Abstract: Disclosed herein is a method of facilitating provisioning contexts for business situations. Accordingly, the method may include receiving at least one business dataset associated with a business organization in real time from a device, analyzing the at least one business dataset, extracting a plurality of business events from the at least one business dataset based on the analyzing of the at least one business dataset, analyzing the plurality of business events, determining a plurality of business insights from the plurality of business events using the event assessment algorithm, analyzing the plurality of business insights, determining at least one connection between at least two of the plurality of business insights, constructing a semantic graph for providing a context for each of at least one business situation associated with the business organization, and storing the semantic graph.

Abstract: A system may include: a database table, a grammar containing metadata-referencing non-terminals, and metadata mapping the metadata-referencing non-terminals into alternative terms for the table and a column therein.

Abstract: Systems and methods are described for automatically generating natural language queries and chatbot-assisted responses to the queries. A server may generate observed fact data structures from numeric-type data columns of a data set received from a client device over a network connection. From the generated observed facts, a subset of priority observed facts may be identified based on a plurality of priority factors associated with each observed fact. To generate actionable text recommendations, the server may combine one of the priority observed facts with a natural language template retrieved from a template database. The resulting populated natural language template may then be augmented with a selected call to action query selected based on a received user explanation type. The chatbot service may then cause a text recommendation to be transmitted to a user device that is responsive to the format and values contained within the augmented natural language template.

Abstract: Systems and methods are described for deriving actionable insight interfaces from received data sets using performance indicators and stored insight templates. A server may tag data columns of the data set, which may be then mapped to a plurality of performance indicator inputs to determine a plurality of performance indicators. A selected insight template may then be retrieved from a template database based on the determined performance indicators matching input requirements of the selected insight template. Each insight template stored within the template database may be stored as a data object that includes a plurality of rules and narrative text that provides a text recommendation based on the rule outputs. After the rules for the selected insight template have been executed, the narrative text and the rule outputs may be transmitted to a display device for display on an insight graphic interface.

Abstract: An example embodiment may involve a software application executable on computing devices of a remote network management platform and a computation instance associated with a managed network. The computational instance may contain a database storing data of the managed network. The software application may receive, from a client device of the managed network, a natural language query (NLQ), and retrieve Backus-Naur form (BNF) rules and a set of metadata associated with the BNF rules. The metadata may include a text-based description of a schema of the database and abbreviations associated with the BNF rules. The NLQ may be parsed using the BNF rules together with the metadata by applying the metadata during parsing to extend the BNF rules. A query object based on the parsed query may be generated, and the database searched using the query object. A result of the search may be transmitted to the client device.

Abstract: An example embodiment may involve a software application executable on a computing device of a computational instance of a remote network management platform associated with a managed network. The software application may receive, from a client device of the managed network, a natural language query, determine that a first term of the query does not match any term in a list of predetermined terms, and revise the query by (i) applying a set of linguistic rules that modify the first term to generate a replacement term, (ii) determining whether the replacement term matches at least one term in the list, and (iii) after determining that the replacement term matches at least one term in the list, replacing the first term in the query with the replacement term. The software application may then parse the revised query, search the database, and transmit a result to the client device.

Abstract: A system may include: a database table, a grammar containing metadata-referencing non-terminals, and metadata mapping the metadata-referencing non-terminals into alternative terms for the table and a column therein.

Abstract: An example embodiment may involve a software application executable on a computing device of a computational instance of a remote network management platform associated with a managed network. The computational instance may contain a database from which information associated with the managed network can be delivered to the client device. The software application may receive, from a client device of the managed network, a natural language query, parse the query using context-free grammar rules in Backus-Naur form (BNF), generate a search sentence based on the parsed query, search the database using the search sentence as a search query, and transmit, to the client device, a result of the search query. The context-free grammar rules may include an extended context-free grammar rule that defines a non-terminal symbol and comprises a Boolean NOT operator that eliminates one or more potential definitions of the non-terminal symbol from consideration when parsing the query.

Abstract: A system may include: a database table, a grammar containing metadata-referencing non-terminals, and metadata mapping the metadata-referencing non-terminals into alternative terms for the table and a column therein.

Abstract: Translating a natural language search query into a query language includes receiving a natural language query for a database, processing the natural language query to generate a modified text input, generating an entity tree based on the modified text input, including assigning one or more semantic markers to one or more words or one or more groups of words within the modified text input, wherein each semantic tag denotes a semantic class for each respective word or group or words, and converting the entity tree into the query language associated with the first database.

Abstract: A system may include: a database table, a grammar containing metadata-referencing non-terminals, and metadata mapping the metadata-referencing non-terminals into alternative terms for the table and a column therein.

Abstract: An example embodiment may involve a software application executable on computing devices of a remote network management platform and a computation instance associated with a managed network. The computational instance may contain a database storing data of the managed network. The software application may receive, from a client device of the managed network, a natural language query (NLQ), and retrieve Backus-Naur form (BNF) rules and a set of metadata associated with the BNF rules. The metadata may include a text-based description of a schema of the database and abbreviations associated with the BNF rules. The NLQ may be parsed using the BNF rules together with the metadata by applying the metadata during parsing to extend the BNF rules. A query object based on the parsed query may be generated, and the database searched using the query object. A result of the search may be transmitted to the client device.

Abstract: An example embodiment may involve a software application executable on a computing device of a computational instance of a remote network management platform associated with a managed network. The computational instance may contain a database from which information associated with the managed network can be delivered to the client device. The software application may receive, from a client device of the managed network, a natural language query, parse the query using context-free grammar rules in Backus-Naur form (BNF), generate a search sentence based on the parsed query, search the database using the search sentence as a search query, and transmit, to the client device, a result of the search query. The context-free grammar rules may include an extended context-free grammar rule that defines a non-terminal symbol and comprises a Boolean NOT operator that eliminates one or more potential definitions of the non-terminal symbol from consideration when parsing the query.

Abstract: An example embodiment may involve a software application executable on a computing device of a computational instance of a remote network management platform associated with a managed network. The software application may receive, from a client device of the managed network, a natural language query, determine that a first term of the query does not match any term in a list of predetermined terms, and revise the query by (i) applying a set of linguistic rules that modify the first term to generate a replacement term, (ii) determining whether the replacement term matches at least one term in the list, and (iii) after determining that the replacement term matches at least one term in the list, replacing the first term in the query with the replacement term. The software application may then parse the revised query, search the database, and transmit a result to the client device.

Abstract: Translating a natural language search query into a query language includes receiving a natural language query for a database, processing the natural language query to generate a modified text input, generating an entity tree based on the modified text input, including assigning one or more semantic markers to one or more words or one or more groups of words within the modified text input, wherein each semantic tag denotes a semantic class for each respective word or group or words, and converting the entity tree into the query language associated with the first database.