International Business Machines Corporation

Manasa Jammi

Diptikalyan Saha

Jaydeep Sen

Karthik Sankaranarayanan

Ashish Mittal

Priyanka Agrawal

One embodiment provides a method, including: receiving, within a conversational agent creation framework used to create conversational agents that perform negotiations on behalf of users, (i) a selection of one of a plurality of conversational agent roles (ii) negotiation constraints for the selected role, and (iii) an object for the negotiation; creating a conversational agent (i) having the selected role and (ii) programmed with the negotiation constraints; identifying at least one other conversational agent (i) having an opposing role with respect to the object and (ii) being programmed with opposing role negotiation constraints; and conducting a conversational session between the conversational agent and the at least one other conversational agent, wherein the conversational session comprises the negotiation for the object and wherein the conversational agent and the at least one other conversational agent perform the negotiation in view of the negotiation constraints and the opposing role negotiation constraints.

Multi-agent conversational agent framework

Embodiments are disclosed for correcting a natural language interface database (NLIDB) system. The techniques include receiving feedback indicating that an answer provided in response to a question for an NLIDB system is inaccurate. The techniques further include finding an ontology element for a datastore of the NLIDB system that matches to the feedback. The techniques also include selecting candidate annotations for the NLIDB system based on the ontology element and a data type of the ontology element. Additionally, the techniques include generating a question-answer (QA) pair for each of the candidate annotations. Further, the techniques include adding one of the candidate annotations to annotations for a natural language query (NLQ) engine of the NLIDB system based on a client verification of the QA pair.

Natural language interface databases

Systems and methods are provided for generating nested queries from natural language queries. In particular, system and methods are provided to implement natural language interfaces to databases (NLIDB) frameworks which are configured to apply intelligent reasoning over domain semantics to detect and generate nested queries across different domains without the need for domain specific training or utilizing domain-specific semantic templates for mapping a natural language query to a structured query.

Generating nested database queries from natural language queries

Methods, systems, and computer program products for automated generation of test cases for analyzing natural-language-interface-to-database systems are provided herein. A computer-implemented method includes identifying sources of ambiguity from input to a natural-language-interface-to-database system and a precondition corresponding to each of the identified sources of ambiguity; generating test cases which analyze capabilities of the natural-language-interface-to-database system, wherein generating the one or more test cases includes determining validity of the preconditions within the context of the capabilities of the natural-language-interface-to-database system; automatically generating an ontology-dependent structured query template based at least in part on the generated test cases; automatically generating natural language queries based at least in part on the ontology-dependent structured query template; and outputting, to at least one user, the ontology-dependent structured query template and the natural language queries.

Automated generation of test cases for analyzing natural-language-interface-to-database systems

One embodiment provides a method, including: receiving a natural language query from a user; identifying a plurality of interpretations for interpreting the natural language query, wherein the plurality of interpretations are based upon at least one ambiguity in the received natural language query; generating, for each of the plurality of interpretations, a plurality of example queries; generating, for each of the interpretations, both (i) an answer to the received natural language query and (ii) an answer to each of the generated plurality of example queries; and providing, to the user, (i) the generated answer for each interpretation of the natural language query and (ii) a plurality of question/answer pairs for each of the identified plurality of interpretations that assists in disambiguating the ambiguity, wherein each question/answer pair comprises at least one of the generated plurality of example queries and the corresponding generated answer to the example query.

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Kharagpur, India

Manasa Jammi