Papers by Adrian Kochsiek
Sequence-to-Sequence Knowledge Graph Completion and Question Answering (2022.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embeddable vectors. |
| Approach: | They propose to use an off-the-shelf encoder-decoder Transformer model to generate a knowledge graph embedding model that can be used for KG link prediction and incomplete KG question answering. |
| Outcome: | The proposed model outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning. |
LibKGE - A knowledge graph embedding library for reproducible research (2020.emnlp-demos)
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| Challenge: | Knowledge graph embedding models are trained to predict false triples and high scores for true triples. |
| Approach: | LibKGE is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. |
| Outcome: | LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. |
A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Semi-inductive link prediction (LP) is a task of predicting facts for new, previously unseen entities based on context information. |
| Approach: | They propose to use Wikidata5M to evaluate semi-inductive link prediction (LP) in knowledge graphs. |
| Outcome: | The proposed benchmark provides a test bed for further research into semi-inductive link prediction (LP) in knowledge graphs. |