Results 1 to 10 of about 7,291,916 (317)
KGAT: Knowledge Graph Attention Network for Recommendation [PDF]
Knowledge Discovery and Data Mining, 2019 To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Cao, Yixin +4 more
core +2 more sources
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering [PDF]
MATCHING, 2023 Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training.
Jinheon Baek +2 more
semanticscholar +2 more sources
Knowledge Graph Prompting for Multi-Document Question Answering [PDF]
AAAI Conference on Artificial Intelligence, 2023 The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this paradigm in multi-document question answering (MD-QA), a task demanding a
Yu Wang +5 more
semanticscholar +1 more source
A Comprehensive Survey on Automatic Knowledge Graph Construction [PDF]
ACM Computing Surveys, 2023 Automatic knowledge graph construction aims at manufacturing structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources.
Lingfeng Zhong +4 more
semanticscholar +1 more source
Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
doaj +1 more source
Research and Application Progress of Chinese Medical Knowledge Graph [PDF]
Jisuanji kexue yu tansuo, 2022 Knowledge graph is a large-scale semantic network that gives machine background knowledge. Using knowledge graph to organize heterogeneous medical information can effectively improve the utilization value of massive medical resources and promote the ...
FAN Yuanyuan, LI Zhongmin
doaj +1 more source
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021 Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future is still far from resolved.
Zixuan Li +7 more
semanticscholar +1 more source
Learning Intents behind Interactions with Knowledge Graph for Recommendation [PDF]
The Web Conference, 2021 Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).
Xiang Wang +6 more
semanticscholar +1 more source
Translational Oncology, 2021 Objectives: The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung ...
Chengdi Wang +9 more
doaj +1 more source
Knowledge Graph Self-Supervised Rationalization for Recommendation [PDF]
Knowledge Discovery and Data Mining, 2023 In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that ...
Yuhao Yang +3 more
semanticscholar +1 more source