International Business Machines Corporation

Elahe Khorasani

Vadim Sheinin

Hangu Yeo

Octavian Popescu

Ngoc Phuoc An Vo

Branimir Konstantinov Boguraev

Petros Zerfos

Siddharth A Patwardhan

Tin Kam Ho

Irene Lizeth Manotas Gutiérrez

A set of values can be selected from a plurality of fields of a table in a database. At least one adverb or adjective can be selected for the set of values. Join paths for values in the set of values can be determined. A structured query language pattern can be determined based, at least in part, on at least one value in the set of values, the at least one adverb or adjective for the set of values, and the join paths for the set of values. The structured query language pattern can be stored to first training data configured, at least in part, for use in machine learning to train a text-to-SQL model, the text-to-SQL model comprising a first artificial neural network and configured to convert first natural language text to a first structured query language query.

Generating training data for a machine learning model that performs text-to-SQL

A method of authoring a conversation service for a chatbot and a database includes receiving, from a user, a selection of a database, and connecting an authoring service of the chatbot to a table in the database; outputting, from the authoring service to the user, a question requesting a description of a subject matter of the table; receiving the description of the subject matter of the table; outputting to the user a question requesting an identification of a key column of the table that contains values that represent the subject matter of the table; receiving the identification of the key column of the table; and translating, by a natural language query service, the description of the subject matter of the table and the key column of the table into the conversation service, wherein the conversation service includes SQL statements suitable for querying the database table.

Authoring a conversation service module from relational data

A method, system and apparatus of processing queries, including inputting a query as query data, generating paraphrases from the query data, and normalizing the generated paraphrases according to predefined annotations of a schema.

Accuracy of QA systems by normalizing logical queries

An embodiment for identifying and replacing logically neutral phrases in natural language queries may include receiving a natural language query. The embodiment may also identify one or more logically neutral or non-logically neutral anchors in the natural language query. The embodiment may also identify boundaries containing one or more logically neutral phrases. The embodiment may further include detecting semantic and logical relations between verbal phrases and functional language between and adjacent to the one or more logically neutral and non-logically neutral anchors to reintroduce non-logically neutral language back into a non-logically neutral portion of the natural language query. The embodiment may also include generating a modified natural language query by automatically removing the boundaries and optionally replacing the one or more logically neutral phrases in the natural language query. The embodiment may further include providing the modified natural language query to a query processing system for further processing.

Identifying and replacing logically neutral phrases in natural language queries for query processing

A computer-implemented method, a computer system and a computer program product create rules for a rule-based natural language interface for databases (NLIDB). The method may include receiving a natural language query from a user. The method may also include generating a first explanation for the natural language query using a deep learning model and a second explanation for the natural language query using the rule-based NLIDB and validating whether the first and second explanations correctly represent the natural language query. The method may further include identifying the database value in the first explanation in response to the first explanation correctly representing the natural language query and the second explanation not correctly representing the natural language query. Lastly, the method may include creating a rule in a table for the rule-based natural language interface for databases that associates the database value with the original word of the natural language query.

Refining understanding of natural language database queries

Methods, systems and computer readable media are provided for automatically creating a semantic model of a relational database for processing natural language queries. A computing device automatically extracts relational database metadata. The computing device prompts a user to enter textual labels for columns of the extracted metadata. The computing device automatically generates a schema annotation file based upon the relational database metadata and the textual labels for the columns. A natural language query is processed for the relational database using the schema annotation file.

Automatic creation of schema annotation files for converting natural language queries to structured query language

An embodiment of the invention provides a method for including receiving a natural language query from a user with an interface, and generating multiple dependency parses of the natural language query with a parser device connected to the interface. The generating of the multiple dependency parses includes dividing the natural language query into multiple components, and creating a single dependency parse by connecting each component of the components with at least one other component of the components. A processor connected to the parser device applies rules to all of the multiple dependency parses to identify entities and relations in the natural language query.

Robust and readily domain-adaptable natural language interface to databases

An embodiment of the invention provides a method wherein a natural language query is received from a user with an interface. An ontological representation of data in a database is received with an input port, including names of concepts and names of concept properties. Template rules are received with the input port, the templates rules being language dependent and ontology independent, the template rules including widely used constructs of a language. Rules are automatically generated with a rule generation engine with the ontological representation of the data in the database and the template rules to identify entities and relations in the natural language query. Entities and relations are identified with a processor, the entities and relations being identified in the natural language query with the rules. The structured data language query is generated with a query generation engine from the entities and relations.

Natural language interface to databases

In accordance with one or more implementations of the present principles, digital medical images may be efficiently transmitted between industry protocol compliant stations. Proxies may be employed to intercept messages and data transmitted between the stations and to enable transmission of data between the stations in compressed form. Further, the proxies may be incorporated into existing systems without altering integrated components to permit efficient and seamless implementation of one or more compression schemes.

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Yorktown Heights, NY, United States of America

Elahe Khorasani