Abstract: A normalized processing method of a named entity includes: obtaining first text data; recognizing a named entity from the first text data; determining whether a first standard named entity exists in a standard named entity database according to the named entity; determining the first standard named entity as a normalized representation of the named entity in response to determining that the first standard named entity exists in the standard named entity database; and obtaining a second standard named entity from the standard named entity database and determining an obtained second standard named entity as the normalized representation of the named entity in response to determining that the first standard named entity does not exist in the standard named entity database.

Abstract: A display assembly including a stand, a mounting assembly and a display. The mounting assembly includes a fixed base, a first pivot, a pivotable base, a second pivot, a handle, a plurality of first engagement structures and a second engagement structure. The handle includes a handheld part and a mounting part. The mounting part is connected to the handheld part and pivotally connected to the pivotable base via the second pivot. The first engagement structures are disposed at one of the fixed base and the mounting part of the handle. The second engagement structure is disposed at another one of the fixed base and the mounting part of the handle. The display is fixed on the pivotable base. The first pivot is not coaxial with the second pivot so that the second engagement structure is configured to be engaged with any one of the first engagement structures.

Abstract: An online concierge system uses a co-engagement graph to assign attribute values to items for which those attribute values are uncertain. A co-engagement graph is a graph with nodes that represent items and edges that represent co-engagement between items. The online concierge system generates a co-engagement graph for a set of items based on item engagement data and item data for the items. The set of items includes items for which the online concierge system has an attribute value for a target attribute and items for which the online concierge system does not have an attribute value for the target attribute. The online concierge system identifies a node that corresponds to an unknown item and identifies a node connected to that first node that corresponds to a known item. The online concierge system assigns the attribute value for the known item to the unknown item.

Abstract: An offline adaptation method of customizing and incorporating external knowledge domain specific knowledge sources into an existing knowledge base (KB) of a cognitive conversation system, includes obtaining an instance-concept frequency from the existing KB. All the contexts of a domain ontology in which an instance-concept is used are computed. All instance-concepts from the existing KB to an external domain-specific knowledge source are mapped. The existing KB is configured to search for semantically related terms in response to a natural language query received from a conversation system.

Abstract: A system, method, and computer readable medium perform a method of query relaxation. A query output from a conversational system is received. At least one search term in the query is identified. Instance data is output to the conversational system in response to determining that the instance data in a data store matches the at least one search term in the query. The computer device outputs the received query to an external domain-specific knowledge source in response to determining that the at least one search term does not the match instance data in the data store. The computer device receives the relaxed data matches from the external domain-specific knowledge source being semantically-related to at least one search term in the query based on a plurality of criteria associated with the query. The computing device generates a response to the query based on contextual information and structural information.

Abstract: To simplify retrieval of items from a database that at least partially satisfy a received query, an online concierge system trains a model that outputs scores for items from the database without initially retrieving items for evaluation by the model. The online concierge system pre-trains the model using natural language inputs corresponding to items from the database, with a natural language input including masked words that the model is trained to predict. Subsequently, the model is refined using multi-task training where a task is trained to predict scores for items from the received query. The online concierge system selects items for display in response to the received query based on the predicted scores.

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 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: 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: 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.

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: 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: A system, method, and computer readable medium perform a method of query relaxation. A query output from a conversational system is received. At least one search term in the query is identified. Instance data is output to the conversational system in response to determining that the instance data in a data store matches the at least one search term in the query. The computer device outputs the received query to an external domain-specific knowledge source in response to determining that the at least one search term does not the match instance data in the data store. The computer device receives the relaxed data matches from the external domain-specific knowledge source being semantically-related to at least one search term in the query based on a plurality of criteria associated with the query. The computing device generates a response to the query based on contextual information and structural information.