Search Results for author:
Found 10 papers, 7 papers with code
Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs
Hence, as a reference implementation, we develop a simple neural model induced with EFT for capturing evolving graph spectra.
Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?
$\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology.
Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions
Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra.
How Expressive are Transformers in Spectral Domain for Graphs?
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
SpecTRA: Spectral Transformer for Graph Representation Learning
Transformers have recently been applied in the more generic domain of graphs.
HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
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).
QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph
In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility.
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
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).
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
