Abstract: 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.

Abstract: 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.

Abstract: An insight comprising a fact about a portion of data in a dataset is detected in the dataset. A context is determined by analyzing a natural language input, the context identifying an intent and an entity referenced by the natural language input. A relevance score is computed for the insight, the relevance score comprising a measure of similarity between a vector representation of the insight and a vector representation of the context, the vector representation of the insight comprising an encoding of the insight in multidimensional numerical form, the vector representation of the context comprising an encoding of the context in multidimensional numerical form. A selected insight is presented, the selected insight selected from a set of insights including the insight, the selected insight selected based on the relevance score.

Abstract: An embodiment includes generating a first concept representation based on a first portion of a knowledge base using a first processing path of a neural network that includes a hyperbolic graph convolution layer. The embodiment also includes generating a second concept representation based on a second portion of the knowledge base using a second processing path of the neural network, the second processing path comprising a heterogenous graph convolution layer. The embodiment also includes generating a unified concept representation including concatenating the first concept representation with the second concept representation. The embodiment also includes generating a prediction score using a using a predictive matching module, where the predictive score is indicative of an extent of a match between the unified concept representation and a concept representation from a second knowledge base.

Abstract: A method is provided to implement a conversational system with artifact generation. A middleware component receives a user input and determines whether there is sufficient information in the user input and a conversation space in a context storage of the conversational system to identify user intent associated with the user input. Responsive to the middleware component determining there is not sufficient information to identify user intent, a communications handler component sends a natural language query to an external data source via a natural language query (NLQ) interface and receives a natural language response from the external data source. The middleware component updates the conversation space based on the natural language response and returns a user response based on the natural language response.

Abstract: A computer-implemented method for generating an ontology-driven conversational interface includes generating an ontology from a description of a domain schema of a Data Analysis (DA) model, in which the DA model is a defined in terms of quantifiable, qualifying or categorical entities and their relationships as described by the domain schema. Conversational artifacts of a conversation space including a conversational pattern framework are generated by extracting DA-related intents, entities, and a dialog from the generated ontology for the conversational interface. A dialog logic table maps DA-related patterns to intents, extracted quantifiable, qualifying or categorical attributes to entities, and the dialog to user-prompts for one or more parameters in an identified DA pattern. The conversation space is integrated with at least one of an external data source or an analytics platform that stores and processes data.

Abstract: 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.

Abstract: A method is provided to implement a conversational system with artifact generation. A middleware component receives a user input and determines whether there is sufficient information in the user input and a conversation space in a context storage of the conversational system to identify user intent associated with the user input. Responsive to the middleware component determining there is not sufficient information to identify user intent, a communications handler component sends a natural language query to an external data source via a natural language query (NLQ) interface and receives a natural language response from the external data source. The middleware component updates the conversation space based on the natural language response and returns a user response based on the natural language response.

Abstract: 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.

Abstract: Techniques for distributed data placement are provided. Query workload information corresponding to a domain is determined by a data orchestrator, and the query workload information is modeled as a hypergraph, where the hypergraph includes a set of vertices and a set of hyperedges, where each vertex in the set of vertices corresponds to a concept in an ontology associated with the domain. Mappings are generated between concepts and a plurality of data nodes based on the hypergraph and based further on predefined capability of each of the plurality of data nodes. A distributed knowledge base is established based on the generated mappings.

Abstract: Techniques for query routing are provided. An ontological query is received by a query orchestrator. One or more query blocks, each indicating one or more operations and one or more quantifiers representing data flow between query blocks, are generated based on the ontological query. At least one data node is identified for each of the one or more query blocks, based on the one or more quantifiers and the one or more operations. One or more of the identified data nodes are selected based on predefined cost criteria, and one or more subqueries are transmitted to the selected one or more data nodes.