61 citations · 133 across the 14 of their papers we have counts for
14 papers
Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Bo Xiong, Mojtaba Nayyer, Shirui Pan +1
Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a triple. A significant amount of important kno…
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
Tim Schneider, Amin Totounferoush, Wolfgang Nowak +1
Symbolic Regression (SR) allows for the discovery of scientific equations from data. To limit the large search space of possible equations, prior knowledge has been expressed in te…
Geometric Relational Embeddings: A Survey
Bo Xiong, Mojtaba Nayyeri, Ming Jin +4
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for str…
The Role of Large Language Models in the Recognition of Territorial Sovereignty: An Analysis of the Construction of Legitimacy
Francisco Castillo-Eslava, Carlos Mougan, Alejandro Romero-Reche +1
We examine the potential impact of Large Language Models (LLM) on the recognition of territorial sovereignty and its legitimization. We argue that while technology tools, such as G…
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
Jiaying Lu, Jiaming Shen, Bo Xiong +3
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a unifor…
Emotional Framing in the Spreading of False and True Claims
Akram Sadat Hosseini, Steffen Staab
The explosive growth of online misinformation, such as false claims, has affected the social behavior of online users. In order to be persuasive and mislead the audience, false cla…
Modeling Relational Patterns for Logical Query Answering over Knowledge Graphs
Yunjie He, Mojtaba Nayyeri, Bo Xiong +3
Answering first-order logical (FOL) queries over knowledge graphs (KG) remains a challenging task mainly due to KG incompleteness. Query embedding approaches this problem by comput…
Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models
Cosimo Gregucci, Mojtaba Nayyeri, Daniel Hernández +1
Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a…
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht +5
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to enco…
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report
Daniel Frank, Decky Aspandi Latif, Michael Muehlebach +2
Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide an…
Predicting Eye Gaze Location on Websites
Ciheng Zhang, Decky Aspandi, Steffen Staab
World-wide-web, with the website and webpage as the main interface, facilitates the dissemination of important information. Hence it is crucial to optimize them for better user int…
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
Carlos Mougan, Klaus Broelemann, Gjergji Kasneci +2
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In the past, predictive performance was considered the key indicator…
Faithiful Embeddings for EL++ Knowledge Bases
Bo Xiong, Nico Potyka, Trung-Kien Tran +2
Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledg…
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka +3
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.…