Abstract: A method for utilizing shared memory spaces for data lifecycle management in a conversational AI system includes receiving one or more storage requests directing conversational data to multiple nodes of a conversational data object and storing the conversational data that is directed to each different one of the multiple nodes within a different one of multiple discrete memory spaces. Each of the multiple discrete memory spaces is governed by a scope policy defining a lifetime for data stored within the memory space. The method further provides for erasing data of each node of the multiple different nodes of the conversational data object according to the lifetime defined by the scope policy governing the memory space storing the node.

Abstract: Technologies are described herein for denormalizing data instances. Schemas for data instances are embedded with annotations indicating how the denormalization is to be performed. Based on the annotations, one or more sub per object indexes (“sub POIs”) can be generated for each data instance and stored. The sub POIs can include a target sub POI containing data from the data instance, and at least one source sub POI containing data from another data instance, if the data instance depends on the other data instance. Data instance updates can be performed by identifying sub POIs that are related to the updated data instance in storage, and updating the related sub POIs according to the update to the data instance. The sub POIs can be sent to an indexing engine to generate an index for a search engine to facilitate searches on the data instances.

Abstract: Techniques are provided for building a dialog-state specific contextual language understanding system using subsumption logic. Information establishing conversational rules identifying the conversational dialog is received to present in respective dialog states. Each rule has a Boolean trigger expression of predicates for testing the conversational state together with logical connectives to identify when the rule is applicable. Subsumption logic is used to arrange the rules into a directed acyclic graph (DAG) where more specific rules are preferred to more general rules. During a conversation, the DAG is used to filter the triggered rules to only the most specific triggered rules from which a rule to run is selected. This structure makes it easier to build conversational systems because rules can be added or removed without having to change or reason over other rules. The rules also act as a constraint to help machine learned selection systems converge with less data.

Abstract: A computer-executed conversational system utilizing adaptive dialogs to provide dynamic modification of a conversation structure. The adaptive dialogs facilitate an adaptive and dynamic way to model conversations. A conversational rule may be associated with a trigger received at an adaptive dialog. The conversational rule may populate a plan with steps to provide the conversational structure of the conversational system. Modifying triggers may be received that allow conversational rules to amend the plan including adding steps, removing steps, or modifying steps from the plan. The adaptive dialogs and/or components thereof may be provided as constructible elements in a platform for building a bot such as a developer Software Development Kit (SDK) or the like. In turn, sophisticated conversational modeling may be conveniently facilitated to provide features such as a dialog dispatcher, the ability to handle interruptions, and to build a pluggable, extensible conversational system.

Abstract: Conversational event modeling for determining how to process an input in a dialog stack of a computer-executed conversational system. Receipt of an input at an active dialog in a dialog stack may result in generation of an event corresponding to the input. The event may be propagated through the dialog stack. Dialogs in the dialog stack may generate a confidence value in relation to processing the input, and selection of the dialog in the dialog stack for use in processing the input may be based at least in part on the confidence values. In turn, the conversational system may facilitate improved handling of ambiguous or unrelated inputs at dialogs by propagating the event associated with such an input through the dialog stack. The event creation and processing functions may be provided as parameters for dialogs in a modular dialog creation system.

Abstract: Conversational event modeling for determining how to process an input in a dialog stack of a computer-executed conversational system. Receipt of an input at an active dialog in a dialog stack may result in generation of an event corresponding to the input. The event may be propagated through the dialog stack. Dialogs in the dialog stack may generate a confidence value in relation to processing the input, and selection of the dialog in the dialog stack for use in processing the input may be based at least in part on the confidence values. In turn, the conversational system may facilitate improved handling of ambiguous or unrelated inputs at dialogs by propagating the event associated with such an input through the dialog stack. The event creation and processing functions may be provided as parameters for dialogs in a modular dialog creation system.

Abstract: A method for utilizing shared memory spaces for data lifecycle management in a conversational AI system includes receiving one or more storage requests directing conversational data to multiple nodes of a conversational data object and storing the conversational data that is directed to each different one of the multiple nodes within a different one of multiple discrete memory spaces. Each of the multiple discrete memory spaces is governed by a scope policy defining a lifetime for data stored within the memory space. The method further provides for erasing data of each node of the multiple different nodes of the conversational data object according to the lifetime defined by the scope policy governing the memory space storing the node.

Abstract: A computer-executed conversational system utilizing adaptive dialogs to provide dynamic modification of a conversation structure. The adaptive dialogs facilitate an adaptive and dynamic way to model conversations. A conversational rule may be associated with a trigger received at an adaptive dialog. The conversational rule may populate a plan with steps to provide the conversational structure of the conversational system. Modifying triggers may be received that allow conversational rules to amend the plan including adding steps, removing steps, or modifying steps from the plan. The adaptive dialogs and/or components thereof may be provided as constructible elements in a platform for building a bot such as a developer Software Development Kit (SDK) or the like. In turn, sophisticated conversational modeling may be conveniently facilitated to provide features such as a dialog dispatcher, the ability to handle interruptions, and to build a pluggable, extensible conversational system.

Abstract: Techniques are provided for building a dialog-state specific contextual language understanding system using subsumption logic. Information establishing conversational rules identifying the conversational dialog is received to present in respective dialog states. Each rule has a Boolean trigger expression of predicates for testing the conversational state together with logical connectives to identify when the rule is applicable. Subsumption logic is used to arrange the rules into a directed acyclic graph (DAG) where more specific rules are preferred to more general rules. During a conversation, the DAG is used to filter the triggered rules to only the most specific triggered rules from which a rule to run is selected. This structure makes it easier to build conversational systems because rules can be added or removed without having to change or reason over other rules. The rules also act as a constraint to help machine learned selection systems converge with less data.

Abstract: Technologies are described herein for denormalizing data instances. Schemas for data instances are embedded with annotations indicating how the denormalization is to be performed. Based on the annotations, one or more sub per object indexes (“sub POIs”) can be generated for each data instance and stored. The sub POIs can include a target sub POI containing data from the data instance, and at least one source sub POI containing data from another data instance, if the data instance depends on the other data instance. Data instance updates can be performed by identifying sub POIs that are related to the updated data instance in storage, and updating the related sub POIs according to the update to the data instance. The sub POIs can be sent to an indexing engine to generate an index for a search engine to facilitate searches on the data instances.

Abstract: Technologies are described herein for denormalizing data instances. Schemas for data instances are embedded with annotations indicating how the denormalization is to be performed. Based on the annotations, one or more sub per object indexes (“sub POIs”) can be generated for each data instance and stored. The sub POIs can include a target sub POI containing data from the data instance, and at least one source sub POI containing data from another data instance, if the data instance depends on the other data instance. Data instance updates can be performed by identifying sub POIs that are related to the updated data instance in storage, and updating the related sub POIs according to the update to the data instance. The sub POIs can be sent to an indexing engine to generate an index for a search engine to facilitate searches on the data instances.

Abstract: Technologies are described herein for denormalizing data instances. Schemas for data instances are embedded with annotations indicating how the denormalization is to be performed. Based on the annotations, one or more sub per object indexes (“sub POIs”) can be generated for each data instance and stored. The sub POIs can include a target sub POI containing data from the data instance, and at least one source sub POI containing data from another data instance, if the data instance depends on the other data instance. Data instance updates can be performed by identifying sub POIs that are related to the updated data instance in storage, and updating the related sub POIs according to the update to the data instance. The sub POIs can be sent to an indexing engine to generate an index for a search engine to facilitate searches on the data instances.

Abstract: Methods and computer-readable media for indexing an encrypted file by impersonating a user is provided. A set of keys may be associated with a particular encrypted file. Once these keys are identified, the users who own these keys may then be identified by consulting an association of keys to users, which may be updated immediately upon the addition of new keys. If one of the users is currently logged on, the logon information associated with that user may be used to access the content of the encrypted file. The encrypted file may then be indexed based on the accessed content. To allow more than one user to use the same index, security identifiers may be assigned to index records associated with encrypted files to protect content of encrypted files from unauthorized users.

Abstract: Methods and computer-readable media are provided for performing a federated search using a library description file to locate multiple data sources. For a federated search, a library description can be used to describe a set of data sources searched, and may further be used to describe how search results should be presented to a user. The format of such a library description file can include multiple elements, some of which provide information on how to display the library and others that define which data sources are included in the library. The library description file can be created according to library description template.

Abstract: Methods and computer-readable media are provided for performing a federated search using a library description file to locate multiple data sources. For a federated search, a library description can be used to describe a set of data sources searched, and may further be used to describe how search results should be presented to a user. The format of such a library description file can include multiple elements, some of which provide information on how to display the library and others that define which data sources are included in the library. The library description file can be created according to library description template.

Abstract: Methods and computer-readable media for indexing an encrypted file by impersonating a user is provided. A set of keys may be associated with a particular encrypted file. Once these keys are identified, the users who own these keys may then be identified by consulting an association of keys to users, which may be updated immediately upon the addition of new keys. If one of the users is currently logged on, the logon information associated with that user may be used to access the content of the encrypted file. The encrypted file may then be indexed based on the accessed content. To allow more than one user to use the same index, security identifiers may be assigned to index records associated with encrypted files to protect content of encrypted files from unauthorized users.

Abstract: Methods and computer-readable media are provided for performing a federated search using a library description file to locate multiple data sources. For a federated search, a library description can be used to describe a set of data sources searched, and may further be used to describe how search results should be presented to a user. The format of such a library description file can include multiple elements, some of which provide information on how to display the library and others that define which data sources are included in the library. The library description file can be created according to library description template.

Abstract: An authoring tool (or process) to facilitate the performance of an annotation function and an indexing function. The annotation function may generate informational annotations and word annotations to a database design schema (e.g., an entity-relationship diagram or “ERD”). The indexing function may analyze the words of the annotations by classifying the words in accordance with a concordance and dictionary, and assign a normalized weight to each word of each of the annotations based on the classification(s) of the word(s) of the annotation. A query translator (or query translation process) to (i) accept a natural language query from a user interface process, (ii) convert the natural language query to a formal command query (e.g., an SQL query) using the indexed annotations generated by the authoring tool and the database design schema, and (iii) present the formal command query to a database management process for interrogating the relational database.

Abstract: A relational database management system having an XML storage implementation to reduce overhead associated with consuming data from multiple data providers, where each having proprietary database schemas. The XML storage solution allows data from any arbitrary relational database schema to be loaded, rearranged and retrieved. The system is adapted to receive and managing data from external content providers by exposing a predetermined provider content file formats These formats use XPath as a schema reference, as a definition of legal combinations, and as a pattern to match other XPaths.

Abstract: An authoring tool (or process) to facilitate the performance of an annotation function and an indexing function. The annotation function may generate informational annotations and word annotations to a database design schema (e.g., an entity-relationship diagram or “ERD”). The indexing function may analyze the words of the annotations by classifying the words in accordance with a concordance and dictionary, and assign a normalized weight to each word of each of the annotations based on the classification(s) of the word(s) of the annotation. A query translator (or query translation process) to (i) accept a natural language query from a user interface process, (ii) convert the natural language query to a formal command query (e.g., an SQL query) using the indexed annotations generated by the authoring tool and the database design schema, and (iii) present the formal command query to a database management process for interrogating the relational database.