24 citations · 121 across the 50 of their papers we have counts for
10 papers · 1 filter
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
Yushi Bai, Xin Lv, Juanzi Li +1
Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries, an…
A Survey of Knowledge Enhanced Pre-trained Language Models
Linmei Hu, Zeyi Liu, Ziwang Zhao +3
Pre-trained Language Models (PLMs) which are trained on large text corpus via self-supervised learning method, have yielded promising performance on various tasks in Natural Langua…
EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph
Bowen Zhao, Jiuding Sun, Bin Xu +9
Web and artificial intelligence technologies, especially semantic web and knowledge graph (KG), have recently raised significant attention in educational scenarios. Nevertheless, s…
Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension
Xin Lv, Yankai Lin, Zijun Yao +4
Knowledge graphs, as the cornerstone of many AI applications, usually face serious incompleteness problems. In recent years, there have been many efforts to study automatic knowled…
ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction
Ji Qi, Bin Xu, Kaisheng Zeng +5
Document-level relation extraction with graph neural networks faces a fundamental graph construction gap between training and inference - the golden graph structure only available…
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing
Lunyiu Nie, Jiuding Sun, Yanlin Wang +6
The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structur…
Towards a General Pre-training Framework for Adaptive Learning in MOOCs
Qingyang Zhong, Jifan Yu, Zheyuan Zhang +6
Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
Schema-Free Dependency Parsing via Sequence Generation
Boda Lin, Zijun Yao, Jiaxin Shi +6
Dependency parsing aims to extract syntactic dependency structure or semantic dependency structure for sentences. Existing methods suffer the drawbacks of lacking universality or h…
Multimodal Entity Tagging with Multimodal Knowledge Base
Hao Peng, Hang Li, Lei Hou +2
To enhance research on multimodal knowledge base and multimodal information processing, we propose a new task called multimodal entity tagging (MET) with a multimodal knowledge bas…