1.8k citations · 2.1k across the 44 of their papers we have counts for
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GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs
Ruijie Wang, Luca Rossetto, Michael Cochez +1
Despite the rapid progress of large language models (LLMs), knowledge graph-based question answering (KGQA) remains essential for producing verifiable and hallucination-resistant a…
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency
Azhar Shaikh, Michael Cochez, Denis Diachkov +2
This paper introduces DONUT-hole, a sparse OCR-free visual document understanding (VDU) model that addresses the limitations of its predecessor model, dubbed DONUT. The DONUT model…
A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs
Tamara Cucumides, Daniel Daza, Pablo Barceló +4
The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community. Neuro-symbolic models have emerged as…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
Daniel Daza, Dimitrios Alivanistos, Payal Mitra +3
Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embed…
Geometric Relational Embeddings: A Survey
Bo Xiong, Mojtaba Nayyeri, Ming Jin +4
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for str…