International Business Machines Corporation

Gabriele Picco

Thanh Lam Hoang

Vanessa Lopez Garcia

Lam Nguyen

Dzung Tien Phan

Natalia Mulligan

Marco Luca Sbodio

Yufang Hou

Inge Lise Vejsbjerg

Vasileios Vasileiadis

Ramon Fernandez Astudillo

Young-Suk Lee

Leopold Fuchs

Denisa Claudia Moga

Marcos Martínez Galindo

Automatically enriching the descriptions of an entity mentioned in a sentence corpus includes generating multiple enriched descriptions corresponding to a label of the entity. Each of the multiple enriched descriptions is ranked. The ranking is generated by a machine learning model that is configured to determine a likelihood that an enriched description correctly describes the entity. The sentence corpus are annotated by coupling each mention of the entity with one or more of the enriched descriptions. The one or more enriched descriptions are selected based on the ranking. As annotated, the sentence corpus can be output.

Automated enrichment of entity descriptions in unstructured text

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.

Growing community of inventors

Dublin, Ireland

Gabriele Picco