Question Answering on Agricultural Knowledge Graph Based on Multi-label Text Classification

Abstract

AbstractTraditional search engines retrieve relevant web pages based on keywords in the entered questions, while sometimes the required information may not be included in these keyword-based retrieved web pages. Compared to the search engines, the question answering system can provide more accurate answers. However, traditional question answering systems can only provide answers to users based on matching the questions in a question answering pair. At the same time, the number of question answering pairs remain somewhat limited. As a result, the user’s requirements cannot be met well. In contrast, knowledge graphs can store information such as entities and their relationships in a structured pattern. Therefore, the knowledge graph is highly scalable as the data is stored in a structured form. Besides, the relationship between entities and the knowledge graph structure allows the desired answer to be found quickly. Moreover, the process of relation classification can also be regarded as an operation of text classification. Therefore, this study proposed a new approach to knowledge graph-based question answering systems that require a named entity recognition method and a multi-label text classification method to search for the answers. The results of entity name and question type are turned into a Cypher query that searches for the answer in the knowledge graph. In this paper, three models, i.e., TextCNN, bi-LSTM, and bi-LSTM + Att, are used to examine the effectiveness of multi-label text classification, demonstrating our method’s feasibility. Among these three models, TextCNN worked best, attaining an F1 score of 0.88.KeywordsAgricultural information presentation and metricsKnowledge graphQuestion answering systemMulti-label text classification