88 citations · 95 across the 6 of their papers we have counts for
7 papers
Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion
Ananjan Nandi, Navdeep Kaur, Parag Singla +1
High-quality and high-coverage rule sets are imperative to the success of Neuro-Symbolic Knowledge Graph Completion (NS-KGC) models, because they form the basis of all symbolic inf…
DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion
Ananjan Nandi, Navdeep Kaur, Parag Singla +1
We consider two popular approaches to Knowledge Graph Completion (KGC): textual models that rely on textual entity descriptions, and structure-based models that exploit the connect…
NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs
Ishaan Singh, Navdeep Kaur, Garima Gaur +1
While Knowledge Graph Completion (KGC) on static facts is a matured field, Temporal Knowledge Graph Completion (TKGC), that incorporates validity time into static facts is still in…
Knowledge Graph Alignment using String Edit Distance
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity bet…
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for st…
Neural Networks for Relational Data
Navdeep Kaur, Gautam Kunapuli, Saket Joshi +2
While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domain…