predicted answer meets a threshold criterion. If not, the first predicted query is altered and used for querying the knowledge graph in a subsequent iteration of the iterative process. The embodiment also generates an assertion correctness score indicative of a degree of confidence that the assertion is factual using the respective confidence scores.

Abstract: Methods, systems, and computer program products for zero-shot entity linking based on symbolic information are provided herein. A computer-implemented method includes obtaining a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph; training a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph; and mapping an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

Abstract: Methods, systems, and computer program products for explaining cross domain model predictions are provided herein. A computer-implemented method includes providing a test data point to a domain adaptation model to obtain a prediction, wherein the domain adaptation model is trained on a set of labeled data points and a set of unlabeled data points. The method includes generating a task specific explanation for the prediction that includes one or more data points from among the sets that satisfy a threshold score for influencing the prediction. Additionally, the method includes generating a domain invariant explanation for the prediction. The domain variation explanation is generated by ranking pairs of data points based on a statistical similarity to the test data point, wherein each pair includes a data point from the set of labeled data points and a data point from the set of unlabeled data points.

Abstract: A computer-implemented method is provided. The embodiments include evaluating, by one or more processors, a specimen chart relative to a chart erratum model that has features mapped to an optimum state for a first chart type. The method also includes generating a first risk score for a first sample feature of the specimen chart. The first risk score may include a delta from the optimum state. The method also includes refactoring the specimen chart to mitigate the first risk score of the first sample feature.

Abstract: Methods, systems, and computer program products for explaining cross domain model predictions are provided herein. A computer-implemented method includes providing a test data point to a domain adaptation model to obtain a prediction, wherein the domain adaptation model is trained on a set of labeled data points and a set of unlabeled data points. The method includes generating a task specific explanation for the prediction that includes one or more data points from among the sets that satisfy a threshold score for influencing the prediction. Additionally, the method includes generating a domain invariant explanation for the prediction. The domain variation explanation is generated by ranking pairs of data points based on a statistical similarity to the test data point, wherein each pair includes a data point from the set of labeled data points and a data point from the set of unlabeled data points.

Abstract: Provided are a method and apparatus for designing and processing a rule pipeline for in silico prediction of chemical reactions. The method includes designing a rule pipeline from at least one rule for chemical conversion and processing at least one input molecule by using the designed rule pipeline to predict a chemical reaction based on a processing result of the processing.

Abstract: Provided are a method and apparatus for designing and processing a rule pipeline for in silico prediction of chemical reactions. The method includes designing a rule pipeline from at least one rule for chemical conversion and processing at least one input molecule by using the designed rule pipeline to predict a chemical reaction based on a processing result of the processing.

Abstract: Systems and methods for identifying enzymes for catalysing biochemical reactions include receiving input of reaction(s) and/or target molecule(s) along with data associated with chemical conversion, determining functional and linker region(s) in the input, scanning a transformation library for the determined functional region(s) of the reaction(s) and/or the target molecule(s) to find similar functional region(s) within the transformation library, assigning the reaction(s) and/or target molecule(s) to group(s) of the transformation library showing a high similarity to the transformation, computing a metabolite similarity score of the reaction(s) and/or target molecule(s) with respect to one or more reactions of the assigned group, and identifying enzyme(s) associated with the reaction(s) of the assigned group having a high metabolite similarity score. A transformation library is also generated.