Search Results for author: Steffen Staab

Found 34 papers, 15 papers with code

Shrinking Embeddings for Hyper-Relational Knowledge Graphs

no code implementations3 Jun 2023 Bo Xiong, Mojtaba Nayyer, Shirui Pan, Steffen Staab

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.

Knowledge Graphs Link Prediction

Geometric Relational Embeddings: A Survey

no code implementations24 Apr 2023 Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

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.

Hierarchical Multi-label Classification Knowledge Graph Completion +1

HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

no code implementations12 Apr 2023 Jiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, Carl Yang

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.

Decision Making Knowledge Graphs

Modeling Relational Patterns for Logical Query Answering over Knowledge Graphs

no code implementations21 Mar 2023 Yunjie He, Mojtaba Nayyeri, Bo Xiong, Evgeny Kharlamov, Steffen Staab

However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature.

Inductive Bias Knowledge Graphs

Explanation Shift: How Did the Distribution Shift Impact the Model?

no code implementations14 Mar 2023 Carlos Mougan, Klaus Broelemann, David Masip, Gjergji Kasneci, Thanassis Thiropanis, Steffen Staab

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.

Equal Treatment: Measuring Fairness using Explanation Distributions

no code implementations14 Mar 2023 Carlos Mougan, Laura State, Antonio Ferrara, Salvatore Ruggieri, Steffen Staab

Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics.

Fairness Philosophy

SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

1 code implementation9 Jan 2023 Thomas Monninger, Julian Schmidt, Jan Rupprecht, David Raba, Julian Jordan, Daniel Frank, Steffen Staab, Klaus Dietmayer

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.

Knowledge Graphs Node Classification

Predicting Eye Gaze Location on Websites

no code implementations15 Nov 2022 Ciheng Zhang, Decky Aspandi, Steffen Staab

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.

Gaze Prediction

Explanation Shift: Detecting distribution shifts on tabular data via the explanation space

no code implementations22 Oct 2022 Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, Steffen Staab

We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples.

Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces

no code implementations4 Aug 2022 Mojtaba Nayyeri, ZiHao Wang, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Md Rashad Al Hasan Rony, Jens Lehmann, Steffen Staab

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.

Knowledge Graph Embedding Knowledge Graphs +1

Ultrahyperbolic Knowledge Graph Embeddings

no code implementations1 Jun 2022 Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.

Knowledge Graph Embeddings

Detecting Anomalies within Time Series using Local Neural Transformations

1 code implementation8 Feb 2022 Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, Maja Rudolph

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.

Anomaly Detection Epidemiology +5

Fairness Implications of Encoding Protected Categorical Attributes

2 code implementations27 Jan 2022 Carlos Mougan, Jose M. Alvarez, Salvatore Ruggieri, Steffen Staab

We investigate the interaction between categorical encodings and target encoding regularization methods that reduce unfairness.

Fairness Feature Engineering

Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History

1 code implementation9 Dec 2021 Lukas Schmelzeisen, Corina Dima, Steffen Staab

Wikidata is the largest general-interest knowledge base that is openly available.

ProGS: Property Graph Shapes Language (Extended Version)

1 code implementation12 Jul 2021 Philipp Seifer, Ralf Lämmel, Steffen Staab

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.

Knowledge Graphs

Learning Gradual Argumentation Frameworks using Genetic Algorithms

1 code implementation25 Jun 2021 Jonathan Spieler, Nico Potyka, Steffen Staab

As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models.

BIG-bench Machine Learning Interpretable Machine Learning

Pseudo-Riemannian Graph Convolutional Networks

1 code implementation6 Jun 2021 Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab

Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.

Graph Reconstruction Inductive Bias +2

Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction

1 code implementation11 Mar 2021 Alexandra Baier, Zeyd Boukhers, Steffen Staab

Physical motion models offer interpretable predictions for the motion of vehicles.

LaHAR: Latent Human Activity Recognition using LDA

1 code implementation23 Nov 2020 Zeyd Boukhers, Danniene Wete, Steffen Staab

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.

Clustering Human Activity Recognition

MOFA: Modular Factorial Design for Hyperparameter Optimization

no code implementations18 Nov 2020 Bo Xiong, Yimin Huang, Hanrong Ye, Steffen Staab, Zhenguo Li

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.

Hyperparameter Optimization Model Selection

Knowledge Graphs

2 code implementations4 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.

Knowledge Graphs

Understanding Social Networks using Transfer Learning

no code implementations16 Oct 2019 Jun Sun, Steffen Staab, Jérôme Kunegis

A detailed understanding of users contributes to the understanding of the Web's evolution, and to the development of Web applications.

Transfer Learning

Observing and Recommending from a Social Web with Biases

no code implementations25 Apr 2016 Steffen Staab, Sophie Stalla-Bourdillon, Laura Carmichael

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)?

A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser-Ney Smoothing

1 code implementation13 Apr 2014 Rene Pickhardt, Thomas Gottron, Martin Körner, Paul Georg Wagner, Till Speicher, Steffen Staab

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.

Language Modelling

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