Improvement of Search of Logical Forms in Question Answering based on the Knowledge Graph using Logical Form Patterns

Abstract

In recent years, various applications such as semantic search, question answering, and dialogue systems using large-scale knowledge graphs such as Wikidata and DBpedia have been studied. This study focuses on Question Answering over Knowledge Graphs (KGQA). To promote and evaluate KGQA studies, multiple question answering data sets based on large-scale knowledge graphs have been constructed. CSQA is a question answering benchmark based on Wikidata. CSQA targets interactive question answering, and there are 10 question types, some of which require inference, such as comparison and set operation. In the previous study, a multi-task semantic parsing model that converts utterances to logical forms that express operations on a database can answer with high accuracy. The conversion to a logical form is performed by defining the grammar for deriving the logical form and predicting the order in which the grammar is applied from the utterance. To learn a model for the prediction, it is necessary to search for a logical form that can answer the question from logical forms that can be generated by applying the grammar. However, since the search space is enormous, depending on the search method, problems such as a decrease in the success rate of the search and incorrect logical forms occur, which may adversely affect the learning of the model. In this research, we propose a method for searching logical forms with a high success rate of the search in a short time by using patterns of logical forms. The proposed method consists of logical form pattern search unit, logical form search unit, and logical form determination unit. The logical form pattern search unit searches for a logical form pattern that can generate a logical form that can answer questions. The logical form search unit searches only logical forms that can be created from logical form patterns obtained. The logical form determination unit decides which logical form to use for learning. For each logical form pattern, count the number of questions that can be answered by the logical form generated from that pattern. For each logical forms searched for each question, select a logical form generated from the logical form pattern with the largest number. In the evaluation, we searched the logical format for each of the 10 question types in the dataset used, with 5 000 questions each, and compared the search success rate with the search time. We also compared the accuracy of question answering when learning the existing system using the searched questions. The search success rate increased for 9 out of 10 question types in the dataset used in the experiment. The search time was reduced for 7 out of 10 question types. The question answering accuracy improved for 8 out of 10 question types.