Content of Knowledge-Based Question Answering Systems in our journal

Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Survey of Open-Domain Knowledge Graph Question Answering CHEN Zirui, WANG Xin, WANG Lin, XU Dawei, JIA Yongzhe Journal of Frontiers of Computer Science and Technology    2021, 15 (10): 1843-1869.   DOI: 10.3778/j.issn.1673-9418.2106095 Knowledge graph question answering (KGQA) is the procedure of processing natural language questions posed by users to obtain relevant answers from knowledge graph (KG) based on some form of KG. Due to the limitation of knowledge scale, computing power and natural language processing capability, the early knowledge base question answering systems were limited to closed-domain questions. In recent years, with the development of KG and the construction of open-domain question answering (QA) datasets, KG has been used for open-domain QA research and practice. In this paper, in accordance with the development of technology, the open-domain KGQA is summarized. Firstly, five rule and template based KGQA methods are reviewed, including traditional semantic parsing, traditional information retrieval, triplet matching, utterance template, and query template. This type of methods mainly relies on manually defined rules and templates to complete QA task. Secondly, five deep learning based KGQA methods are introduced, which use neural network models to complete the subtasks of QA process, including knowledge graph embedding, memory network, neural network-based semantic parsing, neural network-based query graph, and neural network-based information retrieval method. Thirdly, four general domain KG and eleven open-domain QA datasets, which KGQA commonly used are described. Fourthly, three classic KGQA datasets are selected according to the difficulty of questions to compare and analyze the performance metric of each KGQA system, and the effect between above methods. Finally, this paper looks forward to the future research directions on this topic. Reference | Related Articles | Metrics Abstract （1351） PDF （1951） Select Incorporating Subgraph Structure Knowledge Base Question Answering via Neural Reasoning CHEN Ziyang, LIAO Jinzhi, ZHAO Xiang, CHEN Yingguo Journal of Frontiers of Computer Science and Technology    2021, 15 (10): 1870-1879.   DOI: 10.3778/j.issn.1673-9418.2106094 As an effective representation model of the real world knowledge, knowledge base (or knowledge graph) has attracted wide attention from academia and industry. In recent years, with the emergence of large-scale knowledge bases, knowledge base question answering has also attracted attention as a basic application technology of knowledge bases. Among them, the typical method based on semantic parsing transforms questions into answer retrieval on graphs by parsing query sentences,  however, which neglects that there are often missing links in knowledge bases. As a result, the above process might fall short in some cases. The typical model based on neural reasoning performs entity similarity ranking by encoding questions, but it cannot solve the cold start problem of given entities in dynamic scenarios. To address the above problems, a neural inference knowledge base question-and-answer method incorporating subgraph structures is proposed to achieve a more adequate inference by taking into account the semantic and structural information of entities in the question-and-answer inference process. Firstly, the question and answer are converted into vectors containing semantic information by the pre-training model RoBERTa. Secondly, the corresponding question and answer subgraphs are constructed based on the entities in the question and answer, and the structural information of the subgraphs is extracted using graph neural networks. Then, the entity representations are pre-trained based on the background knowledge base and fused with the corresponding structural representations.  Finally, the candidate answers are rated based on the fused vectors, and the entity with the highest rating is considered as the answer. Extensive experiments are conducted on the WebQuestionsSP dataset, and the experimental results show that the proposed model outperforms other benchmark models. Reference | Related Articles | Metrics Abstract （508） PDF （525） Select Question-aware Graph Convolutional Network for Educational Knowledge Base Question Answering LIN Qika, ZHANG Lingling, LIU Jun, ZHAO Tianzhe Journal of Frontiers of Computer Science and Technology    2021, 15 (10): 1880-1887.   DOI: 10.3778/j.issn.1673-9418.2106093 In recent years, with the continuous informatization of education and the accumulation of massive education resources and teaching data, some educational knowledge bases have been proposed, which provides a good deve-lopment condition for data-driven intelligent education. The question answering method based on educational know-ledge base can provide learners with instant tutoring, and then effectively improve their learning interest and efficiency. However, there are few studies on educational knowledge base question answering (KBQA), and most of the open domain KBQA methods independently model question sentences and candidate answer entities, so the effect of modeling is limited. Based on this, this paper proposes a question answering method of educational knowledge base based on question-aware graph convolutional network (GCN). Firstly, for a specific question, the description information and query entity set of the question are extracted. And they are processed respectively by Transformer and pre-trained embeddings of the knowledge base. Secondly, the subgraph of candidate answer set is extracted from the knowledge base according to the query entity set, and the node information is updated by the GCN with two attention mechanisms. The scores of attention are expressed by the question description and the query entity set respectively. In this way, the question-aware GCN is realized. Finally, the query description information, query entity set and candidate entity representation are fused to calculate the score and predict the answer. Experiments are carried out on the real data set MOOC Q&A, and the prediction accuracy and mean reciprocal rank are used to evaluate. The experimental results show that the proposed method is superior to the benchmark models. Reference | Related Articles | Metrics Abstract （333） PDF （632）