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.

To effectively utilize knowledge of relationship information among material property parameters the users tangibly and intangibly have in a search system that generates a graph in which material property parameters are nodes and relationships of the material property parameters are edges from a database of material property parameter pairs whose relationships are already known, and conducts a path search in the generated graph. A search system, which includes the database, a graph generator that generates the graph, and a graph searcher searches the graph, further includes a user interface and a user information storage unit corresponding to each user. The user conducts a search unique to the user by inputting relationship information between the material property parameters that he has to the user information storage unit and integrating the relationship information into the above graph. Further, by accumulating a history of searches conducted by the user in the user information storage unit and analyzing the search history, the user can be provided with new knowledge.

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.

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.

A computer-implemented method for generating a description of a target skill set using domain specific language, a computer program product, and a system. Embodiments may comprise, on a processor, ingesting a data set related to the target skill from a data store, semantically analyzing the data set to generate a skill ontology, generating a hyperplane to separate one or more priority skills from among the plurality of related skills, generating a description for the target skill from the one or more priority skills, and presenting the generated description to a user. The skill ontology may include relationships between the target skill and a plurality of related skills.

Technical solution relates to the fields of voice technologies and knowledge graph technologies. A technical solution includes: acquiring script matched with a scenario from a speech package, and acquiring a broadcast template configured for the scenario in advance; and filling the broadcast template with the script to generate the broadcast speech.

An approach to ranking identified technical solutions summaries may be provided. The approach may include extracting data from technical tickets, subject matter expert reports, and online forum data. The approach may include receiving data relating to prior applications of one or more technical solutions. Steps associated with a technical solution may be included in the information from the prior application of the technical solutions and updated based on the information from prior applications of technical solutions. The approach may include generating a risk score and a cost score for the updated technical solution based on contextual factors associated with a user or machine. The approach may include enriching a static summary for the technical solution with the cost and risk score. The approach may include ranking the enriched summary against multiple potential technical solutions.

Aspects described herein may relate to methods, systems, and apparatuses that determine one or more categories associated with a dataset, or a portion thereof. The determination may be performed based on one or more tags associated with the dataset and/or a description associated with the dataset. Further, the determination may be performed by searching an ontology based on the one or more tags and/or the description. The determination may be performed by using a machine-learning model based on the one or more tags and/or the description. Once the one or more categories associated with the dataset are determined, the one or more categories may be used as a basis for modifying the dataset and/or validating the dataset.