32 citations · 61 across the 13 of their papers we have counts for
14 papers
Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
Aneta Koleva, Martin Ringsquandl, Ahmed Hatem +2
Interest in solving table interpretation tasks has grown over the years, yet it still relies on existing datasets that may be overly simplified. This is potentially reducing the ef…
Adversarial Attacks on Tables with Entity Swap
Aneta Koleva, Martin Ringsquandl, Volker Tresp
The capabilities of large language models (LLMs) have been successfully applied in the context of table representation learning. The recently proposed tabular language models have…
Active Learning with Tabular Language Models
Martin Ringsquandl, Aneta Koleva
Despite recent advancements in tabular language model research, real-world applications are still challenging. In industry, there is an abundance of tables found in spreadsheets, b…
Named Entity Recognition in Industrial Tables using Tabular Language Models
Aneta Koleva, Martin Ringsquandl, Mark Buckley +2
Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformer…
Combining Sub-Symbolic and Symbolic Methods for Explainability
Anna Himmelhuber, Stephan Grimm, Sonja Zillner +3
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide…
Demystifying Graph Neural Network Explanations
Anna Himmelhuber, Mitchell Joblin, Martin Ringsquandl +1
Graph neural networks (GNNs) are quickly becoming the standard approach for learning on graph structured data across several domains, but they lack transparency in their decision-m…
Generating Table Vector Representations
Aneta Koleva, Martin Ringsquandl, Mitchell Joblin +1
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG). The focus of this paper is an evaluation of methods for table-to-class a…
On Event-Driven Knowledge Graph Completion in Digital Factories
Martin Ringsquandl, Evgeny Kharlamov, Daria Stepanova +4
Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such fac…
Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids
Martin Ringsquandl, Houssem Sellami, Marcel Hildebrandt +4
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspon…
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
Yushan Liu, Marcel Hildebrandt, Mitchell Joblin +3
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems. The nature of biological data leads to a graph structure that differ…
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
Yushan Liu, Marcel Hildebrandt, Mitchell Joblin +2
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range depende…
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma +3
We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between…
Reasoning on Knowledge Graphs with Debate Dynamics
Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma +3
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between…
Context-Aware Analytics in MOM Applications
Martin Ringsquandl, Steffen Lamparter, Raffaello Lepratti
Manufacturing Operations Management (MOM) systems are complex in the sense that they integrate data from heterogeneous systems inside the automation pyramid. The need for context-a…