Symbolic Regression (SR) allows for the discovery of scientific equations from data.
Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability.
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system.
However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature.
Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions.
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics.
As a result, the combined model can learn relational and structural patterns.
In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders.
Ranked #1 on Node Classification on BGS
In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances.
This is done by the curation of a unified dataset that consists of website screenshots, eye-gaze heatmap and website's layout information in the form of image and text masks.
We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples.
In our approach, we build on existing strong representations of single modalities and we use hypercomplex algebra to represent both, (i), single-modality embedding as well as, (ii), the interaction between different modalities and their complementary means of knowledge representation.
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
We investigate the interaction between categorical encodings and target encoding regularization methods that reduce unfairness.
Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB.
In RDF representations, this error can be addressed by shape languages such as SHACL or ShEx, which allow for checking whether graphs are valid with respect to a set of domain constraints.
As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models.
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time.
MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis.
We introduce an approach to semantically represent and query raster data in a Semantic Web graph.
We present PubMedSection, a novel topic classification dataset focussed on the biomedical domain.
2 code implementations • 4 Mar 2020 • Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.
A detailed understanding of users contributes to the understanding of the Web's evolution, and to the development of Web applications.
How can we recognise social roles of people, given a completely unlabelled social network?
The research question this report addresses is: how, and to what extent, those directly involved with the design, development and employment of a specific black box algorithm can be certain that it is not unlawfully discriminating (directly and/or indirectly) against particular persons with protected characteristics (e. g. gender, race and ethnicity)?
In an extensive empirical experiment over English text corpora we demonstrate that our generalized language models lead to a substantial reduction of perplexity between 3. 1% and 12. 7% in comparison to traditional language models using modified Kneser-Ney smoothing.