activity
20202022
most citedEvaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models

32 citations · 34 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions

Anson Bastos, Abhishek Nadgeri, Kuldeep Singh +2

Learning on evolving(dynamic) graphs has caught the attention of researchers as static methods exhibit limited performance in this setting. The existing methods for dynamic graphs…

cs.IR20211 cited

HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations

Anson Bastos, Kuldeep Singh, Abhishek Nadgeri +3

Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in dense vector space and employed in downstream tasks such as li…

cs.CL20211 cited

KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

Abhishek Nadgeri, Anson Bastos, Kuldeep Singh +4

We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially…

cs.CL2020

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

Anson Bastos, Abhishek Nadgeri, Kuldeep Singh +4

In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). R…

cs.CL202032 cited

Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models

Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu +3

Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from text to improve the performance of several NLP tasks. These models,…