Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program parses a knowledge graph comprising a plurality of nodes and a plurality of edges to identify metadata associated with the plurality of nodes and the plurality of edges. Each edge in the plurality of edges connects a pair of nodes in the plurality of nodes. Each node in the plurality of nodes represents an entity. Each edge in the plurality of edges connecting a particular pair of nodes in the plurality of nodes represents a relationship between two entities represented by the particular pair of nodes. The program further determines a set of token definitions. The program also generates a set of syntax rules based on the identified metadata and the set of token definitions.

Abstract: A computer-implemented method includes detecting attributes and values in rules contained in a rules set. Definitions of the attributes are determined from a data model associated with the rules set. Multiple different data entries having fields corresponding to the attributes are generated by populating the fields with data according to the values detected in the rules and the definitions of the attributes determined from the data model. A labeled dataset is formed using the data entries and logic contained in the rules. At least a portion of the labeled dataset is used to train a machine learning.

Abstract: Training data for a speech-to-text service can be generated according to a variety of techniques. For example, synthetic speech audio recordings for training a speech-to-text service can be generated in an automated system via linguistic expression templates that are input to a text-to-speech service. Pre-generation characteristics and post-generation adjustments can be made. The resulting adjusted synthetic speech audio recordings can then be used for training and validation. A large number of recordings can easily be generated for development, leading to a more robust service. Domain-specific vocabulary can be supported, resulting in a trained speech-to-text service that functions well within the targeted domain.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program parses a knowledge graph comprising a plurality of nodes and a plurality of edges to identify metadata associated with the plurality of nodes and the plurality of edges. Each edge in the plurality of edges connects a pair of nodes in the plurality of nodes. Each node in the plurality of nodes represents an entity. Each edge in the plurality of edges connecting a particular pair of nodes in the plurality of nodes represents a relationship between two entities represented by the particular pair of nodes. The program further determines a set of token definitions. The program also generates a set of syntax rules based on the identified metadata and the set of token definitions.

Abstract: A service descriptor file is retrieved from a remote database by a server, where the service descriptor file describes a service corresponding to a service-specific chatbot requested by a user. The received service descriptor is parsed to obtain a capability and relevant service data associated with the service. The service-specific chatbot is generated with at least one object that is based on the obtained capability and relevant service data, where the at least one object includes intents, expressions, and skills that correspond to the service. The generated service-specific chatbot is provided to the user through at least one channel to enable an interaction with the service-specific chatbot with a graphical user interface.

Abstract: A computer implemented system and method of generating chatbots. The system uses a machine learning model to identify elements on the webpage. The system associates intents with the elements, and associates expressions and skills related to the intent. The system generates a chatbot according to the intents, expressions and skills.

Abstract: A rule is received at a client application from an input text control. The received rule includes a sequence of characters and is parsed into a plurality of tokens. For each of the tokens, a plurality of behaviors associated with that token are configured to generate a behavior configuration. Each of the generated behavior configurations can be modified by an authorized user. A user interface (UI) control is generated based on the behavior configurations.

Abstract: Linguistic expressions for training a chatbot can be generated in an automated system via linguistic expression templates that are associated with intents. The pre-categorized linguistic expressions can then be used for training and validation. Chatbot development can thus be improved by having a large number of expressions for development, leading to a more robust chatbot. In practice, the process can iterate with modifications to the templates until a suitable benchmark is met. The technique can be applied across human languages to generate chatbots conversant in any number of languages and is applicable to a variety of domains.

Abstract: Linguistic expressions for training a chatbot can be generated in an automated system via linguistic expression templates that are associated with intents. The pre-categorized linguistic expressions can then be used for training and validation. Chatbot development can thus be improved by having a large number of expressions for development, leading to a more robust chatbot. In practice, the process can iterate with modifications to the templates until a suitable benchmark is met. The technique can be applied across human languages to generate chatbots conversant in any number of languages and is applicable to a variety of domains.

Abstract: A service descriptor file is retrieved from a remote database by a server, where the service descriptor file describes a service corresponding to a service-specific chatbot requested by a user. The received service descriptor is parsed to obtain a capability and relevant service data associated with the service. The service-specific chatbot is generated with at least one object that is based on the obtained capability and relevant service data, where the at least one object includes intents, expressions, and skills that correspond to the service. The generated service-specific chatbot is provided to the user through at least one channel to enable an interaction with the service-specific chatbot with a graphical user interface.

Abstract: A rule is received at a client application from an input text control. The received rule includes a sequence of characters and is parsed into a plurality of tokens. For each of the tokens, a plurality of behaviors associated with that token are configured to generate a behavior configuration. Each of the generated behavior configurations can be modified by an authorized user. A user interface (UI) control is generated based on the behavior configurations.

Abstract: A business object model, which reflects data that is used during a given business transaction, is utilized to generate interfaces. This business object model facilitates commercial transactions by providing consistent interfaces that are suitable for use across industries, across businesses, and across different departments within a business during a business transaction. In some operations, software creates, updates, or otherwise processes information related to a business document message monitoring view, a customs arrangement, and/or a freight list business object.

Abstract: A business object model, which reflects data that is used during a given business transaction, is utilized to generate interfaces. This business object model facilitates commercial transactions by providing consistent interfaces that are suitable for use across industries, across businesses, and across different departments within a business during a business transaction. In some operations, software creates, updates, or otherwise processes information related to a business document message monitoring view, a customs arrangement, and/or a freight list business object.