International Business Machines Corporation

Vasilis Efthymiou

Fatma Ozcan

Chuan Lei

Abdul Hussain Quamar

Rana Alotaibi

Venkata Vamsikrishna Meduri

Junheng Hao

Alina Vretinaris

A computing device and computer-implemented method to provide access to business intelligence data via a conversational interface is described. The method begins with receiving prior user interactions based on corresponding pairs of user queries and computing device responses, with the conversational interface. Received also is a business intelligence ontology and access to a conversational space structure for the conversational interface. The computing device executes a machine learning model based upon the prior user interactions, business intelligence ontology, and the conversational space structure to predict possible next queries. The possible next queries are sent to a user. In one example, the computing device receives a user query, which is in addition to the prior user interactions with the conversational interface. A response to the query is generated. The response to the query is sent to the user prior to sending the possible next queries, which have been predicted.

Guided exploration for conversational business intelligence

An embodiment includes extracting, responsive to an update request from a remote requesting system, technical descriptor data from a data source. The embodiment also includes forming a new graph data structure using the technical descriptor data extracted from the data source. The embodiment also includes augmenting the new graph data structure to include a concept based on a value from instance data from the data source. The embodiment also includes identifying a first pair of concepts that are connected in a pre-existing ontology that correspond with a second pair of concepts that lack a connection therebetween in the new graph structure. The embodiment also includes augmenting the new graph data structure to include a connection between the second pair of concepts. The embodiment also includes outputting the new graph data structure as part of a response to the update request from the requesting system.

Hybrid graph neural network

Computer-implemented techniques for entity disambiguation using graph neural networks (GNNs) are provided. According to an embodiment, computer implemented method can comprise receiving, by a system operatively coupled to a processor, an unstructured text snippet comprising an unknown term. The method further comprises employing, by the system, a heterogeneous GNN trained on a knowledge graph associated with a domain of the unstructured text snippet to facilitate identifying one or more similar terms included within the knowledge graph for the unknown term.

Entity disambiguation using graph neural networks

According to one or more embodiments of the present invention, a computer-implemented method for providing a query response includes receiving, by a computing device, a domain-specific knowledge graph. The method further includes generating a first property graph schema, a property graph schema includes vertices, edges, and properties of the domain-specific knowledge graph, wherein the first property graph schema is generated based on an ontology of the domain-specific knowledge graph. The method further includes generating a second property graph schema from a copy of the first property graph schema that is optimized by applying one or more types of relationships in the first property graph schema. The method further includes instantiating a property graph using the second property graph schema. The method further includes receiving a query to obtain particular data from the domain-specific knowledge graph. The method further includes responding to the query using the property graph.

Growing community of inventors

San Jose, CA, United States of America

Vasilis Efthymiou