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Research About Knowledge Graph Completion Based on Active Learning
Jisuanji kexue yu tansuo, 2020 Knowledge graph completion focuses on how to improve the missing information in knowledge graph. Knowledge graph completion task has many applications, for example, it can be applied to the knowledge graph of rail transit system to support the system ...
CHEN Qinkuang, CHEN Ke, WU Sai, SHOU Lidan, CHEN Gang
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Few-Shot Knowledge Graph Completion
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available.
Zhang, Chuxu +5 more
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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
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Learning Context-based Embeddings for Knowledge Graph Completion
Journal of Data and Information Science, 2022 Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one ...
Pu Fei +3 more
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Improved Knowledge Graph Completion Method for Capsule Network [PDF]
Jisuanji gongcheng, 2020 In order to complete the missing relations between entities of knowledge graph,this paper proposes an improved knowledge graph completion method for capsule network.First,the triplets are presented as a 3-column matrix,which is convolved with multiple ...
WANG Weimei, SHI Yimin, LI Guanyu
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Open-World Knowledge Graph Completion Based on Bayesian Network [PDF]
Jisuanji gongcheng, 2021 The relations among entities in Knowledge Graph(KG) are usually interdependent, and this interdependency can be leveraged to construct more triples based on new entities in open-world data to complete KG.Bayesian Network(BN) is an effective model for ...
LI Xinbai, WU Xinran, YUE Kun
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AYNEC: All you need for evaluating completion techniques in knowledge graphs [PDF]
, 2019 The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples
Ayala Hernández, Daniel +5 more
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Completeness-Aware Rule Learning from Knowledge Graphs [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2017 Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts that are widely used in entity recognition, structured search, question answering, and similar. Rule mining is commonly applied to discover patterns in KGs. However, unlike in traditional association rule mining, KGs provide a setting with a high degree of incompleteness, which
Pellissier Tanon, T. +4 more
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Temporal Knowledge Graph Completion Using Box Embeddings
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp.
Messner, Johannes +2 more
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A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion
Information, 2022 Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion.
Haoliang Peng, Yue Wu
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