7 citations · 17 across the 3 of their papers we have counts for
8 papers
Knowledge Hypergraph Embedding Meets Relational Algebra
Bahare Fatemi, Perouz Taslakian, David Vazquez +1
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do not capture the procedural rules underlying t…
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi
Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this pr…
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Bahare Fatemi, Perouz Taslakian, David Vazquez +1
Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on…
Improved Knowledge Graph Embedding using Background Taxonomic Information
Bahare Fatemi, Siamak Ravanbakhsh, David Poole
Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be u…
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Nandini Ramanan, Gautam Kunapuli, Tushar Khot +5
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight…
Record Linkage to Match Customer Names: A Probabilistic Approach
Bahare Fatemi, Seyed Mehran Kazemi, David Poole
Consider the following problem: given a database of records indexed by names (e.g., name of companies, restaurants, businesses, or universities) and a new name, determine whether t…