---
_id: '46516'
abstract:
- lang: eng
  text: Linked knowledge graphs build the backbone of many data-driven applications
    such as search engines, conversational agents and e-commerce solutions. Declarative
    link discovery frameworks use complex link specifications to express the conditions
    under which a link between two resources can be deemed to exist. However, understanding
    such complex link specifications is a challenging task for non-expert users of
    link discovery frameworks. In this paper, we address this drawback by devising
    NMV-LS, a language model-based verbalization approach for translating complex
    link specifications into natural language. NMV-LS relies on the results of rule-based
    link specification verbalization to apply continuous training on T5, a large language
    model based on the Transformerarchitecture. We evaluated NMV-LS on English and
    German datasets using well-known machine translation metrics such as BLUE, METEOR,
    ChrF++ and TER. Our results suggest that our approach achieves a verbalization
    performance close to that of humans and outperforms state of the art approaches.
    Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.
author:
- first_name: Abdullah Fathi Ahmed
  full_name: Ahmed, Abdullah Fathi Ahmed
  id: '29670'
  last_name: Ahmed
- first_name: Asep Fajar
  full_name: Firmansyah, Asep Fajar
  id: '76787'
  last_name: Firmansyah
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Ahmed AFA, Firmansyah AF, Sherif M, Moussallem D, Ngonga Ngomo A-C. Explainable
    Integration of Knowledge Graphs Using Large Language Models. In: <i>Natural Language
    Processing and Information Systems</i>. Springer Nature Switzerland; 2023. doi:<a
    href="https://doi.org/10.1007/978-3-031-35320-8_9">10.1007/978-3-031-35320-8_9</a>'
  apa: Ahmed, A. F. A., Firmansyah, A. F., Sherif, M., Moussallem, D., &#38; Ngonga
    Ngomo, A.-C. (2023). Explainable Integration of Knowledge Graphs Using Large Language
    Models. In <i>Natural Language Processing and Information Systems</i>. Springer
    Nature Switzerland. <a href="https://doi.org/10.1007/978-3-031-35320-8_9">https://doi.org/10.1007/978-3-031-35320-8_9</a>
  bibtex: '@inbook{Ahmed_Firmansyah_Sherif_Moussallem_Ngonga Ngomo_2023, place={Cham},
    title={Explainable Integration of Knowledge Graphs Using Large Language Models},
    DOI={<a href="https://doi.org/10.1007/978-3-031-35320-8_9">10.1007/978-3-031-35320-8_9</a>},
    booktitle={Natural Language Processing and Information Systems}, publisher={Springer
    Nature Switzerland}, author={Ahmed, Abdullah Fathi Ahmed and Firmansyah, Asep
    Fajar and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille},
    year={2023} }'
  chicago: 'Ahmed, Abdullah Fathi Ahmed, Asep Fajar Firmansyah, Mohamed Sherif, Diego
    Moussallem, and Axel-Cyrille Ngonga Ngomo. “Explainable Integration of Knowledge
    Graphs Using Large Language Models.” In <i>Natural Language Processing and Information
    Systems</i>. Cham: Springer Nature Switzerland, 2023. <a href="https://doi.org/10.1007/978-3-031-35320-8_9">https://doi.org/10.1007/978-3-031-35320-8_9</a>.'
  ieee: 'A. F. A. Ahmed, A. F. Firmansyah, M. Sherif, D. Moussallem, and A.-C. Ngonga
    Ngomo, “Explainable Integration of Knowledge Graphs Using Large Language Models,”
    in <i>Natural Language Processing and Information Systems</i>, Cham: Springer
    Nature Switzerland, 2023.'
  mla: Ahmed, Abdullah Fathi Ahmed, et al. “Explainable Integration of Knowledge Graphs
    Using Large Language Models.” <i>Natural Language Processing and Information Systems</i>,
    Springer Nature Switzerland, 2023, doi:<a href="https://doi.org/10.1007/978-3-031-35320-8_9">10.1007/978-3-031-35320-8_9</a>.
  short: 'A.F.A. Ahmed, A.F. Firmansyah, M. Sherif, D. Moussallem, A.-C. Ngonga Ngomo,
    in: Natural Language Processing and Information Systems, Springer Nature Switzerland,
    Cham, 2023.'
date_created: 2023-08-16T08:57:11Z
date_updated: 2024-06-04T12:23:45Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-031-35320-8_9
language:
- iso: eng
place: Cham
publication: Natural Language Processing and Information Systems
publication_identifier:
  isbn:
  - '9783031353192'
  - '9783031353208'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Explainable Integration of Knowledge Graphs Using Large Language Models
type: book_chapter
user_id: '76787'
year: '2023'
...
---
_id: '54616'
author:
- first_name: Alexander
  full_name: Becker, Alexander
  last_name: Becker
- first_name: Abdullah Fathi Ahmed
  full_name: Ahmed, Abdullah Fathi Ahmed
  id: '29670'
  last_name: Ahmed
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Becker A, Ahmed AFA, Sherif M, Ngonga Ngomo A-C. COBALT: A Content-Based Similarity
    Approach for Link Discovery over Geospatial Knowledge Graphs. In: <i>SEMANTiCS</i>.
    ; 2023.'
  apa: 'Becker, A., Ahmed, A. F. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2023).
    COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial
    Knowledge Graphs. <i>SEMANTiCS</i>.'
  bibtex: '@inproceedings{Becker_Ahmed_Sherif_Ngonga Ngomo_2023, title={COBALT: A
    Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge
    Graphs}, booktitle={SEMANTiCS}, author={Becker, Alexander and Ahmed, Abdullah
    Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
  chicago: 'Becker, Alexander, Abdullah Fathi Ahmed Ahmed, Mohamed Sherif, and Axel-Cyrille
    Ngonga Ngomo. “COBALT: A Content-Based Similarity Approach for Link Discovery
    over Geospatial Knowledge Graphs.” In <i>SEMANTiCS</i>, 2023.'
  ieee: 'A. Becker, A. F. A. Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “COBALT: A
    Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge
    Graphs,” 2023.'
  mla: 'Becker, Alexander, et al. “COBALT: A Content-Based Similarity Approach for
    Link Discovery over Geospatial Knowledge Graphs.” <i>SEMANTiCS</i>, 2023.'
  short: 'A. Becker, A.F.A. Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: SEMANTiCS, 2023.'
date_created: 2024-06-04T15:59:28Z
date_updated: 2024-06-04T15:59:50Z
department:
- _id: '574'
keyword:
- ahmed becker dice ngonga sail sherif
language:
- iso: eng
publication: SEMANTiCS
status: public
title: 'COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial
  Knowledge Graphs'
type: conference
user_id: '67199'
year: '2023'
...
---
_id: '46514'
abstract:
- lang: eng
  text: "Purpose: Data integration and applications across knowledge graphs (KGs)
    rely heavily on the discovery of links between resources within these KGs. Geospatial
    link discovery algorithms have to deal with millions of point sets containing
    billions of points. \r\nMethodology: To speed up the discovery of geospatial links,
    we propose COBALT. COBALT combines the content measures with R-tree indexing.
    The content measures are based on the area, diagonal and distance of the minimum
    bounding boxes of the polygons which speeds up the process but is not perfectly
    accurate. We thus propose two polygon splitting approaches for improving the accuracy
    of COBALT. \r\nFindings: Our experiments on real-world datasets show that COBALT
    is able to speed up the topological relation discovery over geospatial KGs by
    up to 1.47 × 104 times over state-of-the-art linking algorithms while maintaining
    an F-Measure between 0.7 and 0.9 depending on the relation. Furthermore, we were
    able to achieve an F-Measure of up to 0.99 by applying our polygon splitting approaches
    before applying the content measures. \r\nValue: The process of discovering links
    between geospatial resources can be significantly faster by sacrificing the optimality
    of the results. This is especially important for real time data-driven applications
    such as emergency response, location-based services and traffic management. In
    future work, additional measures, like the location of polygons or the name of
    the entity represented by the polygon, could be integrated to further improve
    the accuracy of the results."
author:
- first_name: Alexander
  full_name: Becker, Alexander
  last_name: Becker
- first_name: Abdullah Fathi Ahmed
  full_name: Ahmed, Abdullah Fathi Ahmed
  id: '29670'
  last_name: Ahmed
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Becker A, Ahmed AFA, Sherif M, Ngonga Ngomo A-C. COBALT: A Content-Based Similarity
    Approach for Link Discovery over Geospatial Knowledge Graphs. In: <i>SEMANTiCS</i>.
    ; 2023.'
  apa: 'Becker, A., Ahmed, A. F. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2023).
    COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial
    Knowledge Graphs. <i>SEMANTiCS</i>. SEMANTiCS 2023, Leipzig, Germany.'
  bibtex: '@inproceedings{Becker_Ahmed_Sherif_Ngonga Ngomo_2023, title={COBALT: A
    Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge
    Graphs}, booktitle={SEMANTiCS}, author={Becker, Alexander and Ahmed, Abdullah
    Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
  chicago: 'Becker, Alexander, Abdullah Fathi Ahmed Ahmed, Mohamed Sherif, and Axel-Cyrille
    Ngonga Ngomo. “COBALT: A Content-Based Similarity Approach for Link Discovery
    over Geospatial Knowledge Graphs.” In <i>SEMANTiCS</i>, 2023.'
  ieee: 'A. Becker, A. F. A. Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “COBALT: A
    Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge
    Graphs,” presented at the SEMANTiCS 2023, Leipzig, Germany, 2023.'
  mla: 'Becker, Alexander, et al. “COBALT: A Content-Based Similarity Approach for
    Link Discovery over Geospatial Knowledge Graphs.” <i>SEMANTiCS</i>, 2023.'
  short: 'A. Becker, A.F.A. Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: SEMANTiCS, 2023.'
conference:
  end_date: 2023-09-23
  location: Leipzig, Germany
  name: SEMANTiCS 2023
  start_date: 2023-09-20
date_created: 2023-08-16T08:55:23Z
date_updated: 2023-08-16T09:04:20Z
keyword:
- ahmed becker dice ngonga sail sherif
language:
- iso: eng
publication: SEMANTiCS
status: public
title: 'COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial
  Knowledge Graphs'
type: conference
user_id: '67234'
year: '2023'
...
---
_id: '45558'
abstract:
- lang: eng
  text: Graffiti is an urban phenomenon that is increasingly attracting the interest
    of the sciences. To the best of our knowledge, no suitable data corpora are available
    for systematic research until now. The Information System Graffiti in Germany
    project (Ingrid) closes this gap by dealing with graffiti image collections that
    have been made available to the project for public use. Within Ingrid, the graffiti
    images are collected, digitized and annotated. With this work, we aim to support
    the rapid access to a comprehensive data source on Ingrid targeted especially
    by researchers. In particular, we present IngridKG, an RDF knowledge graph of
    annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update
    IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our
    generation pipeline applies RDF data conversion, link discovery and data fusion
    approaches to the original data. The current version of IngridKG contains 460,640,154
    triples and is linked to 3 other knowledge graphs by over 200,000 links. In our
    use case studies, we demonstrate the usefulness of our knowledge graph for different
    applications.
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Ana Alexandra
  full_name: Morim da Silva, Ana Alexandra
  last_name: Morim da Silva
- first_name: Svetlana
  full_name: Pestryakova, Svetlana
  last_name: Pestryakova
- first_name: Abdullah Fathi Ahmed
  full_name: Ahmed, Abdullah Fathi Ahmed
  id: '29670'
  last_name: Ahmed
- first_name: Sven
  full_name: Niemann, Sven
  id: '6593'
  last_name: Niemann
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Sherif M, Morim da Silva AA, Pestryakova S, Ahmed AFA, Niemann S, Ngonga Ngomo
    A-C. <i>IngridKG: A FAIR Knowledge Graph of Graffiti</i>. LibreCat University;
    2023. doi:<a href="https://doi.org/10.5281/ZENODO.7560242">10.5281/ZENODO.7560242</a>'
  apa: 'Sherif, M., Morim da Silva, A. A., Pestryakova, S., Ahmed, A. F. A., Niemann,
    S., &#38; Ngonga Ngomo, A.-C. (2023). <i>IngridKG: A FAIR Knowledge Graph of Graffiti</i>.
    LibreCat University. <a href="https://doi.org/10.5281/ZENODO.7560242">https://doi.org/10.5281/ZENODO.7560242</a>'
  bibtex: '@book{Sherif_Morim da Silva_Pestryakova_Ahmed_Niemann_Ngonga Ngomo_2023,
    title={IngridKG: A FAIR Knowledge Graph of Graffiti}, DOI={<a href="https://doi.org/10.5281/ZENODO.7560242">10.5281/ZENODO.7560242</a>},
    publisher={LibreCat University}, author={Sherif, Mohamed and Morim da Silva, Ana
    Alexandra and Pestryakova, Svetlana and Ahmed, Abdullah Fathi Ahmed and Niemann,
    Sven and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
  chicago: 'Sherif, Mohamed, Ana Alexandra Morim da Silva, Svetlana Pestryakova, Abdullah
    Fathi Ahmed Ahmed, Sven Niemann, and Axel-Cyrille Ngonga Ngomo. <i>IngridKG: A
    FAIR Knowledge Graph of Graffiti</i>. LibreCat University, 2023. <a href="https://doi.org/10.5281/ZENODO.7560242">https://doi.org/10.5281/ZENODO.7560242</a>.'
  ieee: 'M. Sherif, A. A. Morim da Silva, S. Pestryakova, A. F. A. Ahmed, S. Niemann,
    and A.-C. Ngonga Ngomo, <i>IngridKG: A FAIR Knowledge Graph of Graffiti</i>. LibreCat
    University, 2023.'
  mla: 'Sherif, Mohamed, et al. <i>IngridKG: A FAIR Knowledge Graph of Graffiti</i>.
    LibreCat University, 2023, doi:<a href="https://doi.org/10.5281/ZENODO.7560242">10.5281/ZENODO.7560242</a>.'
  short: 'M. Sherif, A.A. Morim da Silva, S. Pestryakova, A.F.A. Ahmed, S. Niemann,
    A.-C. Ngonga Ngomo, IngridKG: A FAIR Knowledge Graph of Graffiti, LibreCat University,
    2023.'
date_created: 2023-06-09T10:09:34Z
date_updated: 2023-08-16T10:23:55Z
department:
- _id: '34'
doi: 10.5281/ZENODO.7560242
project:
- _id: '104'
  grant_number: '289287267'
  name: 'INGRID: INGRID: Informationssystem Graffiti in Deutschland'
publisher: LibreCat University
status: public
title: 'IngridKG: A FAIR Knowledge Graph of Graffiti'
type: research_data
user_id: '67234'
year: '2023'
...
---
_id: '29038'
abstract:
- lang: eng
  text: An increasing number of heterogeneous datasets abiding by the Linked Data
    paradigm is published everyday. Discovering links between these datasets is thus
    central to achieving the vision behind the Data Web. Declarative Link Discovery
    (LD) frameworks rely on complex Link Specification (LS) to express the conditions
    under which two resources should be linked. Complex LS combine similarity measures
    with thresholds to determine whether a given predicate holds between two resources.
    State of the art LD frameworks rely mostly on string-based similarity measures
    such as Levenshtein and Jaccard. However, string-based similarity measures often
    fail to catch the similarity of resources with phonetically similar property values
    when these property values are represented using different string representation
    (e.g., names and street labels). In this paper, we evaluate the impact of using
    phonetics-based similarities in the process of LD. Moreover, we evaluate the impact
    of phonetic-based similarity measures on a state-of-the-art machine learning approach
    used to generate LS. Our experiments suggest that the combination of string-based
    and phonetic-based measures can improve the Fmeasures achieved by LD frameworks
    on most datasets.
author:
- first_name: Abdullah Fathi Ahmed
  full_name: Ahmed, Abdullah Fathi Ahmed
  id: '29670'
  last_name: Ahmed
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Ahmed AFA, Sherif M, Ngonga Ngomo A-C. Do your Resources Sound Similar? On
    the Impact of Using Phonetic Similarity in Link Discovery. In: <i>K-CAP 2019:
    Knowledge Capture Conference</i>. ; 2019.'
  apa: 'Ahmed, A. F. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). Do your Resources
    Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery. <i>K-CAP
    2019: Knowledge Capture Conference</i>.'
  bibtex: '@inproceedings{Ahmed_Sherif_Ngonga Ngomo_2019, title={Do your Resources
    Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery},
    booktitle={K-CAP 2019: Knowledge Capture Conference}, author={Ahmed, Abdullah
    Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019} }'
  chicago: 'Ahmed, Abdullah Fathi Ahmed, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo.
    “Do Your Resources Sound Similar? On the Impact of Using Phonetic Similarity in
    Link Discovery.” In <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019.'
  ieee: A. F. A. Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “Do your Resources Sound
    Similar? On the Impact of Using Phonetic Similarity in Link Discovery,” 2019.
  mla: 'Ahmed, Abdullah Fathi Ahmed, et al. “Do Your Resources Sound Similar? On the
    Impact of Using Phonetic Similarity in Link Discovery.” <i>K-CAP 2019: Knowledge
    Capture Conference</i>, 2019.'
  short: 'A.F.A. Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge Capture
    Conference, 2019.'
date_created: 2021-12-17T10:05:09Z
date_updated: 2023-08-16T09:35:21Z
keyword:
- sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:ngonga ahmed sherif solide
  limboproject opal group_aksw dice
language:
- iso: eng
publication: 'K-CAP 2019: Knowledge Capture Conference'
status: public
title: Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity
  in Link Discovery
type: conference
user_id: '67234'
year: '2019'
...