Results 31 to 40 of about 8,113 (297)

Explainable Temporal Knowledge Graph Reasoning via Expressive Logic Rules [PDF]

Temporal Knowledge Graphs (TKGs) capture dynamic event behaviors with temporal information. However, existing TKG link prediction methods are predominantly embedding-based, lacking interpretability and transparency.
Wang, Z, Wang, K, Feng, Z, Wu, H, Zuo, J, Bao, X, Zhang, X   +6 more
core   +1 more source

STKE: Temporal Knowledge Graph Embedding in the Spherical Coordinate System [PDF]

, 2022
Knowledge graph embedding (KGE) aims to learn the representation of entities and predicates in low-dimensional vector spaces which can complete the missing parts of the Knowledge Graphs (KGs).
Shen, Linshan, Liu, Ruinan, Wang, Shibo, Khattak, Asad Masood   +3 more
core   +2 more sources

Research on SARIMA-based embedding for temporal knowledge graph reasoning

Dianzi Jishu Yingyong
This paper focuses on reasoning for Temporal Knowledge Graph (TKG) in periodic scenarios and proposes the EI-SAR model. The model integrates the temporal decomposition capability of the SARIMA model with Temporal Knowledge Graph Embedding (TKGE ...
Wu Yu, Hu Youde, Feng Tong, Du Yingming, Cao Han, Tang Jing, Li Xinbing   +6 more
doaj   +1 more source

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022
EMNLP 2022 Full ...
Yuwei Xia, Mengqi Zhang 0002, Qiang Liu 0006, Shu Wu, Xiaoyu Zhang 0002   +4 more
openaire   +2 more sources

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas F. Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Retractions in Rheumatology: Trends, Causes, and Implications for Research Integrity

Arthritis Care &Research, EarlyView.
Objective We aimed to describe the trends and main reasons for study retraction in rheumatology literature. Methods We reviewed the Retraction Watch database to identify retracted articles in rheumatology. We recorded the main study characteristics, authors’ countries, reasons for retraction, time from publication to retraction, and trends over time ...
Anna Maria Vettori, Michele Iudici
wiley   +1 more source

Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning [PDF]

, 2023
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual
Li, Zheng, Cao, Tianyu, Zhang, Chao, Yang, Jingfeng, Yin, Bing, Wang, Ruijie, Abdelzaher, Tarek   +6 more
core   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph [PDF]

Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications.
Cai, X, Jiang, Z, Mei, X, Han, J, Yang, L, Gao, D, Pan, S   +6 more
core   +1 more source

Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task.
Hao Dong 0010, Zhiyuan Ning 0001, Pengyang Wang, Ziyue Qiao, Pengfei Wang 0008, Yuanchun Zhou, Yanjie Fu   +6 more
openaire   +2 more sources