International Business Machines Corporation

Globalfoundries Inc.

Nternational Business Machines Corporation

Mustafa Canim

Bishwaranjan Bhattacharjee

Mohammad Sadoghi Hamedani

Kenneth Andrew Ross

Robert G Farrell

Arun Kwangil Iyengar

John A Gunnels

Yuan-Chi Chang

Ryan A Musa

Alfio Massimiliano Gliozzo

Haggai Roitman

Achille Belly Fokoue-Nkoutche

George Andrei Mihaila

Michael Robert Glass

Guy Feigenblat

Vijay Anand Saraswat

Cristina Cornelio

Mariano Rodriguez Muro

Roee Shraga

Adam J Storm

Sarthak Dash

Bassem Makni

Akiko Suzuki

Sharon Mary Trewin

Christian Alexander Lang

Jaydeep Sen

Douglas Ronald Burdick

Saneem Ahmed Chemmengath

Samarth Bharadwaj

Joshua F Payne

Sonia Mariette Embid Droz

Nicolas Rodolfo Fauceglia

Ruben Rodriguez Torrado

Yuan Chi Chang

Vishwajeet Kumar

Ioannis Katsis

Giorgio De Paola

Cristina Ibanez-Llano

Ehab Abdelhamid Mohammed Abdelhamid

Fabian Nagel

Hidayet Aksu

A system for ranking machine learning base models for transfer learning purposes is described. The system receives image data in the form an image or an image set and extracts image tags from the images. The image tags are expanded into a set of associated terms using a word embedding database and model. The associated terms are used to query a knowledge database for parent or categorical terms used to rank various matching machine learning base models that may be improved or trained by the image data.

Ranking image sources for transfer learning

A question answering bot that digests non-relational data tables is provided. A processor receives a question regarding a non-relational data table. A processor extracts at least one feature of the question using a natural language processing (NLP) model. A processor extracts at least one similar feature of the non-relational data table to the extracted at least one feature of the question. A processor determines at least one relevant cell in the non-relational data table based on the at least one feature of the question and the at least one similar feature of the non-relational data table. A processor provides an answer to the question, where the answer is based on the at least one relevant cell.

Natural language question answering using non-relational tables

A processor-implemented method securely responds to a query for information from a data graph. One or more processors create an embedding for encrypted sensitive information in vertices in a data graph; and bucketize embedded encrypted sensitive information on an embedding graph, where bucketizing the embedded encrypted sensitive information clusters vertices from the graph database that have shared data graph features. The processor(s) receive a query of the data graph from a requester, where the query is for information related to the shared data graph features. The processor(s) retrieve a bucket from the embedding graph that contains the information related to the shared data graph features; and extract encrypted sensitive information from the retrieved bucket.

Secure query processing on graph stores

A computer answers a question using a data table. The computer receives a user question and a target table containing a target cell corresponding to a target answer for the user question, with the target cell corresponding to a target column and a target row. The computer generates, a first classifier to provide column correlation values reflecting the probability that a given column is the target column. The computer generates a second classifier that provides row correlation values reflecting the probability that a given row is the target row. The computer applies the first classifier to the target table to determine a column correlation value for each column. The computer applies the second classifier to the target table to determine a row correlation value for each row. The computer suggests, as the target cell, a cell having elevated column and row correlation values relative to other target table cells.

Answering questions with artificial intelligence using tabular data

A method, apparatus and computer program product for establishing a logical connection between an indirect utterance and a transaction is described. An indirect utterance is received from a user as an input to a conversational system. The indirect utterance is parsed to a first logical form. A first set of predicates and terms is mapped from the first logical form to a first subgraph in a knowledge graph. A second set of predicates and terms is mapped from a second logical form belonging to a transaction to a second subgraph of the knowledge graph. A best path in the knowledge graph between the first subgraph and the second subgraph is searched for while transforming the first logical form using the node and edge labels along the best path to generate an intermediate logical form. A system action is performed for a transaction if a graph structure of the intermediate logical form matches the graph structure of the logical form of the transaction above a threshold.

Establishing a logical connection between an indirect utterance and a transaction

A system and a computer-implemented method for ranking tabular data entities by likelihood of comprising answers for (natural language) queries, based on multimodal descriptions of the tabular data entities, comprising separate representations, which represent different aspects of the tabular data entities. The ranking is based on joint representations, generated from the query representation and separate representations of the tabular data entities' aspects, using gated multimodal units. The computer-implemented method may be used for applications such as web searches, data aggregation, and research tasks.

Method and system for table retrieval using multimodal deep co-learning with helper query-dependent and query-independent relevance labels

A system comprises a memory that stores computer-executable components; and a processor, operably coupled to the memory, that executes the computer-executable components. The system includes a receiving component that receives a corpus of data; a relation extraction component that generates noisy knowledge graphs from the corpus; and a training component that acquires global representations of entities and relation by training from output of the relation extraction component.

Deep symbolic validation of information extraction systems

Multimodal table encoding, including: Receiving an electronic document that contains a table. The table includes multiple rows, multiple columns, and a schema comprising column labels or row labels. The electronic document includes a description of the table which is located externally to the table. Next, operating separate machine learning encoders to separately encode the description, schema, each of the rows, and each of the columns of the table, respectively. The schema, the rows, and the columns are encoded together with end-of-column tokens and end-of-row tokens that mark an end of each column and row, respectively. Then, applying a machine learning gating mechanism to the encoded description, encoded schema, encoded rows, and encoded columns, to produce a fused encoding of the table, wherein the fused encoding is representative of both a structure of the table and a content of the table.

Multimodal table encoding for information retrieval systems

Organizing and/or aligning fragments of text that are included in a set of physical and/or digital documents so that the arrangement of the text fragments is in a readily understandable and meaningful format for a given reader. This organization and/or alignment uses a relation model of the various text fragments to correlate a meaning between and amongst the various text fragments to ultimately determine the final alignment and/or arrangement of those text fragments.

Organizing fragments of meaningful text

Ad-hoc table retrieval, including: Representing each of a plurality of tables as a multi-field text document in which: different modalities of the table are represented as separate fields, and a concatenation of all the modalities is represented as a separate, auxiliary field. Receiving a query. Executing the query on the multi-field text documents, to retrieve a list of preliminarily-ranked candidate tables out of the plurality of tables. Calculating an intrinsic table similarity score for each of the candidate tables, based on the query and the auxiliary field. Calculating an extrinsic table similarity score for each of the candidate tables, based on a cluster hypothesis of the candidate tables. Combining: the preliminary rankings, the intrinsic table similarity scores, and the extrinsic table similarity scores, to re-rank the candidate tables.

Table indexing and retrieval using intrinsic and extrinsic table similarity measures

A policy violation detection computer-implemented method, system, and computer program product, includes extracting a policy activity from a policy, the policy activity including an actor in the policy, an object of the policy, an action of the policy, and policy scope metadata, capturing a transaction by a user including metadata of the transaction, translating the transaction by the user into an actor in the transaction, an action of the transaction, and an object of the transaction, and alerting the user of a policy violation by navigating a knowledge graph is-a hierarchy to relate the actor in the transaction to the actor in the policy, the object of the transaction to an object of the policy, and the action of the transaction to an action of the policy activity.

System, method and computer program product for detecting policy violations

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.

Automatic transformation of complex tables in documents into computer understandable structured format and managing dependencies

Automatic transformation of complex tables in documents into computer understandable structured format and providing schema-less query support data extraction

Automatic transformation of complex tables in documents into computer understandable structured format with mapped dependencies and providing schema-less query support for searching table data

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Ossining, NY, United States of America

Mustafa Canim