International Business Machines Corporation

Doordash, Inc.

Pavan Kapanipathi Bangalore

Qingzi Vera Liao

Biplav Srivastava

Salim E Roukos

Achille Belly Fokoue-Nkoutche

Dinesh Garg

Kartik Talamadupula

Kshitij P Fadnis

Ibrahim Abdelaziz

Nandana Sampath Mihindukulasooriya

Alexander Gray

Alfio Massimiliano Gliozzo

L Venkata Subramaniam

Maria Chang

Gaetano Rossiello

Chulaka Gunasekara

Saswati Dana

Bassem Makni

Dinesh Khandelwal

G P Shrivatsa Bhargav

Avinash Balakrishnan

Nicholas Mattei

Veronika Thost

Srinivas Ravishankar

Siva Sankalp Patel

A computer-implemented method according to one embodiment includes identifying a natural language query; translating the natural language query into an intermediate representation; converting the intermediate representation into one or more query triples; and performing relation linking between each of the one or more query triples and a plurality of knowledge base triples.

Implementing relation linking for knowledge bases

Methods, systems, and computer program products for zero-shot entity linking based on symbolic information are provided herein. A computer-implemented method includes obtaining a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph; training a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph; and mapping an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

Zero-shot entity linking based on symbolic information

A dialogue complexity assessment method, system, and computer program product including calculating a complexity utilizing domain-dependent terms and domain-independent terms of a dialogue, where the dialogue includes dialogue data from contact centers of service providers.

Assessing complexity of dialogs to streamline handling of service requests

One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Knowledge base question answering

A method for assigning weights to a knowledge graph includes extracting information from a knowledge graph. The information including entities extracted from nodes of the knowledge graph and relations extracted from edges of the knowledge graph. A shortest path generator receives the extracted entities and relations, and potential assigned weights from a heuristic data repository. Weights for the edges of the knowledge graph are determined. The weights are assigned to the edges of the knowledge graph.

Interpretable knowledge contextualization by re-weighting knowledge graphs

One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Infusing knowledge into natural language processing tasks using graph structures

A dialogue complexity assessment method, system, and computer program product for introducing the notion of dialogue complexity to understand and compare dialogues in a repository, calculating the dialogue complexity, use the dialogue complexity to understand customer interactions in a variety of domains using public and proprietary data, and demonstrate the dialogue complexity usage to improve a service management operation.

Growing community of inventors

Yorktown Heights, NY, United States of America

Pavan Kapanipathi Bangalore