Results 21 to 30 of about 7,092,434 (358)
InGram: Inductive Knowledge Graph Embedding via Relation Graphs [PDF]
International Conference on Machine Learning, 2023 Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training.
Jaejun Lee +2 more
semanticscholar +1 more source
Nature Communications, 2022 Resistance to EGFR inhibitors presents a major obstacle in treating non-small cell lung cancer. Here, the authors develop a recommender system ranking genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of ...
Anna Gogleva +14 more
doaj +1 more source
Exploring Large Language Models for Knowledge Graph Completion [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph completion.
Liang Yao +3 more
semanticscholar +1 more source
A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications
Mathematics, 2023 As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios.
Yong Chen +5 more
doaj +1 more source
Convolutional 2D Knowledge Graph Embeddings [PDF]
AAAI Conference on Artificial Intelligence, 2017 Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs.
Tim Dettmers +3 more
semanticscholar +1 more source
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 Knowledge graph completion (KGC) aims to reason over known facts and infer the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations from natural language descriptions, and have the potential for inductive KGC ...
Liang Wang +3 more
semanticscholar +1 more source
Knowledge Graph Contrastive Learning for Recommendation [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items.
Yuhao Yang +3 more
semanticscholar +1 more source
Scientific Reports, 2021 This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19.
Feng Pan +10 more
doaj +1 more source
Analysis of Knowledge Graph Path Reasoning Based on Variational Reasoning
Applied Sciences, 2022 Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities.
Hongmei Tang +5 more
doaj +1 more source
Text-Graph Enhanced Knowledge Graph Representation Learning
Frontiers in Artificial Intelligence, 2021 Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space.
Linmei Hu +6 more
doaj +1 more source