activity
20192022
most citedPyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

89 citations · 184 across the 25 of their papers we have counts for

collaborators

30 papers

cs.CV20222 cited

CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering

Yao Zhang, Haokun Chen, Ahmed Frikha +5

Visual Question Answering (VQA) is a multi-discipline research task. To produce the right answer, it requires an understanding of the visual content of images, the natural language…

cs.AI20226 cited

Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information

Zifeng Ding, Jingpei Wu, Bailan He +3

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only de…

cs.AI2022

Named Entity Recognition in Industrial Tables using Tabular Language Models

Aneta Koleva, Martin Ringsquandl, Mark Buckley +2

Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformer…

cs.LG2022

Continuous Temporal Graph Networks for Event-Based Graph Data

Jin Guo, Zhen Han, Zhou Su +3

There has been an increasing interest in modeling continuous-time dynamics of temporal graph data. Previous methods encode time-evolving relational information into a low-dimension…

cs.LG20224 cited

A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs

Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin +2

The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based m…

cs.CV20224 cited

Relationformer: A Unified Framework for Image-to-Graph Generation

Suprosanna Shit, Rajat Koner, Bastian Wittmann +8

A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blo…