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Found 11 papers, 8 papers with code
Box-To-Box Transformations for Modeling Joint Hierarchies
In Patel et al., (2020), the authors demonstrate that only the transitive reduction is required and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction
It is well known that the graph classification performance of graph neural networks often improves by adding an artificial virtual node to the graphs, which is connected to all nodes in the graph.
Box Embeddings: An open-source library for representation learning using geometric structures
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations.
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings
In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective.
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple.
Box-To-Box Transformation for Modeling Joint Hierarchies
In Patel et al. (2020), the authors demonstrate that only the transitive reduction is required, and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Improving Local Identifiability in Probabilistic Box Embeddings
Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment.
Representing Joint Hierarchies with Box Embeddings
Box Embeddings [Vilnis et al., 2018, Li et al., 2019] represent concepts with hyperrectangles in $n$-dimensional space and are shown to be capable of modeling tree-like structures efficiently by training on a large subset of the transitive closure of the WordNet hypernym graph.
Dating Documents using Graph Convolution Networks
While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web.
Ranked #1 on
Document Dating
on APW
AD3: Attentive Deep Document Dater
Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc.
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods.
