Techniques include obtaining, with a computer system, a natural-language-text document comprising unstructured text; generating, with the computer system, based on a first set of machine learning model parameters, a neural representation of the unstructured text; identifying, with the computer system, based on the neural representation, a trigger word located within the unstructured text and associated with a first category; determining, with the computer system, based on the trigger word, a region within the unstructured text comprising descriptors associated with the first category; determining, with the computer system, from the region based on a second set of machine learning model parameters, a descriptor describing an action or condition of the first category; generating, with the computer system, a data model object comprising the descriptor defining an action or condition of the first category; and storing, with the computer system, the data model object in memory.

A selection of content from a content presentation is received. At least one topic from the selected content is extracted using natural language processing (NLP). The at least one topic is representative of a subject conveyed within the selected content. At least one perspective associated with the at least one topic is extracted using NLP. The at least one perspective is representative of a point of view conveyed within the selected content regarding the at least one topic. A topic rating of the extracted topics and associated perspectives is determined based upon the extracted topics and associated perspectives. The topic rating is representative of a topic diversity among the extracted topics and associated perspectives. The topic rating is presented within a graphical user interface (GUI).

An example method of determining geolocations of objects based on information retrieved from heterogeneous data sources comprises: receiving, from a first data source associated with an object by an ontology-defined relationship, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the object; receiving, from a second data source associated with an object by an ontology-defined relationship, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the object and a corresponding time identifier.

Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store, wherein the ERs are generated, updated/modified, and/or accessed via a graph-based domain ontology.

A method may include a obtaining a first data model instance comprising an identifier string and a set of attributes associated with a set of attribute name strings. The method may include obtaining an ontology graph that includes a first label, a second label, and an association between them. The method may include using a prediction model to select the first label based on the first data model instance and determining the second label based on the relationship. The method may include determining a selected set of labels that includes the first label and the second label to associate with the first data model instance. The method may include associating the selected set of labels with the first data model instance in a dataset that includes a plurality of records, where each record is associated with a different data model instance.

Embodiments may determine user intent in conversations with dialogue systems so as to improve the quality of such conversations and to reduce the number of failed conversations. For example, a method may comprise receiving, at a dialogue system, a first text utterance from a user, generating a plurality of second text utterances at the dialogue system in response to the received text utterance, generating a third text utterance based on each generated second text utterance using a trained deep neural network model, generating a score indicating a quality of each conversation, wherein each conversation includes the first text utterance, one of the second text utterances, and the third text utterance based on the one of the second text utterances, and outputting to the user the second text utterance included in the conversation having the highest quality score.

Systems, computer-implemented methods, and computer program products to facilitate query recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ontology component that can generate an ontology based on unstructured data of a description of an application programming interface. The computer executable components can further comprise a reasoner component that can identify one or more terms of the ontology that correspond semantically to a term of a query.

An Artificial Intelligence (AI)-based data processing system employs a trained AI model for extracting features of products from various product classes and building a product ontology from the features. The product ontology is used to respond to user queries with product recommendations and customizations. Training data for the generation of the AI model for feature extraction is initially accessed and verified to determine of the training data meets a data density requirement. If the training data does not meet the data density requirement, data from one of a historic source or external sources is added to the training data. One of the plurality of AI models is selected for training based on the degree of overlap and the inter-class distance between the datasets of the various product classes within the training data.

Some embodiments of the current disclosure disclose methods and systems for analyzing root causes of an incident disrupting information technology services such as cloud services. In some embodiments, a set of problem review board (PRB) documents including information about said incidents may be parsed using a natural language processing (NLP) neural model to extract structured PRB data from the unstructured investigative information contained in the PRB documents. The structured PRB data may include symptoms of the incident, root causes of the incident, resolutions of the incidents, etc., and a causal knowledge graph causally relating the symptoms, root causes, resolutions of the incidents may be generated.

A method may include accessing a report definition template, the report definition template identifying a set of data requirement for a report; mapping the set of data requirements to a corresponding semantic object in a semantic ontology; parsing a semantic map to determine a database table storing data for the semantic object; retrieving the data for the semantic object from the database table; generating a report data file adhering to the semantic object ontology based in part on the retrieved data; transmitting a logical location of the generated report data file, a logical location of the semantic map, and logical location of the semantic ontology to a blockchain node for adding to a report block in the blockchain.