73 citations · 113 across the 15 of their papers we have counts for
16 papers
Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection
Fina Polat, Daniel Daza, Pengyu Zhang +2
Entity Disambiguation (ED) is a key task for constructing and using knowledge graphs. State-of-the-art neural approaches commonly model ED as a single task, although it consists of…
Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation
Yannick Brunink, Daniel Daza, Yunjie He +1
Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit graph structure, allowing them to…
Discovering Association Rules in High-Dimensional Small Tabular Data
Erkan Karabulut, Daniel Daza, Paul Groth +1
Association Rule Mining (ARM) aims to discover patterns between features in datasets in the form of propositional rules, supporting both knowledge discovery and interpretable machi…
Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello +4
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by di…
EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge
Klim Zaporojets, Daniel Daza, Edoardo Barba +3
Knowledge Graphs (KGs) are structured knowledge repositories containing entities and relations between them. In this paper, we study the problem of automatically updating KGs over…
Explaining Graph Neural Networks for Node Similarity on Graphs
Daniel Daza, Cuong Xuan Chu, Trung-Kien Tran +3
Similarity search is a fundamental task for exploiting information in various applications dealing with graph data, such as citation networks or knowledge graphs. While this task h…