3 Search Results for "Maus, Heiko"

Document
Use Case
DOI: 10.4230/TGDK.3.3.2
Automating Invoice Validation with Knowledge Graphs: Optimizations and Practical Lessons

Authors: Johannes Mäkelburg and Maribel Acosta

Published in: TGDK, Volume 3, Issue 3 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 3

Abstract
To increase the efficiency of creating, distributing, and processing of invoices, invoicing is handled in the form of Electronic Data Interchange (EDI). With EDI, invoices are handled in a standardized electronic or digital format rather than on paper. While EDIFACT is widely used for electronic invoicing, there is no standardized approach for validating its content. In this work, we tackle the problem of automatically validating electronic invoices in the EDIFACT format by leveraging KG technologies. We build on a previously developed pipeline that transforms EDIFACT invoices into RDF knowledge graphs (KGs). The resulting graphs are validated using SHACL constraints defined in collaboration with domain experts. In this work, we improve the pipeline by enhancing the correctness of the invoice representation, reducing validation time, and introducing error prioritization through the use of the severity predicate in SHACL. These improvements make validation results easier to interpret and significantly reduce the manual effort required. Our evaluation confirms that the approach is correct, efficient, and practical for real-world use.
Cite as

Johannes Mäkelburg and Maribel Acosta. Automating Invoice Validation with Knowledge Graphs: Optimizations and Practical Lessons. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 3, pp. 2:1-2:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{makelburg_et_al:TGDK.3.3.2,
  author =	{M\"{a}kelburg, Johannes and Acosta, Maribel},
  title =	{{Automating Invoice Validation with Knowledge Graphs: Optimizations and Practical Lessons}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:24},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.3.2},
  URN =		{urn:nbn:de:0030-drops-252137},
  doi =		{10.4230/TGDK.3.3.2},
  annote =	{Keywords: Electronic Invoice, Ontology, EDIFACT, RDF, RML, SHACL}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.9
Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Authors: Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives.
Cite as

Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal. Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 9:1-9:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{ibanez_et_al:TGDK.1.1.9,
  author =	{Ib\'{a}\~{n}ez, Luis-Daniel and Domingue, John and Kirrane, Sabrina and Seneviratne, Oshani and Third, Aisling and Vidal, Maria-Esther},
  title =	{{Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{9:1--9:32},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.9},
  URN =		{urn:nbn:de:0030-drops-194839},
  doi =		{10.4230/TGDK.1.1.9},
  annote =	{Keywords: Trust, Accountability, Autonomy, AI, Knowledge Graphs}
}
Document
DOI: 10.4230/OASIcs.LDK.2019.11
Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications

Authors: Christian Jilek, Markus Schröder, Rudolf Novik, Sven Schwarz, Heiko Maus, and Andreas Dengel

Published in: OASIcs, Volume 70, 2nd Conference on Language, Data and Knowledge (LDK 2019)

Abstract
A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems - just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines. In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step, we address word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing real-time capable runtime performance.
Cite as

Christian Jilek, Markus Schröder, Rudolf Novik, Sven Schwarz, Heiko Maus, and Andreas Dengel. Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 11:1-11:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{jilek_et_al:OASIcs.LDK.2019.11,
  author =	{Jilek, Christian and Schr\"{o}der, Markus and Novik, Rudolf and Schwarz, Sven and Maus, Heiko and Dengel, Andreas},
  title =	{{Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications}},
  booktitle =	{2nd Conference on Language, Data and Knowledge (LDK 2019)},
  pages =	{11:1--11:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Eskevich, Maria and de Melo, Gerard and F\"{a}th, Christian and McCrae, John P. and Buitelaar, Paul and Chiarcos, Christian and Klimek, Bettina and Dojchinovski, Milan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2019.11},
  URN =		{urn:nbn:de:0030-drops-103759},
  doi =		{10.4230/OASIcs.LDK.2019.11},
  annote =	{Keywords: Ontology-based information extraction, Named entity recognition, Inflectional languages, Real-time systems}
}
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