A personal knowledge graph comprising people as nodes, their personal data as node attributes, and their relationships as edges has a number of applications in de-identification, master data management, and fraud prevention . While artificial neural networks have led to significant improvements in different tasks in cold start knowledge graph population , the overall F1 of the  system remains quite low. This problem is more acute in personal knowledge graph population which presents additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators to augment training data for neural models, and for slot filling to increase the diversity of the populated knowledge graph. We also propose a representative set sampling method to use the populated knowledge graph data for downstream applications. We introduce new resources and discuss our results .
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents . While artificial neural networks have led to significant improvements in the different tasks that are part of KBP , the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset .
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A personal knowledge graph comprising people as nodes, their personal data as node attributes, and their relationships as edges has a number of applications in de-identification, master data management, and fraud prevention
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents
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different tasks in cold start knowledge graph population
the different tasks that are part of KBP
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end-to-end
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graph population which presents
bases, which present
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to augment training data for neural models, and for slot filling to increase the diversity of the populated knowledge graph. We also propose a representative set sampling method to use the populated knowledge graph data for downstream applications. We introduce new resources and discuss our results
and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset