@article{25209,
  author       = {{Demir, Caglar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{A shallow neural model for relation prediction}}},
  volume       = {{abs/2101.09090}},
  year         = {{2021}},
}

@article{25213,
  author       = {{Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike}},
  journal      = {{CoRR}},
  title        = {{{MLCheck- Property-Driven Testing of Machine Learning Models}}},
  volume       = {{abs/2105.00741}},
  year         = {{2021}},
}

@article{25215,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{Out-of-Vocabulary Entities in Link Prediction}}},
  volume       = {{abs/2105.12524}},
  year         = {{2021}},
}

@article{25217,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{DRILL- Deep Reinforcement Learning for Refinement Operators in ALC}}},
  volume       = {{abs/2106.15373}},
  year         = {{2021}},
}

@inproceedings{28350,
  abstract     = {{In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks).
In this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software
discrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime}},
  author       = {{Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike}},
  booktitle    = {{Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}},
  publisher    = {{IEEE}},
  title        = {{{MLCHECK–Property-Driven Testing of Machine Learning Classifiers}}},
  year         = {{2021}},
}

@inproceedings{29287,
  abstract     = {{Knowledge graph embedding research has mainly focused on the two smallest
normed division algebras, $\mathbb{R}$ and $\mathbb{C}$. Recent results suggest
that trilinear products of quaternion-valued embeddings can be a more effective
means to tackle link prediction. In addition, models based on convolutions on
real-valued embeddings often yield state-of-the-art results for link
prediction. In this paper, we investigate a composition of convolution
operations with hypercomplex multiplications. We propose the four approaches
QMult, OMult, ConvQ and ConvO to tackle the link prediction problem. QMult and
OMult can be considered as quaternion and octonion extensions of previous
state-of-the-art approaches, including DistMult and ComplEx. ConvQ and ConvO
build upon QMult and OMult by including convolution operations in a way
inspired by the residual learning framework. We evaluated our approaches on
seven link prediction datasets including WN18RR, FB15K-237 and YAGO3-10.
Experimental results suggest that the benefits of learning hypercomplex-valued
vector representations become more apparent as the size and complexity of the
knowledge graph grows. ConvO outperforms state-of-the-art approaches on
FB15K-237 in MRR, Hit@1 and Hit@3, while QMult, OMult, ConvQ and ConvO
outperform state-of-the-approaches on YAGO3-10 in all metrics. Results also
suggest that link prediction performances can be further improved via
prediction averaging. To foster reproducible research, we provide an
open-source implementation of approaches, including training and evaluation
scripts as well as pretrained models.}},
  author       = {{Demir, Caglar and Moussallem, Diego and Heindorf, Stefan and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The 13th Asian Conference on Machine Learning, ACML 2021}},
  title        = {{{Convolutional Hypercomplex Embeddings for Link Prediction}}},
  year         = {{2021}},
}

@article{25350,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{A Physical Embedding Model for Knowledge Graphs}}},
  volume       = {{abs/2001.07418}},
  year         = {{2020}},
}