Incorporating Ontological Information in KG Learning & Applications
May 10 | KGC 2023
•
29m
In this talk, we explores how such hierarchical ontological components in knowledge graphs are incorporated into KG representation learning. We present multiple practical machine learning methods, such as hierarchical graph modeling, graph neural networks, self-supervised learning, and language models, that can effectively and efficiently capture ontological information, given different knowledge graph formulations. As a result, our proposed approaches address various real-world challenges in multiple domains, from knowledge graph itself to diverse disciplines including natural language processing (language models), recommender systems, bioinformatics, and societal studies, and expand ML frontiers to knowledge graphs to multi-modal applications.
Up Next in May 10 | KGC 2023
-
The Fellowship of the Graph
The maturing of the knowledge graph use cases leads to the sophistication of the requirements. Despite vendor promises, a single product can rarely cover all capabilities for all knowledge graph applications.
In this presentation, we will discuss our experience in implementing production systems ... -
Knowledge Graphs in Media
This talk will look at Knowledge Graphs in publishing and broadcast media. Where have things moved on since pioneering projects using Linked Data? Have knowledge graphs significantly changed the landscape? We explore the topic through a set of case studies looking at how knowledge graphs are chan...
-
The Key to Solving Broken Data Analys...
Tired of wondering why your critical business numbers don’t match? Overhead of propagating data knowledge in your organization getting you down? Hear from dbt Labs Director (Data & Community) Anna Filippova on how to apply a knowledge graph approach to managing data in an organization to help you...
