Abstract: One or more processor can automatically identify, structure and retrieve spatial and/or temporal sequences of digital media content according to semantic specification. Digital media content can be received and information from digital media content can be extracted. Based on the information, a knowledge graph can be constructed or structured to include at least one of spatial and temporal representation of the digital media content. A search query can be received associated with the digital media content. Based on traversing the knowledge graph structure according to at least one of spatial and temporal criterion mapped from the search query, new digital media content can be composed which meets the search query.

Abstract: Entities and temporal information associated with the entities can be extracted from the documents using natural language processing. A graph structure can be created representing the document's temporal semantics, nodes of the graph structure including the entities and edges of the graph structure representing temporal relationships between the nodes. The graph structure can be linked with the document. Multiple documents can be received and a knowledgebase can be created including multiple graph structures representing the multiple documents according to the multiple documents' temporal semantics. An input document for query can be received and transformed into a graph structure for query, the graph structure for query representing the input document's temporal semantics. The knowledgebase can be searched for a matching document having a graph structure similar to the graph structure for query based on a similarity threshold. The matching document can be output.

Abstract: Automatically mapping and combining the application of machine learning models to answer queries according to semantic specification. A query is parsed to extract keywords from the query and to contextualize the query. Based on the keywords, machine learning models are selected that process concepts associated with the keywords. The machine learning models are sorted according to the contextualization of the query. The machine learning models are run on multimodal data according to a sorted order, where data resulting from an output of one of the machine learning models is used as input to another one of the machine learning models. A query result is output based on a result from running the machine learning models.

Abstract: A method and system of creating a knowledge graph includes capturing information of a user interacting with given data, as user interaction data. The user interaction data is structured as a trail of actions over time. An ontology for a domain related to the user interaction data is received. Each action of the trail of actions is matched onto entities of the ontology. The knowledge graph is created based on the ontology having the matched actions.

Abstract: A computer-implemented method of performing an incremental specification of a query includes extracting text from each of a plurality of participants in a dialog. A contextual information is determined of the extracted text of one or more of the plurality of participants. A dialog understanding operation is performed by processing at least the contextual information of the extracted text in a knowledge graph to identify in the dialog at least one or more of a structural gap, an information about entities, relationships, and actions. Query information is provided responsive to the dialog for at least one of filling the identified structural gap, or for providing additional information about one or more of the identified entities, relationships or actions in the dialog.

Abstract: One or more processor can automatically identify, structure and retrieve spatial and/or temporal sequences of digital media content according to semantic specification. Digital media content can be received and information from digital media content can be extracted. Based on the information, a knowledge graph can be constructed or structured to include at least one of spatial and temporal representation of the digital media content. A search query can be received associated with the digital media content. Based on traversing the knowledge graph structure according to at least one of spatial and temporal criterion mapped from the search query, new digital media content can be composed which meets the search query.