Complex Question Answering Method on Risk Management Knowledge Graph: Multi-Intent Information Retrieval Based on Knowledge Subgraphs

Abstract

The critical aspects of risk management include hazard identification, risk assessment, and risk control. Timely risk management is critical to company decision-making, but the process of acquiring risk management knowledge is often time-consuming and labor-intensive. Knowledge graph question answering (KGQA) provides an effective solution by delivering knowledge through accurate reasoning. However, existing KGQA methods do not cover the critical risk management aspects and are difficult to retrieve quickly and accurately from large knowledge graphs. This study describes a complex question answering method for intelligently generating risk management knowledge, specifically through multi-intent information retrieval based on knowledge subgraphs. The proposed method comprises three main modules. First, in the question understanding module, we propose an intent recognition method that integrates topic entity extraction with convolutional neural networks (CNNs) to identify eleven different user intents. To enhance the retrieval efficiency, we propose a hierarchical knowledge-embedding subgraph constructed based on company and hazard descriptions. Once user intent is identified, the information retrieval module based on a novel approximate nearest neighbor (ANN) algorithm achieves deep semantic feature matching of company and hazard expressions from the knowledge embedding subgraph. After obtaining these two deep semantic features, in the answer generation module, we propose a rule-based knowledge subgraph reasoning method to answer complex questions including single-hop, multihop, constraints, and numerical calculations. On the real risk management dataset, the precision of the intent recognition module reaches 91.3% and the information retrieval module spends only 0.36 ms, verifying that the model outperforms the existing state-of-the-art models. Meanwhile, a question answering system based on the proposed method is developed to acquire risk management knowledge: Xiao An. Compared to the popular search engine and expert system for acquiring knowledge, Xiao An achieves the best results regarding ease of use, time spent, and overall performance.