Results 31 to 40 of about 9,217,308 (160)

A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

CoRR, 2023
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting ...
Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong 0001, Heng Liu, Linhao Luo, Tengfei Liu 0005, Yongli Hu, Baocai Yin, Wen Gao 0001   +10 more
openaire   +3 more sources

Graph-based implicit knowledge discovery from architecture change logs [PDF]

, 2012
Service architectures continuously evolve as a consequence of frequent business and technical change cycles. Architecture change log data represents a source of evolution-centric information in terms of intent, scope and operationalisation to ...
Pooyan Jamshidi (5276344), Aakash, Ahmad, Aakash Ahmad, Ahmad, Aakash, Claus Pahl (12588787), Pahl, Claus, Muteer Arshad, Jamshidi, Pooyan, Pooyan Jamshidi, Arshad, Muteer, Claus Pahl, Muteer Arshad (12620287), Aakash Ahmad (5022314)   +12 more
core   +2 more sources

A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion [PDF]

, 2022
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time.
Tresp, Volker, Ding, Zifeng, He, Bailan, Ma, Yunpu   +3 more
core   +1 more source

Temporal knowledge graph completion:methods and progress

大数据, 2021
Temporal knowledge graph (TKG) are obtained by adding the time information of real-world knowledge to classical knowledge graphs.Recently, TKG completion has drawn much attention and become a hot topic in research.Two main methodologies for TKG ...
Yuming SHEN, Jianfeng DU
doaj  

Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion [PDF]

, 2023
Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems.
Poon, Josiah, Wang, Kunze, Han, Soyeon Caren   +2 more
core   +1 more source

A Convolutional Neural Network With Time-Aware Channel Weighting for Temporal Knowledge Graph Completion

IEEE Access
Temporal Knowledge Graphs (TKGs) extend traditional knowledge graphs by incorporating a temporal dimension into triples, enabling a more precise modeling of dynamic relationships.
Kesheng Zhang, Guige Ouyang, Yongzhong Huang   +2 more
doaj   +1 more source

Temporal Graph Analysis using Gradoop

, 2019
The temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems.
Thor, Andreas, Rost, Christopher, Rahm, Erhard   +2 more
core   +1 more source

Abnormal entity-aware knowledge graph completion

, 2022
In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service
Sun, Ke, Peng, Ciyuan, Naseriparsa, Mehdi, Li, Xiang, Xia, Feng, Yu, Shuo   +5 more
core   +1 more source

A Temporal Knowledge Graph Embedding Model Based on Variable Translation

Tsinghua Science and Technology
Knowledge representation learning (KRL) aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors. It is popularly used in knowledge graph completion (or link prediction) tasks.
Yadan Han, Guangquan Lu, Shichao Zhang, Liang Zhang, Cuifang Zou, Guoqiu Wen   +5 more
doaj   +1 more source

Frequency-Modulated Spiral Manifold: A Model for Temporal Knowledge Graph Completion

Mathematics
Temporal knowledge graph completion (TKGC) infers missing facts by modeling the temporal evolution of relations. Existing methods typically encode time through low-dimensional geometric transformations or frequency-domain decomposition.
Xindong You, Kai Long, Zhaojun Wang, Likun Lu, Kai Zhang   +4 more
doaj   +1 more source