2 Search Results for "Lüdtke, Daniel"

Document
Invited Paper
DOI: 10.4230/OASIcs.RW.2024/2025.1
Rule-Based Knowledge Graph Completion (Invited Paper)

Authors: Patrick Betz, Christian Meilicke, and Heiner Stuckenschmidt

Published in: OASIcs, Volume 138, Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025)

Abstract
The field of knowledge graph completion is concerned with augmenting knowledge graphs with missing information. Symbolic rule-based approaches are not only efficient and interpretable but also competitive with embedding-based methods in regard to predictive quality. Rule-based knowledge graph completion can be separated into two stages, the learning stage and the application stage, which are both individually challenging. In the learning stage, horn rules are mined from a given knowledge graph. Given the vast size of the space of all possible rules, the mining approach must select relevant rules effectively. In the application stage, the mined rules are used to make new predictions which are assigned with plausibility scores. These scores need to be set by aggregating individual confidence values of rules that have the same consequence. This tutorial covers the fundamental aspects required to build a symbolic rule-based approach for knowledge graph completion. It will discuss the different rule types, mining strategies, and how to effectively apply the rules in different scenarios. Finally, we discuss practical examples for rule application by using the Python-based PyClause library.
Cite as

Patrick Betz, Christian Meilicke, and Heiner Stuckenschmidt. Rule-Based Knowledge Graph Completion (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 1:1-1:45, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{betz_et_al:OASIcs.RW.2024/2025.1,
  author =	{Betz, Patrick and Meilicke, Christian and Stuckenschmidt, Heiner},
  title =	{{Rule-Based Knowledge Graph Completion}},
  booktitle =	{Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 \& RW 2025)},
  pages =	{1:1--1:45},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-405-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{138},
  editor =	{Artale, Alessandro and Bienvenu, Meghyn and Garc{\'\i}a, Yazm{\'\i}n Ib\'{a}\~{n}ez and Murlak, Filip},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.RW.2024/2025.1},
  URN =		{urn:nbn:de:0030-drops-250461},
  doi =		{10.4230/OASIcs.RW.2024/2025.1},
  annote =	{Keywords: Knowledge Graph Completion, Rule Learning, Symbolic AI}
}
Document
DOI: 10.4230/OASIcs.WCET.2022.3
DELOOP: Automatic Flow Facts Computation Using Dynamic Symbolic Execution

Authors: Hazem Abaza, Zain Alabedin Haj Hammadeh, and Daniel Lüdtke

Published in: OASIcs, Volume 103, 20th International Workshop on Worst-Case Execution Time Analysis (WCET 2022)

Abstract
Constructing a complete control-flow graph (CGF) and computing upper bounds on loops of a computing system are essential to safely estimate the worst-case execution time (WCET) of real-time tasks. WCETs are required for verifying the timing requirements of a real-time computing system. Therefore, we propose an analysis using dynamic symbolic execution (DSE) that detects and computes upper bounds on the loops, and resolves indirect jumps. The proposed analysis constructs and initializes memory models, then it uses a satisfiability modulo theories (SMT) solver to symbolically execute the instructions. The analysis showed higher precision in bounding loops of the Mälardalen benchmarks comparing to SWEET and oRange. We integrated our analysis with the OTAWA toolbox for performing a WCET analysis. Then, we used the proposed analysis for estimating the WCET of functions in a use case inspired by an aerospace project.
Cite as

Hazem Abaza, Zain Alabedin Haj Hammadeh, and Daniel Lüdtke. DELOOP: Automatic Flow Facts Computation Using Dynamic Symbolic Execution. In 20th International Workshop on Worst-Case Execution Time Analysis (WCET 2022). Open Access Series in Informatics (OASIcs), Volume 103, pp. 3:1-3:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{abaza_et_al:OASIcs.WCET.2022.3,
  author =	{Abaza, Hazem and Haj Hammadeh, Zain Alabedin and L\"{u}dtke, Daniel},
  title =	{{DELOOP: Automatic Flow Facts Computation Using Dynamic Symbolic Execution}},
  booktitle =	{20th International Workshop on Worst-Case Execution Time Analysis (WCET 2022)},
  pages =	{3:1--3:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-244-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{103},
  editor =	{Ballabriga, Cl\'{e}ment},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.WCET.2022.3},
  URN =		{urn:nbn:de:0030-drops-166256},
  doi =		{10.4230/OASIcs.WCET.2022.3},
  annote =	{Keywords: Real-Time, WCET, Symbolic execution}
}
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