International Business Machines Corporation

Thanh Lam Hoang

Bei Chen

Gabriele Picco

Mathieu Sinn

Eric P Bouillet

Rahul Nair

Vanessa Lopez Garcia

Ngoc Minh Tran

Johann-Michael Thiebaut

Tiep Mai

Beat Buesser

Pascal Pompey

Randall Lee Cogill

Lam Nguyen

Dzung Tien Phan

Marco Laumanns

John Sheehan

Marco Luca Sbodio

Natalia Mulligan

Alessandra Pascale

Yufang Hou

William Karol Lynch

Inge Lise Vejsbjerg

Hong Min

Jing James Xu

Theodoros Salonidis

Jun Song Wang

Ji Hui Yang

Craig Reginald Tomlyn

Long H Vu

Charles Arthur Jochim

Gregory Bramble

Bradley Eck

Martin Wistuba

Vasileios Vasileiadis

Tim Nonner

Albert Akhriev

Lin Ju

Voranouth Supadulya

Jacint Szabo

John Dillon Eversman

Karol W Lynch

Tuan Dinh

Ramon Fernandez Astudillo

Jishuo Yang

Young-Suk Lee

Denisa Claudia Moga

The present inventive concept provides for a method for automated decision modelling from text including obtaining a text corpus including a policy. Terms and syntax are identified within the text corpus related to the policy. Sentence similarities and co-references based on the terms and syntax are identified. Discourse and sentence level semantic parsing is performed based on the terms and the sentence similarities and the co-references using machine learning. A decision model template is generated based on the discourse and semantic parsing, and the decision model template is transformed into an automated decision model.

Automated decision modelling from text

A method, computer program product and system are provided for feature engineering and synthetic data generation. A processor retrieves a plurality of data tables, where the plurality of data tables are heterogeneous in format and content. A processor trains a variational auto-encoder (VAE) model on the plurality of data tables. A processor receives an input data table. A processor generates a synthetic data table based on the input data table and the trained VAE model.

Unsupervised learning from public tabular datasets

Embodiments are provided for providing predictive computing and data analytics for project management in a computing system by a processor. A lifecycle of each of a plurality of objects may be monitored based on data received from a plurality of data sources. Predictive analytics for project management of each of the plurality of objects may be provide based on monitoring the lifecycle of each of the plurality of objects.

Predictive computing and data analytics for project management

Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically updated with one or more changes to a codebase.

Automatic container specification file creation and update for a codebase

Software that performs the following operations: (i) receiving a set of graph predictions corresponding to an input text, where graph predictions of the set of graph predictions are generated by different respective machine learning models; (ii) blending the graph predictions of the set of graph predictions to generate a plurality of candidate blended graphs, where nodes and edges of the candidate blended graphs have respective selection metric values, generated using a selection metric function, that meet a minimum threshold; and (iii) selecting as an output blended graph a candidate blended graph of the plurality of candidate blended graphs having a highest total combination of selection metric values among the plurality of candidate blended graphs.

Blending graph predictions

Embodiments are provided for unsupervised learning of domain specific knowledge graph from textual data and language generation from knowledge graph via reinforcement learning in a computing system by a processor. Unstructured data is automatically parsed into one or more knowledge graphs based on the unstructured data and a list of candidate relations using a first machine learning model. Text data is generated from the one or more knowledge graphs using a second machine learning model.

Providing a semantic encoding and language neural network

Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.

Reasonable language model learning for text generation from a knowledge graph

A computer-implemented method for automatically generating a semantic vector representation of a relation between a specific set of entities in natural language text is provided. The method may include, in response to receiving a text segment comprising a set of entities, automatically parsing the text segment into an abstract meaning representation (AMR) graph comprising nodes representing the set of entities. The method may further include extracting a number of minimum Steiner trees from the AMR graph, and wherein each Steiner tree comprises a minimum amount of edges between the nodes corresponding to a first entity and at least one second entity. The method may further include using a trained graph neural network (GNN) to determine vector embeddings for the minimum Steiner trees. The method may further include aggregating the vector embeddings to generate the semantic vector representation of the relation between the specific set of entities.

Generating semantic vector representation of natural language data

Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.

Automatic container specification file generation for a codebase

Embodiments for automatic data preprocessing for a machine learning operation by a processor. For each data instance in a set of data instances, a sequence of actions may be automatically learned in a reinforcement learning environment to be applied for preprocessing each data instance separately. Each of the data instances may be preprocessed for use by one or more machine learning models according to the learned sequence of actions.

Automatic data preprocessing

Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

Automatic feature extraction from a relational database

Embodiments for automatic visualization and explanation of feature learning output for predictive modeling in a computing environment by a processor. A degree of importance score may be assigned to one or more features from a relational database according to the machine learning model. A visualization graph of one or more join paths and the one or more features with the degree of importance score to predict a target variable may be generated.

Automatic visualization and explanation of feature learning output from a relational database for predictive modelling

Embodiments for ensemble policy generation for prediction systems by a processor. Policies are generated and/or derived for a set of ensemble models to predict a plurality of target variables for streaming data such that the plurality of policies enables dynamic adjustment of the prediction system. One or more of the policies are updated according to one or more error states of the set of ensemble models.

Ensemble model policy generation for prediction systems

In an approach to unsupervised feature learning for relational data, a computer trains one or more entity aware autoencoders on one or more tables in a relational database, where each of the one or more entity aware autoencoders corresponds to one of the one or more tables in the relational database, and where each of the one or more entity aware autoencoders are comprised of an encoder and a decoder. A computer transforms each of the one or more tables in the relational database with the encoder of the corresponding trained entity aware autoencoder. A computer joins a first transformed table of the one or more tables in the relational database with each remaining one or more transformed tables in the relational database to form one or more joined tables. A computer aggregates the one or more joined tables. A computer outputs one or more feature representations.

Unsupervised feature learning for relational data

Embodiments for intelligent automated feature engineering for relational data in a computing environment by a processor. Indices may be automatically selected and built from one or more columns of one or more tables in a relational database using one or more automated feature engineering models that include a set of queries. One or more features may be determined using a set of queries of an automated feature engineering models to execute for a scoring operation.

Automatic feature engineering during online scoring phase

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Kildare, Ireland

Thanh Lam Hoang