相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Computer Science, Artificial Intelligence
Shaoxiong Ji et al.
Summary: This survey provides a comprehensive review of knowledge graphs, covering topics such as knowledge graph representation learning, knowledge acquisition and completion, temporal knowledge graphs, and knowledge-aware applications. The study proposes a categorization and taxonomies on these topics, as well as explores emerging themes like metarelational learning, commonsense reasoning, and temporal knowledge graphs. Additionally, the research offers curated data sets and open-source libraries to facilitate future research in the field of knowledge graphs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Li Li et al.
Summary: Cause analysis is crucial in fault diagnosis system, and a fuzzy Petri net is a commonly used model for knowledge representation. However, existing FPNs have limitations in cause analysis, which inspired the development of an enhanced grey reasoning Petri net (EGRPN) to address these issues. The EGRPN model utilizes grey numbers to tackle uncertain knowledge, enhancing the reliability of knowledge reasoning processes.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Tiantian Chen et al.
Summary: This study proposes a joint extraction model named PARE-Joint, which addresses the interaction between entities and relations as well as the overlapping triple problem using position-aware attention and relation embedding. The experimental results demonstrate that the proposed model outperforms other baselines on four public datasets.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Ren Li et al.
Summary: This paper presents a two-stage tagging scheme and a lightweight joint extraction neural model based on the entity-first labeling strategy, which outperforms baseline approaches in relation extraction task and achieves competitive entity recognition effect through comprehensive experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Kang Zhao et al.
Summary: This paper proposes a relation extraction model RIFRE based on heterogeneous graph neural networks. Through representation iterative fusion, it successfully establishes effective connections between entities and relations, improving the accuracy and efficiency of relation extraction. Empirical results on multiple datasets have demonstrated the superior performance of RIFRE.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
ZhiQiang Geng et al.
INFORMATION SCIENCES
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Arzoo Katiyar et al.
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1
(2017)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Claire Gardent et al.
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Sanjay K. Dwivedi et al.
FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE: MODELING TECHNIQUES AND APPLICATIONS (CIMTA) 2013
(2013)
