A method, apparatus, system, and computer program code for intelligent data discovery with dynamic ontology are provided. According to one illustrative embodiment, the method using a number of processors to perform the steps of: identifying a set of data items in unstructured content using a dynamic data schema populated from a dynamic ontology; and responsive to identifying a data item that is not recognized in the data schema: storing the data item with labels; generating a weight for the data item; and responsive to the weight exceeding a threshold, updating the schema to include the data item that was not recognized.

Systems and methods for facilitating an enterprise user to obtain an answer to a user question within an enterprise based on an enterprise knowledge graph are provided. In particular, an enterprise server may receive the user question from the enterprise user, determine a suggested topic associated with the user question based on the enterprise knowledge graph by transforming the user question into a semantic representation to identify a plurality of similar entities within the enterprise knowledge graph, and determine whether a relevant question-and-answer (Q&A) pair linked to the suggested topic exists based on the enterprise knowledge graph. In response to a determination that the relevant Q&A pair does not exist, the enterprise server may determine a predicted answer to the user question and update the enterprise knowledge graph.

A method includes storing data in a knowledge graph stored in a database. The knowledge graph is defined by nodes connected by edges, in which a root node of the knowledge graph is connected to a first version node by a first edge. The root node represents a data asset, and the first version node represents a first version of the data asset. A status indicator associated with the first version node has a first state indicating that the first version of the data asset is an editable draft version of the data asset. Responsive to receiving an instruction to publish the first version of the data asset, the state of the status indicator is changed to a second state that indicates that the first version of the data asset is a published version of the data asset.

A POI popularity derivation device (10) includes: a dictionary generation unit (11) that assigns a feature word used as a co-occurrence word of a POI name to each popularity-assigned POI name serving as a popularity assignment target to generate a popularity-assigned POI dictionary in which a popularity-assigned POI name and a feature word are associated with each other; an extraction unit (12) that extracts posted data serving as a search target from posted data on the basis of predetermined criteria; and a popularity derivation unit (18) that searches for the posted data on the basis of a predetermined rule regarding feature words while referring to the popularity-assigned POI dictionary, to extract posted data linked to the popularity-assigned POI name, and derives the popularity of each popularity-assigned POI name on the basis of the number of pieces of extracted posted data for each popularity-assigned POI name.

Methods and systems are disclosed herein for updating a knowledge graph based on a user confirmation. A media guidance application receives a user communication and isolates a term of the user communication. The media guidance application identifies a candidate component of a knowledge graph associated with the term. The media guidance application requests user input directed to confirming whether the term is associated with the candidate component. In response to receiving the user input, the media guidance application modifies a strength of association between the term and the component.

Document retrieval through assertion analysis on entities and document fragments is disclosed. A document is received. Logical structures and entities are extracted from the document by parsing the document. For an entity in the extracted entities, an object representing the entity is created, an assertion made in the document associated with the entity is determined, and the assertion is linked to the object representing the entity. A logical structure from the extracted logical structures and content of the logical structure containing the assertion are identified and linked to the object representing the entity.

Computer-implemented systems and methods are disclosed to interface with one or more storage devices storing a plurality of documents, wherein each of the plurality of documents is associated with one or more tags of one or more predefined hierarchies of tags, wherein the one or more hierarchies of tags include multiple dimensions. In accordance with some embodiments, a method is provided to identify one or more documents from the data storage devices. The method comprises acquiring, via an interface, a selection of one or more tags of the one or more predefined hierarchies of tags. The method further comprises identifying one or more documents from the data storage devices in response to the selection, the identified one or more documents having tags that have a relationship with the selected tags, and providing data corresponding to the identified documents for displaying in the interface.

A Thing Machine is provided having a processor, non-transitory memory, non-transitory computer readable media, and performable machine code P(TM). The P(TM) is comprised of a first set of performable machine code actions, having one or more performable machine code P(TM(i)) action, wherein each performable machine code P(TM(i)) action is configured as an implementation of an algorithmic procedure of a model, wherein a first P(TM(i)) provides an action of self-configuring a first vocabulary of Things in said non-transitory memory of the Thing Machine, said Things representative of Things that said processor can perform as actions, and the set of Things an action can act upon, and wherein at least one P(TM(i)) machine code action is performed to configure a second vocabulary of Things in the non-transitory memory of the Thing Machine representative of a core vocabulary through which an application can be provided.

A Thing Machine is provided having a processor, non-transitory memory, non-transitory computer readable media, and performable machine code P(TM). The P(TM) is comprised of a first set of performable machine code actions, having one or more performable machine code P(TM(i)) action, wherein each performable machine code P(TM(i)) action is configured as an implementation of an algorithmic procedure of a model, wherein a first P(TM(i)) provides an action of self-configuring a first vocabulary of Things in said non-transitory memory of the Thing Machine, said Things representative of Things that said processor can perform as actions, and the set of Things an action can act upon, and wherein at least one P(TM(i)) machine code action is performed to configure a second vocabulary of Things in the non-transitory memory of the Thing Machine representative of a core vocabulary through which an application can be provided.

A method, system and computer readable medium for performing a cognitive search operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive search operation on a corpus of content based upon the cognitive profile, the cognitive search operation returning cognitive results specific to the cognitive profile of the user.