With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Simple questions are the most common type of questions used for evaluating a knowledge
graph question answering (KGQA). A simple question is a question whose answer can be …

Abstract Recently, reinforcement learning (RL)-based methods have achieved remarkable
progress in both effectiveness and interpretability for complex question answering over …

The development of digital technology promotes the construction of the Intangible cultural
heritage (ICH) database but the data is still unorganized and not linked well, which makes …

Abstract The task of Named Entity Recognition (NER) is aimed at identifying named entities
in a given text and classifying them into pre-defined domain entity types such as persons …

Abstract Knowledge graph representation learning (KGRL) aims to infer the missing links
between target entities based on existing triples. Graph neural networks (GNNs) have been …

With vast information that has been digitized and made available online, manually finding
the answer to a question can be tedious. While search engines have emerged to facilitate …

In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to
empower retrieval systems, recommender systems, and question answering systems …

Deep question generation (DQG) refers to generating a complex question from different
sentences in context. Existing methods mainly focus on enhancing information extraction …

With the development of deep learning and its wide application in the field of natural
language, the question and answer research of knowledge graph based on deep learning …