Learning Knowledge Graphs for Question Answering through Conversational Dialog
Ben Hixon, Peter E. Clark, Hannaneh Hajishirzi · 2015
We describe how a question-answering system can learn about its domain from conversational dialogs.Our system learns to relate concepts in science questions to propositions in a fact corpus, stores new concepts and relations in a knowledge graph (KG), and uses the graph to solve questions.We are the first to acquire knowledge for question-answering from open, natural language dialogs without a fixed ontology or domain model that predetermines what users can say.Our relation-based strategies complete more successful dialogs than a query expansion baseline, our taskdriven relations are more effective for solving science questions than relations from general knowledge sources, and our method is practical enough to generalize to other domains.