Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: An information processing system, a computer readable storage medium, and a computer-implemented method, collect tables from a corpus of documents, convert the collected tables to flattened table format and organized to be searchable by schema-less queries. A method collects tables, extracts feature values from collected table data and collected table meta-data for each collected table. A table classifier classifies each collected table as being a type of table. Based on the classifying, the collected table is converted to a flattened table including table values that are the table data and the table meta-data of the collected table. Dependencies of the data values are mapped. The flattened table and mapped dependencies are stored in a triple store searchable by schema-less queries. The table classifier learns and improves its accuracy and reliability. Dependency information is maintained among a plurality of database tables. The dependency information can be updated at variable update frequency.

Abstract: An information processing system, a computer readable storage medium, and a computer-implemented method, collect tables from a corpus of documents, convert the collected tables to flattened table format and organized to be searchable by schema-less queries. A method collects tables, extracts feature values from collected table data and collected table meta-data for each collected table. A table classifier classifies each collected table as being a type of table. Based on the classifying, the collected table is converted to a flattened table including table values that are the table data and the table meta-data of the collected table. Dependencies of the data values are mapped. The flattened table and mapped dependencies are stored in a triple store searchable by schema-less queries. The table classifier learns and improves its accuracy and reliability. Dependency information is maintained among a plurality of database tables. The dependency information can be updated at variable update frequency.

Abstract: An information processing system, a computer readable storage medium, and a computer-implemented method, collect tables from a corpus of documents, convert the collected tables to flattened table format and organized to be searchable by schema-less queries. A method collects tables, extracts feature values from collected table data and collected table meta-data for each collected table. A table classifier classifies each collected table as being a type of table. Based on the classifying, the collected table is converted to a flattened table including table values that are the table data and the table meta-data of the collected table. Dependencies of the data values are mapped. The flattened table and mapped dependencies are stored in a triple store searchable by schema-less queries. The table classifier learns and improves its accuracy and reliability. Dependency information is maintained among a plurality of database tables. The dependency information can be updated at variable update frequency.

Abstract: A semantic embedding model using geometrical set-centric approach to capture both ABox and TBox representational models is disclosed. The model transforms a semantic-rich knowledge graph into a set of overlapping, disjoint, and/or subsumed n-dimensional spheres that captures and represents semantics embedded in the knowledge graph.

Abstract: An information processing system, a computer readable storage medium, and a computer-implemented method, collect tables from a corpus of documents, convert the collected tables to flattened table format and organized to be searchable by schema-less queries. A method collects tables, extracts feature values from collected table data and collected table meta-data for each collected table. A table classifier classifies each collected table as being a type of table. Based on the classifying, the collected table is converted to a flattened table including table values that are the table data and the table meta-data of the collected table. Dependencies of the data values are mapped. The flattened table and mapped dependencies are stored in a triple store searchable by schema-less queries. The table classifier learns and improves its accuracy and reliability. Dependency information is maintained among a plurality of database tables. The dependency information can be updated at variable update frequency.

Abstract: An information processing system, a computer readable storage medium, and a computer-implemented method, collect tables from a corpus of documents, convert the collected tables to flattened table format and organized to be searchable by schema-less queries. A method collects tables, extracts feature values from collected table data and collected table meta-data for each collected table. A table classifier classifies each collected table as being a type of table. Based on the classifying, the collected table is converted to a flattened table including table values that are the table data and the table meta-data of the collected table. Dependencies of the data values are mapped. The flattened table and mapped dependencies are stored in a triple store searchable by schema-less queries. The table classifier learns and improves its accuracy and reliability. Dependency information is maintained among a plurality of database tables. The dependency information can be updated at variable update frequency.

Abstract: An information processing system, a computer readable storage medium, and a computer-implemented method, collect tables from a corpus of documents, convert the collected tables to flattened table format and organized to be searchable by schema-less queries. A method collects tables, extracts feature values from collected table data and collected table meta-data for each collected table. A table classifier classifies each collected table as being a type of table. Based on the classifying, the collected table is converted to a flattened table including table values that are the table data and the table meta-data of the collected table. Dependencies of the data values are mapped. The flattened table and mapped dependencies are stored in a triple store searchable by schema-less queries. The table classifier learns and improves its accuracy and reliability. Dependency information is maintained among a plurality of database tables. The dependency information can be updated at variable update frequency.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: A semantic embedding model using geometrical set-centric approach to capture both ABox and TBox representational models is disclosed. The model transforms a semantic-rich knowledge graph into a set of overlapping, disjoint, and/or subsumed n-dimensional spheres that captures and represents semantics embedded in the knowledge graph.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.