@inbook{62701,
  abstract     = {{Learning  continuous  vector  representations  for  knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class  expressions  in  Description  Logics  (DLs)  is  ante-hoc  and  globally explainable. However, state-of-the-art learners have two well-known lim-itations:  scaling  to  large  knowledge  graphs  and  handling  missing  infor-mation.  Here,  we  present  a  decision-tree-based  learner  (tDL)  to  learn Web  Ontology  Languages  (OWLs)  class  expressions  over  large  knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL  firstly constructs unique OWL expressions in .SHOIN from  concise  bounded  descriptions  of  individuals.  Each  OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL  fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the  built  CART  decision  tree  from  the  root  node  to  leaf  nodes  for  each positive example. By this, tDL  can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms  the  current state-of-the-art  models  across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL  can effectively learn accurate OWL class expressions,  while  the  state-of-the-art  models  fail  to  return  any  results. Finally,  expressions  learned  by  tDL  can  be  seamlessly  translated  into natural language explanations using a pre-trained large language model and a DL verbalizer.}},
  author       = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783032060655}},
  issn         = {{0302-9743}},
  keywords     = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}},
  location     = {{Porto, Portugal}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}},
  doi          = {{10.1007/978-3-032-06066-2_29}},
  year         = {{2025}},
}

@inproceedings{33957,
  abstract     = {{Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.}},
  author       = {{Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}},
  keywords     = {{Assistance system, Knowledge graph, Information retrieval, Neural networks, AR}},
  location     = {{Stuttgart}},
  title        = {{{AI-Based Assistance System for Manufacturing}}},
  doi          = {{10.1109/ETFA52439.2022.9921520}},
  year         = {{2022}},
}

@inproceedings{32509,
  abstract     = {{ We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of
which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach—dubbed HybridFC—that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.}},
  author       = {{Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web -- ISWC 2022}},
  editor       = {{Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti, Valentina}},
  isbn         = {{978-3-031-19433-7}},
  keywords     = {{fact checking · ensemble learning · knowledge graph veracit}},
  location     = {{Hanghzou, China}},
  pages        = {{462----480}},
  publisher    = {{Springer International Publishing}},
  title        = {{{HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs}}},
  doi          = {{10.1007/978-3-031-19433-7_27}},
  year         = {{2022}},
}