A System for Natural Language Interaction With the Heterogeneous Information Network

Abstract

In this chapter, the authors present their system, which can use natural language query to interact with heterogeneous information networks (HIN). This chapter proposes a solution combining the GraphFrames, recurrent neural network (RNN) long short-term memory (LSTM), and dependency relation of question for generating, training, understanding the question-answer pairs and selecting the best match answer for this question. The RNN-LSTM is used to generate the answer from the facts of knowledge graph. The authors need to build a training data set of question-answer pairs from a very large knowledge graph by using GraphFrames for big graph processing. To improve the performance of GraphFrames, they repartition the GraphFrames. For complicated query, they use the Stanford dependency parser to analyze the question and build the motif pattern for searching GraphFrames. They also develop a chatbot that can interact with the knowledge graph by using the natural language query. They conduct their system with question-answer generated from DBLP to prove the performance of our proposed system.