International Business Machines Corporation

Guilherme Augusto Ferreira Lima

Marcio Ferreira Moreno

Rafael R De Mello Brandao

Achille Belly Fokoue-Nkoutche

Rosario A Uceda-Sosa

Maria Chang

Rafael Rossi De Mello Brandao

Alexander Gray

Isabela Drabik Chaves Chambers Ramos

Marcelo Machado

Provided are techniques for a search using an overlay graph mapping to source knowledge graphs. A plurality of overlay graphs are generated, where each overlay graph comprises entities represented by nodes and relations represented by edges, and where the entities and the relations map to a subset of entities and relations in a plurality of source knowledge graphs. A search request comprising an entity and a relation is received. An overlay graph is selected from the plurality of overlay graphs based on the entity and the relation. The search request is issued against the overlay graph, where the search request is translated to knowledge graph specific queries, and where the knowledge graph specific queries are issued against the plurality of source knowledge graphs. Search results are received from the plurality of source knowledge graphs. The search results are used to respond to the search request.

Search using an overlay graph mapping to source knowledge graphs

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.

Mapping machine learning models to answer queries

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.

Retrieving documents according to temporal similarity

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.

Automatically and incrementally specifying queries through dialog understanding in real time

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.

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Campinas, Brazil

Guilherme Augusto Ferreira Lima