International workshop on learning with knowledge graphs: Construction, embedding, and reasoning
Publication Type
Conference Proceeding Article
Publication Date
3-2023
Abstract
A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning.
Keywords
Graph neutral networks, Knowledge graph, Natural language processing systems, Recommender systems
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Cybersecurity; Intelligent Systems and Optimization
Publication
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
First Page
1273
Last Page
1274
ISBN
9781450394079
Identifier
10.1145/3539597.3572705
Publisher
Association for Computing Machinery
City or Country
New York, NY, United States
Citation
LI, Qing; HUANG, Xiao; LIU, Ninghao; DONG, Yuxiao; and PANG, Guansong.
International workshop on learning with knowledge graphs: Construction, embedding, and reasoning. (2023). Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 1273-1274.
Available at: https://ink.library.smu.edu.sg/sis_research/8494
Copyright Owner and License
Authors
Additional URL
https://doi.org/10.1145/3539597.3572705