The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and/or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and/or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

New question and answer (QA) pairs can be automatically discovered from a corpus of data such as online chats and conversations. Newly discovered QA pairs can augment QA database, which can be used by a computer processor or device, e.g., by a chatbot, an automated machine, and/or another. Existing QA knowledge can be used to learn the structures of QA knowledge distribution in conversations, and new QA knowledge can be automatically learned through the structure of learned QA knowledge distribution in conversations. The structure of learned QA knowledge distribution can be refined by adding more semantics based on labeled data.

A system and a method for parsing a user query. The system includes a database arrangement operable to store an ontology; and a processing module communicably coupled to the database arrangement. The processing module operable to receive the user query; refine the user query to obtain a search query using an algorithm; generate a plurality of strings for the obtained search query; sort the plurality of strings in a decreasing order of length of the plurality of strings; assign a part-of-speech tag to each of the query segments of the plurality of strings based on the ontology; identify at least one of the query segments as at least one output class or at least one input class based on the assigned part-of-speech tags; and establish semantic associations between the query segments based on the ontology to obtain the parsed user query.

Provided are techniques for data analysis and rule generation for providing a recommendation. Current features are identified from data in a corpus. In response to receiving an indication that the data has changed, a new feature is identified. A feature set is created by identifying one or more related features of the current features. A feature worthiness score for the feature set is generated. In response to the feature worthiness score exceeding a threshold, the feature set is input to a model. One or more rules from the model are received, where each of the one or more rules includes the one or more related features, the new feature, and a recommendation. In response to receiving a set of values for the one or more related features and the new feature, a rule of the one or more rules is applied to provide the recommendation for that set of values.

Provided are techniques for data analysis and rule generation for providing a recommendation. Current features are identified from data in a corpus. In response to receiving an indication that the data has changed, a new feature is identified. A feature set is created by identifying one or more related features of the current features. A feature worthiness score for the feature set is generated. In response to the feature worthiness score exceeding a threshold, the feature set is input to a model. One or more rules from the model are received, where each of the one or more rules includes the one or more related features, the new feature, and a recommendation. In response to receiving a set of values for the one or more related features and the new feature, a rule of the one or more rules is applied to provide the recommendation for that set of values.

A text search method, a text search device, a server and a storage medium are provided, relating to the field of information processing technology. The method acquires a target text matrix formed by a plurality of target word vectors associated with an input text according to a target database by preconfiguring a target database including a plurality of word vectors, a plurality of to-be-matched texts and a subject graph corresponding to each of the to-be-matched texts; then uses that target text matrix to construct a target subject graph corresponding to the input text; acquires in the target database a plurality of the initially matching texts corresponding to the input text and a subject graph corresponding to each of the initially matching texts; then generates a search result of the input text according to the target subject graph and the subject graph corresponding to each of the initially matching texts.

An information processing system facilitates the extraction of a product relating to a patent or to facilitate the extraction of a patent relating to a product. An information processing system includes a database in which a term associated with a raw material recited in a claim of a patent document and a raw material that corresponds to the term are associated with each other, an acquiring unit configured to acquire patent information, and an extracting unit configured to extract a product containing a raw material that corresponds to a term recited in a claim that is identified by the patent information, with reference to the database and a correspondence between a product and a raw material contained in the product, based on whether the raw material that corresponds to the term and the raw material contained in the product match.

A method is provided for an application program interface (API) to interface with an ontology store storing a plurality of modifiable ontology models having associated dynamic definitions associated that define classes of the associated ontology model and relationships between the respective classes and that is modifiable over time. The method includes receiving from a requesting entity a request that specifies an ontology model and one or more parameters defining attributes of an instantiated ontology object, accessing the ontology store, identifying an ontology model in the ontology store that corresponds to the ontology model specified, and manipulating the identified ontology model based on its one or more parameters. The method further includes generating a semantics query for accessing the identified ontology model based on the one or more parameters specified in the request, submitting the semantics query to and receiving query results from the ontology store, and returning the query results to the requesting entity.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. A knowledge concept graph is generated for the knowledge seeker continually based on each of the received query and the received feedback by the smart-learning and knowledge retrieval system, thereby artificially learning unique characteristics of the knowledge seeker for measuring an ability of the knowledge seeker to learn and to show continued interest in an e-learning course. The knowledge concept graph is a cognitive blueprint of the knowledge seeker in a domain of knowledge at a point in time. The knowledge concept graph displays levels of granularity comprising one or more of interconnected concepts, categories of concepts, sub-categories of concepts, granular concepts, micro concepts, and macro concepts.

An enterprise ontology, an application data usage model, and/or cross-application data dependencies may be developed using artificial intelligence. Using pattern recognition and/or information extraction techniques, the artificial intelligence may analyze application source code to identify common DDL or SQL statements to formulate an ontology and/or a usage model for the application. A plurality of application ontologies and/or data usage models may be used to build a semantic hub. The semantic hub may be analyzed to identify data redundancies, data use frequency, potential data quality challenges, and/or data dependencies between applications to produce a data abstraction model that allows legacy applications to communicate with one or more data stores.