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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
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
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
A type-augmented knowledge graph embedding framework for knowledge graph completion
Scientific Reports, 2023 Knowledge graphs (KGs) are of great importance to many artificial intelligence applications, but they usually suffer from the incomplete problem.
Peng He +4 more
doaj +1 more source
Knowledge graph-enhanced molecular contrastive learning with functional prompt
Nature Machine Intelligence, 2023 Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient.
Yin Fang +7 more
semanticscholar +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
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
IEEE Access, 2022 In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Since the basic
Qifeng Zou, Chaoze Lu
doaj +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
Frontiers in Big Data, 2023 The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data.
Chenwei Yan +6 more
doaj +1 more source