Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion [PDF]
Transactions of the Association for Computational LinguisticsYongxue Shan +5 more
doaj +2 more sources
Knowledge Graph Completion for the Chinese Text of Cultural Relics Based on Bidirectional Encoder Representations from Transformers with Entity-Type Information [PDF]
Entropy (Basel), 2020 Min Zhang +3 more
openalex +2 more sources
Knowledge Graph Completeness: A Systematic Literature Review [PDF]
IEEE Access, 2021 The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality
Issa, Subhi +5 more
openaire +4 more sources
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
Survey on Inductive Learning for Knowledge Graph Completion [PDF]
Jisuanji kexue yu tansuo, 2023 Knowledge graph completion can make knowledge graph more complete. However, traditional knowledge graph completion methods assume that all test entities and relations appear in the training process.
LIANG Xinyu, SI Guannan, LI Jianxin, TIAN Pengxin, AN Zhaoliang, ZHOU Fengyu
doaj +1 more source
QubitE:Qubit Embedding for Knowledge Graph Completion [PDF]
Jisuanji kexue, 2023 The knowledge graph completion task completes the knowledge graph by predicting missing facts in the knowledge graph.The quantum-based knowledge graph embedding(KGE) model uses variational quantum circuits to score triples by mea-suring the probability ...
LIN Xueyuan, E Haihong , SONG Wenyu, LUO Haoran, SONG Meina
doaj +1 more source
TuckER: Tensor Factorization for Knowledge Graph Completion [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones.
Ivana Balazevic +2 more
semanticscholar +1 more source
Survey on Few-Shot Knowledge Graph Completion Technology [PDF]
Jisuanji kexue yu tansuo, 2023 Few-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data.
PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong
doaj +1 more source
Learning Sequence Encoders for Temporal Knowledge Graph Completion [PDF]
Conference on Empirical Methods in Natural Language Processing, 2018 Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line
Alberto García-Durán +2 more
semanticscholar +1 more source
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system.
Xiang Chen +8 more
semanticscholar +1 more source