Personal knowledge graph population from user utterances in conversational understanding

Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from spoken language utterances presents the novelty and numerous challenges. In this paper, we introduce a statistical language understanding approach to automatically construct personal (user-centric) knowledge graphs in conversational dialogs. Such information has the potential to better understand the users' requests, fulfilling them, and enabling other technologies such as developing better inferences or proactive interactions. Knowledge encoded in semantic graphs such as Freebase has been shown to benefit semantic parsing and interpretation of natural language utterances. Hence, as a first step, we exploit the personal factual relation triples from Freebase to mine natural language snippets with a search engine, and the resulting snippets containing pairs of related entities to create the training data. This data is then used to build three key language understanding components: (1) Personal Assertion Classification identifies the user utterances that are relevant with personal facts, e.g., “my mother's name is Rosa”; (2) Relation Detection classifies the personal assertion utterance into one of the predefined relation classes, e.g., “parents”; and (3) Slot Filling labels the attributes or arguments of relations, e.g., “name(parents): Rosa”. Our experiments using the Microsoft conversational understanding system demonstrate the performance of this proposed approach on the population of personal knowledge graphs.