Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers. In addition, most of previous works built on either RNN-based or Transformer based models to encode a linearized KG sugraph, which totally discards the explicit structure information of a KG subgraph. To address this issue, we propose to apply a bidirectional Graph2Seq model to encode the KG subgraph. Furthermore, we enhance our RNN decoder with node-level copying mechanism to allow directly copying node attributes from the KG subgraph to the output question. Both automatic and human evaluation results demonstrate that our model achieves new state-of-the-art scores, outperforming existing methods by a significant margin on two QG benchmarks. Experimental results also show that our QG model can consistently benefit the Question Answering (QA) task as a mean of data augmentation.
Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
Beijing University of Posts and Telecommunications（北京邮电大学）；Huawei Technologies CO., LTD（华为技术有限公司）；University of Lugano（卢加诺大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering
Institute of Computing and Intelligence, Harbin Institute of Technology（哈尔滨工业大学计算与智能学院）；University of Electronic Science and Technology of China（电子科技大学）
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
UMass Amherst（马萨诸塞大学阿默斯特分校）；University of Southern California（南加州大学）；Google DeepMind（谷歌DeepMind）；University of Washington（华盛顿大学）
知识库上的问答（QA）是一个具有挑战性的问题，因为它需要各种各样的推理模式。然而，我们假设在一个大的KB，推理模式需要回答的查询类型重新出现在各自的子图邻域中的各种实体。利用不同子图的局部邻域之间的这种结构相似性，我们引入了半参数模型（CBR-SUBG）具有（i）非参数分量，对于每个查询，动态检索其他类似的$k$ -最近邻（KNN）训练查询以及查询特定的子图，以及（ii）被训练以识别（潜在）从KNN查询的子图中推理模式，然后将它们应用于目标查询的子图。我们还提出了一个自适应的子图收集策略来选择一个查询特定的紧凑子图，使我们能够扩展到包含数十亿事实的完整Freebase KB。我们表明，CBR-SUBG可以回答需要子图推理模式的查询，并在几个KBQA基准测试中与最佳模型竞争。我们的子图收集策略还产生更紧凑的子图（例如，WebQSP的大小减少了55\%，而答案召回率增加了4.85\%）\footnote{Code, model, and subgraphs are available at \url{https://github.com/rajarshd/CBR-SUBG}}。
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Ant Group（蚂蚁集团）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；JP Olive S&T Management LLC（JP Olive科技管理有限责任公司）
SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法北京市重点实验室）；School of Information, Renmin University of China（中国人民大学信息学院）；Alibaba Group（阿里巴巴集团）
Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation
School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Universität Hamburg（汉堡大学）；Nazarbayev University（纳扎尔巴耶夫大学）；ISP RAS（俄罗斯科学院信息安全问题研究所）；Artificial Intelligence Research Institute（人工智能研究院）
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
Stanford University（斯坦福大学）
图像不仅仅是对象或属性的集合--它们代表了相互连接的对象之间的关系网。场景图作为一种新的图像结构化图形表示形式出现。场景图将对象编码为通过成对关系连接的节点作为边。为了支持场景图上的问答，我们提出了GraphVQA，一个语言引导的图神经网络框架，它将自然语言问题翻译和执行为图节点之间消息传递的多次迭代。我们探索了GraphVQA框架的设计空间，并讨论了不同设计选择的权衡。我们在GQA数据集上的实验表明，GraphVQA的性能大大优于最先进的模型（88.43% vs. 94.78%）。
