Search Results for author:
Found 14 papers, 3 papers with code
Wiki-TabNER:Advancing Table Interpretation Through Named Entity Recognition
Finally, we propose a prompting framework for evaluating the newly developed large language models (LLMs) on this novel TI task.
Adversarial Attacks on Tables with Entity Swap
The recently proposed tabular language models have reported state-of-the-art results across various tasks for table interpretation.
Active Learning with Tabular Language Models
Despite recent advancements in tabular language model research, real-world applications are still challenging.
Named Entity Recognition in Industrial Tables using Tabular Language Models
Specialized transformer-based models for encoding tabular data have gained interest in academia.
Combining Sub-Symbolic and Symbolic Methods for Explainability
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making.
Demystifying Graph Neural Network Explanations
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-making.
Generating Table Vector Representations
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG).
On Event-Driven Knowledge Graph Completion in Digital Factories
Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e. g., in the form of knowledge graphs.
Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring.
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems.
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks.
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively.
Reasoning on Knowledge Graphs with Debate Dynamics
The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.
Context-Aware Analytics in MOM Applications
Manufacturing Operations Management (MOM) systems are complex in the sense that they integrate data from heterogeneous systems inside the automation pyramid.
