Methods, systems, and computer-readable media for query language interoperability in a graph database are disclosed. Data elements are inserted into a graph database using one or more of a plurality of graph database query languages. The graph database query languages comprise a first graph database query language associated with a first data model and a second graph database query language associated with a second data model. The data elements are stored in the graph database using an internal data model that differs from the first and second data models. One or more of the data elements are retrieved from the graph database based at least in part on a query. The query is expressed using a different graph database query language than the graph database query language used to insert the one or more retrieved data elements.

A method and an apparatus for processing an intelligent voice query. A voice query input is received from a user. Automatic speech recognition and natural language understanding generate structured query data. It is modified based on an input adaptation rule to obtain modified structured query data appropriate for a content providing server, which provides a query result output corresponding to the modified structured query data. Input adaptation rules may comprise rule sets based on behavior patterns of the user and/or business recommendations. The query result output can be used for natural language generation, which may have similar adaptation rules for output.

Methods and systems for analyzing data are described. In one embodiment, a method comprises a processor receiving a data analysis algorithm over a network and executing the data analysis algorithm, the data analysis algorithm analyzing data stored in a database using machine learning to identify a database organizational format, the data analysis algorithm identifying one or more locations for a set of data stored on the database based on identifying the database organizational format, the data analysis algorithm parsing the set of data to identify whether any entries in the database associated with the set of data includes a particular value, and the data analysis algorithm communicating over the network at least a first number of entries in the database that include the particular value and a second number of entries in the database that do not include the particular value.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform using a plugin system in a user interface to identify each ingredient in an ingredient list of a recipe published on a webpage shown on the user interface; identifying query strings from content on the webpage associated with one or more ingredients of the recipe; identifying one or more respective recipe products and a respective quantity for each of the one or more ingredients; locating a respective catalog product in an online catalog for each of the one or more respective recipe products; automatically generating a list of catalog products; automatically generating a link comprising the list of catalog products; automatically redirecting the user interface to an online retail website; and automatically adding the list of catalog products to an electronic shopping cart. Other embodiments are disclosed.

A method and apparatus for formulating a query by a digital assistant is provided herein. During operation a digital assistant will receive a query from a user. The query will have a type of device mentioned within the query. In response, the digital assistant will listen for any nearby device to announce itself. The query will then be modified by the digital assistant to include a device identification heard in the announcement. Results from the modified query will be provided to the user.

The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

The disclosed subject matter relates to a system and method for providing an automated assistant that retrieves information from a knowledge base in response to a user's natural language question. A user's natural language question voice is transformed into an intermediate representation. From the intermediate representation, a cypher query is generated which may be used to query the database. The query results are provided in response to the user. The transformation into the intermediate representation is database independent while the cypher query is dependent upon the database queried.

Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

An approach for a non-factoid question answering framework across tasks and domains may be provided. The approach may include training a multi-task joint learning model in a general domain. The approach may also include initializing the multi-task joint learning model in a specific target domain. The approach may include tuning the joint learning model in the target domain. The approach may include determining which task of the multiple tasks is more difficult for the multi-task joint learning model to learn. The approach may also include dynamically adjusting the weights of the multi-task joint learning model, allowing the model to concentrate on learning the more difficult learning task.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate generative relation linking for question answering over knowledge bases. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a relation linking component. The relation linking component can map relations identified in a natural language question to corresponding relations of a knowledge base using a generative model.