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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3539597.3572705

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