4 papers
A Survey of Large Language Models for Data Challenges in Graphs
Mengran Li, Pengyu Zhang, Wenbin Xing +11
Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. While graph learning has a…
Implications of construction decisions in keyword-based networks: an empirical assessment
James Nevin, Salvatore Flavio Pileggi, Michael Lees +1
The large amounts of data continuously generated online offer opportunities to identify and analyse trends in various aspects of society. For instance, data from online social medi…
TIGER: Temporally Improved Graph Entity Linker
Pengyu Zhang, Congfeng Cao, Paul Groth
Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many u…
CYCLE: Cross-Year Contrastive Learning in Entity-Linking
Pengyu Zhang, Congfeng Cao, Klim Zaporojets +1
Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in…