Abstract: An approach is provided for combining location data sources. The approach, for instance, involves generating a first context-aware vector representation of a first location entity in a first data source and a second context-aware vector representation of a second location entity in a second data source. The approach also comprises processing the first context-aware vector representation and the second context-aware vector representation using a machine learning model to perform a classification of the first location entity as the same as the second location entity. The approach further comprises combining the first data source and the data source into a new database based on the classification and providing the new database as an output.

Abstract: An approach is provided for providing a location representation for machine learning tasks. The approach, for instance, involves receiving multi-modal relational location data associated with a location as an input to a machine learning model. The approach also involves initiating a processing of the multi-modal relational location data using the machine learning model. The approach further involves extracting a vector representation of the location from a hidden layer of the machine learning model after the processing and providing the vector representation of the location as a location embedding output.

Abstract: An approach is provided for combining location data sources. The approach, for instance, involves generating a first context-aware vector representation of a first location entity in a first data source and a second context-aware vector representation of a second location entity in a second data source. The approach also comprises processing the first context-aware vector representation and the second context-aware vector representation using a machine learning model to perform a classification of the first location entity as the same as the second location entity. The approach further comprises combining the first data source and the data source into a new database based on the classification and providing the new database as an output.

Abstract: An approach is provided for a context-aware location representation. The approach, for instance, involves receiving a knowledge graph that represents location entities as location nodes and relationships between the location entities as location edges The approach also involves processing multi-modal data associated with the location entities to determine a plurality of tokens. The approach further involves creating a hypergraph that represents the tokens as token nodes. The hypergraph includes: (1) a first edge type that relates a token node to a location node of the knowledge graph, and (2) a second edge type that relates a first token node to a second token node. The approach further involves selecting a vertex of the hypergraph and performing a random walk to generate a node sequence comprising a subset of one or more nodes of the hypergraph. The approach further involves generating a node embedding based on the node sequence as the context-aware location representation.

Abstract: An approach is provided for semantic categorization of arbitrarily granular locations. The approach, for instance, involves receiving a location subgraph specified at an arbitrary geographic granularity. The location subgraph, for instance, comprises multi-modal relational location data associated with one or more location entities. The approach also comprises processing the location subgraph using a machine learning model to predict a semantic category representing the location subgraph and/or the one or more location entities in the location subgraph. The approach further comprises providing the semantic category as an output.

Abstract: A method, apparatus and computer program product are provided to evaluate natural language input to identify actions and landmarks, such as for the generation of a landmark graph. In a method, one or more word representations are encoded in context based upon one or more other word representations within a sentence of the natural language input. The method also generates an action context vector defining actions that occur during each of a plurality of time steps and generates a state context vector defining one or more landmarks associated with each of the plurality of time steps. The method predicts the state for a respective time step by defining the one or more landmarks associated with the respective time step. The method also predicts an action for the respective time step based on the action context vector and the state context vector.

Abstract: A method, apparatus and computer program product are provided to evaluate natural language input to identify actions and landmarks, such as for the generation of a landmark graph. In a method, one or more word representations are encoded in context based upon one or more other word representations within a sentence of the natural language input. The method also generates an action context vector defining actions that occur during each of a plurality of time steps and generates a state context vector defining one or more landmarks associated with each of the plurality of time steps. The method predicts the state for a respective time step by defining the one or more landmarks associated with the respective time step. The method also predicts an action for the respective time step based on the action context vector and the state context vector.