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Found 9 papers, 3 papers with code
OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
Open Knowledge Graphs (OpenKG) refer to a set of (head noun phrase, relation phrase, tail noun phrase) triples such as (tesla, return to, new york) extracted from a corpus using OpenIE tools.
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification.
Ranked #3 on
Graph Classification
on FRANKENSTEIN
KVQA: Knowledge-Aware Visual Question Answering
In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e. g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research.
Unsupervised Document Representation using Partition Word-Vectors Averaging
One reason for this degradation is due to the fact that a longer document is likely to contain words from many different themes (or topics), and hence creating a single vector while ignoring all the thematic structure is unlikely to yield an effective representation of the document.
Inducing Interpretability in Knowledge Graph Embeddings
We study the problem of inducing interpretability in KG embeddings.
Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC).
Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Graph-based Semi-supervised learning (SSL) algorithms have been successfully used in a large number of applications.
