---
_id: '62701'
abstract:
- lang: eng
  text: '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:
- first_name: Caglar
  full_name: Demir, Caglar
  last_name: Demir
- first_name: Moshood
  full_name: Yekini, Moshood
  last_name: Yekini
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- first_name: Yasir
  full_name: Mahmood, Yasir
  last_name: Mahmood
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  last_name: Ngonga Ngomo
citation:
  ama: 'Demir C, Yekini M, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class
    Expression Learner over Large Graphs. In: <i>Lecture Notes in Computer Science</i>.
    Springer Nature Switzerland; 2025. doi:<a href="https://doi.org/10.1007/978-3-032-06066-2_29">10.1007/978-3-032-06066-2_29</a>'
  apa: Demir, C., Yekini, M., Röder, M., Mahmood, Y., &#38; Ngonga Ngomo, A.-C. (2025).
    Tree-Based OWL Class Expression Learner over Large Graphs. In <i>Lecture Notes
    in Computer Science</i>. European Conference on Machine Learning and Principles
    and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal.
    Springer Nature Switzerland. <a href="https://doi.org/10.1007/978-3-032-06066-2_29">https://doi.org/10.1007/978-3-032-06066-2_29</a>
  bibtex: '@inbook{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham}, title={Tree-Based
    OWL Class Expression Learner over Large Graphs}, DOI={<a href="https://doi.org/10.1007/978-3-032-06066-2_29">10.1007/978-3-032-06066-2_29</a>},
    booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
    author={Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir
    and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
  chicago: 'Demir, Caglar, Moshood Yekini, Michael Röder, Yasir Mahmood, and Axel-Cyrille
    Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large Graphs.” In
    <i>Lecture Notes in Computer Science</i>. Cham: Springer Nature Switzerland, 2025.
    <a href="https://doi.org/10.1007/978-3-032-06066-2_29">https://doi.org/10.1007/978-3-032-06066-2_29</a>.'
  ieee: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based
    OWL Class Expression Learner over Large Graphs,” in <i>Lecture Notes in Computer
    Science</i>, Cham: Springer Nature Switzerland, 2025.'
  mla: Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.”
    <i>Lecture Notes in Computer Science</i>, Springer Nature Switzerland, 2025, doi:<a
    href="https://doi.org/10.1007/978-3-032-06066-2_29">10.1007/978-3-032-06066-2_29</a>.
  short: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture
    Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.'
conference:
  end_date: 2025-09-19
  location: Porto, Portugal
  name: European Conference on Machine Learning and Principles and Practice of Knowledge
    Discovery in Databases - ECML PKDD
  start_date: 2025-09-15
date_created: 2025-11-28T14:09:17Z
date_updated: 2025-11-28T14:57:39Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-032-06066-2_29
keyword:
- Decision Tree
- OWL Class Expression Learning
- Description Logic
- Knowledge Graph
- Large Language Model
- Verbalizer
language:
- iso: eng
place: Cham
project:
- _id: '285'
  name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
publication: Lecture Notes in Computer Science
publication_identifier:
  isbn:
  - '9783032060655'
  - '9783032060662'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Tree-Based OWL Class Expression Learner over Large Graphs
type: book_chapter
user_id: '114533'
year: '2025'
...
---
_id: '33957'
abstract:
- lang: eng
  text: 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:
- first_name: Sahar
  full_name: Deppe, Sahar
  last_name: Deppe
- first_name: Lukas
  full_name: Brandt, Lukas
  last_name: Brandt
- first_name: Marc
  full_name: Brünninghaus, Marc
  last_name: Brünninghaus
- first_name: Jörg
  full_name: Papenkordt, Jörg
  id: '44648'
  last_name: Papenkordt
- first_name: Stefan
  full_name: Heindorf, Stefan
  id: '11871'
  last_name: Heindorf
  orcid: 0000-0002-4525-6865
- first_name: Gudrun
  full_name: Tschirner-Vinke, Gudrun
  last_name: Tschirner-Vinke
citation:
  ama: Deppe S, Brandt L, Brünninghaus M, Papenkordt J, Heindorf S, Tschirner-Vinke
    G. AI-Based Assistance System for Manufacturing. Published online 2022. doi:<a
    href="https://doi.org/10.1109/ETFA52439.2022.9921520">10.1109/ETFA52439.2022.9921520</a>
  apa: Deppe, S., Brandt, L., Brünninghaus, M., Papenkordt, J., Heindorf, S., &#38;
    Tschirner-Vinke, G. (2022). <i>AI-Based Assistance System for Manufacturing</i>.
    ETFA, Stuttgart. <a href="https://doi.org/10.1109/ETFA52439.2022.9921520">https://doi.org/10.1109/ETFA52439.2022.9921520</a>
  bibtex: '@article{Deppe_Brandt_Brünninghaus_Papenkordt_Heindorf_Tschirner-Vinke_2022,
    series={2022 IEEE 27th International Conference on Emerging Technologies and Factory
    Automation (ETFA)}, title={AI-Based Assistance System for Manufacturing}, DOI={<a
    href="https://doi.org/10.1109/ETFA52439.2022.9921520">10.1109/ETFA52439.2022.9921520</a>},
    author={Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt,
    Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}, year={2022}, collection={2022
    IEEE 27th International Conference on Emerging Technologies and Factory Automation
    (ETFA)} }'
  chicago: Deppe, Sahar, Lukas Brandt, Marc Brünninghaus, Jörg Papenkordt, Stefan
    Heindorf, and Gudrun Tschirner-Vinke. “AI-Based Assistance System for Manufacturing.”
    2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation
    (ETFA), 2022. <a href="https://doi.org/10.1109/ETFA52439.2022.9921520">https://doi.org/10.1109/ETFA52439.2022.9921520</a>.
  ieee: 'S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, and G.
    Tschirner-Vinke, “AI-Based Assistance System for Manufacturing.” 2022, doi: <a
    href="https://doi.org/10.1109/ETFA52439.2022.9921520">10.1109/ETFA52439.2022.9921520</a>.'
  mla: Deppe, Sahar, et al. <i>AI-Based Assistance System for Manufacturing</i>. 2022,
    doi:<a href="https://doi.org/10.1109/ETFA52439.2022.9921520">10.1109/ETFA52439.2022.9921520</a>.
  short: S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, G. Tschirner-Vinke,
    (2022).
conference:
  end_date: 2022-09-09
  location: Stuttgart
  name: ETFA
  start_date: 2022-09-06
date_created: 2022-10-28T11:43:49Z
date_updated: 2023-11-23T08:07:51Z
department:
- _id: '178'
- _id: '574'
- _id: '184'
doi: 10.1109/ETFA52439.2022.9921520
keyword:
- Assistance system
- Knowledge graph
- Information retrieval
- Neural networks
- AR
language:
- iso: eng
project:
- _id: '409'
  grant_number: 02L19C115
  name: 'KIAM: KIAM: Kompetenzzentrum KI in der Arbeitswelt des industriellen Mittelstands
    in OstWestfalenLippe'
related_material:
  link:
  - relation: confirmation
    url: https://ieeexplore.ieee.org/document/9921520
series_title: 2022 IEEE 27th International Conference on Emerging Technologies and
  Factory Automation (ETFA)
status: public
title: AI-Based Assistance System for Manufacturing
type: conference
user_id: '44648'
year: '2022'
...
---
_id: '32509'
abstract:
- lang: eng
  text: " 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\r\nwhich
    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:
- first_name: Umair
  full_name: Qudus, Umair
  id: '83392'
  last_name: Qudus
  orcid: 0000-0001-6714-8729
- first_name: Michael
  full_name: Röder, Michael
  id: '67199'
  last_name: Röder
  orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Muhammad
  full_name: Saleem, Muhammad
  last_name: Saleem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Qudus U, Röder M, Saleem M, Ngonga Ngomo A-C. HybridFC: A Hybrid Fact-Checking
    Approach for Knowledge Graphs. In: Sattler U, Hogan A, Keet M, Presutti V, eds.
    <i>The Semantic Web -- ISWC 2022</i>. Springer International Publishing; :462--480.
    doi:<a href="https://doi.org/10.1007/978-3-031-19433-7_27">10.1007/978-3-031-19433-7_27</a>'
  apa: 'Qudus, U., Röder, M., Saleem, M., &#38; Ngonga Ngomo, A.-C. (n.d.). HybridFC:
    A Hybrid Fact-Checking Approach for Knowledge Graphs. In U. Sattler, A. Hogan,
    M. Keet, &#38; V. Presutti (Eds.), <i>The Semantic Web -- ISWC 2022</i> (pp. 462--480).
    Springer International Publishing. <a href="https://doi.org/10.1007/978-3-031-19433-7_27">https://doi.org/10.1007/978-3-031-19433-7_27</a>'
  bibtex: '@inproceedings{Qudus_Röder_Saleem_Ngonga Ngomo, place={Cham}, title={HybridFC:
    A Hybrid Fact-Checking Approach for Knowledge Graphs}, DOI={<a href="https://doi.org/10.1007/978-3-031-19433-7_27">10.1007/978-3-031-19433-7_27</a>},
    booktitle={The Semantic Web -- ISWC 2022}, publisher={Springer International Publishing},
    author={Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo,
    Axel-Cyrille}, editor={Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti,
    Valentina}, pages={462--480} }'
  chicago: 'Qudus, Umair, Michael Röder, Muhammad Saleem, and Axel-Cyrille Ngonga
    Ngomo. “HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs.” In <i>The
    Semantic Web -- ISWC 2022</i>, edited by Ulrike Sattler, Aidan Hogan, Maria Keet,
    and Valentina Presutti, 462--480. Cham: Springer International Publishing, n.d.
    <a href="https://doi.org/10.1007/978-3-031-19433-7_27">https://doi.org/10.1007/978-3-031-19433-7_27</a>.'
  ieee: 'U. Qudus, M. Röder, M. Saleem, and A.-C. Ngonga Ngomo, “HybridFC: A Hybrid
    Fact-Checking Approach for Knowledge Graphs,” in <i>The Semantic Web -- ISWC 2022</i>,
    Hanghzou, China, pp. 462--480, doi: <a href="https://doi.org/10.1007/978-3-031-19433-7_27">10.1007/978-3-031-19433-7_27</a>.'
  mla: 'Qudus, Umair, et al. “HybridFC: A Hybrid Fact-Checking Approach for Knowledge
    Graphs.” <i>The Semantic Web -- ISWC 2022</i>, edited by Ulrike Sattler et al.,
    Springer International Publishing, pp. 462--480, doi:<a href="https://doi.org/10.1007/978-3-031-19433-7_27">10.1007/978-3-031-19433-7_27</a>.'
  short: 'U. Qudus, M. Röder, M. Saleem, A.-C. Ngonga Ngomo, in: U. Sattler, A. Hogan,
    M. Keet, V. Presutti (Eds.), The Semantic Web -- ISWC 2022, Springer International
    Publishing, Cham, n.d., pp. 462--480.'
conference:
  end_date: 2022-10-27
  location: Hanghzou, China
  name: International Semantic Web Conference (ISWC)
  start_date: 2022-10-23
date_created: 2022-08-02T11:56:03Z
date_updated: 2025-09-11T09:37:16Z
ddc:
- '000'
department:
- _id: '34'
doi: 10.1007/978-3-031-19433-7_27
editor:
- first_name: Ulrike
  full_name: Sattler, Ulrike
  last_name: Sattler
- first_name: Aidan
  full_name: Hogan, Aidan
  last_name: Hogan
- first_name: Maria
  full_name: Keet, Maria
  last_name: Keet
- first_name: Valentina
  full_name: Presutti, Valentina
  last_name: Presutti
file:
- access_level: closed
  content_type: application/pdf
  creator: uqudus
  date_created: 2022-12-22T15:45:29Z
  date_updated: 2022-12-22T15:45:29Z
  file_id: '34853'
  file_name: hybrid_fact_check_iswc2022.pdf
  file_size: 296218
  relation: main_file
  success: 1
file_date_updated: 2022-12-22T15:45:29Z
has_accepted_license: '1'
keyword:
- fact checking · ensemble learning · knowledge graph veracit
language:
- iso: eng
page: 462--480
place: Cham
popular_science: '1'
project:
- _id: '410'
  name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: The Semantic Web -- ISWC 2022
publication_identifier:
  isbn:
  - 978-3-031-19433-7
publication_status: accepted
publisher: Springer International Publishing
quality_controlled: '1'
status: public
title: 'HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs'
type: conference
user_id: '83392'
year: '2022'
...