Context-aware entity linking for knowledge graphs to support decision making

A system comprising one or more processors which alone or in combination, are configured to provide for execution of a method for operating a machine learning model comprising a context transformer and a decision head, the context transformer comprising a neural network having a plurality of self-attention layers, the machine learning model configured to make a link prediction for a query embedding, the method comprising:

• obtaining, from a first data source of the one or more external data sources, one or more first knowledge graphs and first context data associated with the one or more first knowledge graphs;
• obtaining from a second data source of the one or more external data sources, one or more second knowledge graphs and second context data associated with the one or more second knowledge graphs;
• canonicalizing the one or more first knowledge graphs, the first context data, the one or more second knowledge graphs, and the second context data to obtain canonicalized knowledge graphs and canonicalized context data;
• generating knowledge graph embeddings based on the canonicalized knowledge graphs and the canonicalized context data;
• receiving, input embeddings at inputs of the context transformer, the input embeddings comprising:
• a query embedding set, the query embedding set comprising a subject embedding, object embedding, and relation embedding, wherein one of the subject embedding, the object embedding, and the relation embedding is the query embedding;
• and
• the knowledge graph embeddings comprising first embeddings of triples associated with the respective knowledge graphs and second embeddings of the respective context data;
• generating, by a first self-attention layer of the self-attention layers, an attention score for each of the input embeddings;
• generating, by a final layer of the neural network of the context transformer, the link prediction for the query embedding and an output associated with each of the input embeddings;
• combining, by the decision head, the attention score and the output for each of the input embeddings to determine a significance score for each of the input embeddings;
• and
• outputting the link prediction and the significance score for a subset of the input embeddings.

A tangible, non-transitory computer-readable medium having instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for operating a machine learning model comprising a context transformer and a decision head, the context transformer comprising a neural network having a plurality of self-attention layers, the machine learning model configured to make a link prediction for a query embedding, the method comprising:

• obtaining from a first data source of the one or more external data sources, one or more first knowledge graphs and first context data associated with the one or more first knowledge graphs;
• obtaining, from a second ta source of the one or more external data sources, one or more second knowledge graphs and second context data associated with the one or more second knowledge graphs;
• canonicalizing the one or more first knowledge graphs, the first context data, the one or more second knowledge graphs, and the second context data to obtain canonicalized knowledge graphs and canonicalized context data;
• generating knowledge graph embeddings based on the canonicalized knowledge graphs and the canonicalized context data;
• receiving, input embeddings at inputs of the context transformer, the input embeddings comprising:
• a query embedding set, the query embedding set comprising a subject embedding, object embedding, and relation embedding, wherein one of the subject embedding, the object embedding, and the relation embedding is the query embedding;
• and
• the knowledge graph embeddings comprising first embeddings of triples associated with the respective knowledge graphs and second embeddings of the respective context data;
• generating, by a first self-attention layer of the self-attention layers, an attention score for each of the input embeddings;
• generating, by a final layer of the neural network of the context transformer, the link prediction for the query embedding and an output associated with each of the input embeddings;
• combining, by the decision head, the attention score and the output for each of the input embeddings to determine a significance score for each of the input embeddings;
• and
• outputting the link prediction and the significance score for a subset of the input embeddings.