Abstract: A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

Abstract: A method for automated decision making in an artificial intelligence task by fact-relevant open information extraction and knowledge graph generation includes obtaining a keyword query for performing the fact-relevant open information extraction and expanding the keyword query using keyword alias and query generation. The fact-relevant open information extraction is performed to extract triples from a text which contains the keyword or the keyword alias. The knowledge graph is generated using the extracted triples and an open knowledge graph (OpenKG) extractor that has been trained using keywords and aliases. Supervised or unsupervised classification is performed using the generated knowledge graph to make the automated decision in the artificial intelligence task.

Abstract: A method and system for performing natural language processing is provided to populate improved knowledge graphs. The technique for populating a knowledge graph includes: parsing a text document to extract one or more sentences from the text document; for each sentence in the one or more sentences, identifying a set of concept candidates for the sentence; for each concept candidate in the set of concept candidates, obtaining zero or more compound modifier children of the concept candidate; for each concept candidate and the corresponding compound modifier children, adding a first node to the knowledge graph corresponding to the concept candidate and at least one additional node to the knowledge graph corresponding to the compound modifier children; and adding relations to the knowledge graph to associate the first node with the at least one additional node.

Abstract: A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

Abstract: A machine learning model includes a context transformer and a decision head. The context transformer is a neural network of self-attention layers. The model makes a link prediction for a query embedding. Input embeddings are received at inputs of the context transformer. The input embeddings have: a query embedding set, the query embedding set comprising a subject embedding, object embedding, and relation embedding, one of the subject embedding, the object embedding, and the relation embedding being the query embedding; and knowledge graph embeddings. A first self-attention layer generates an attention score for each of the input embeddings. A final layer of the context transformer generates the link prediction for the query embedding and an output associated with each of the input embeddings. The decision head combines the attention score and the output for each of the input embeddings to determine a significance score for each of the input embeddings.