---
_id: '29043'
abstract:
- lang: eng
  text: 'Social media plays a significant role in disaster management by providing
    valuable data about affected people, donations and help requests. Recent studies
    highlight the need to filter information on social media into fine-grained content
    labels. However, identifying useful information from massive amounts of social
    media posts during a crisis is a challenging task. In this paper, we propose I-AID,
    a multimodel approach to automatically categorize tweets into multi-label information
    types and filter critical information from the enormous volume of social media
    data. I-AID incorporates three main components: i) a BERT- based encoder to capture
    the semantics of a tweet and represent as a low-dimensional vector, ii) a graph
    attention network (GAT) to apprehend correlations between tweets’ words/entities
    and the corresponding information types, and iii) a Relation Network as a learnable
    distance metric to compute the similarity between tweets and their corresponding
    information types in a supervised way. We conducted several experiments on two
    real publicly-available datasets. Our results indicate that I-AID outperforms
    state-of- the-art approaches in terms of weighted average F1 score by +6% and
    +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Rricha
  full_name: Jalota, Rricha
  id: '69526'
  last_name: Jalota
- 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: 'Zahera HMA, Jalota R, Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable
    Information from Disaster-related Tweets. In: <i>IEEE Open Access</i>. ; 2021.'
  apa: 'Zahera, H. M. A., Jalota, R., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2021).
    I-AID: Identifying Actionable Information from Disaster-related Tweets. <i>IEEE
    Open Access</i>.'
  bibtex: '@inproceedings{Zahera_Jalota_Sherif_Ngonga Ngomo_2021, title={I-AID: Identifying
    Actionable Information from Disaster-related Tweets}, booktitle={IEEE Open Access},
    author={Zahera, Hamada Mohamed Abdelsamee and Jalota, Rricha and Sherif, Mohamed
    and Ngonga Ngomo, Axel-Cyrille}, year={2021} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Rricha Jalota, Mohamed Sherif, and
    Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information from Disaster-Related
    Tweets.” In <i>IEEE Open Access</i>, 2021.'
  ieee: 'H. M. A. Zahera, R. Jalota, M. Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying
    Actionable Information from Disaster-related Tweets,” 2021.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “I-AID: Identifying Actionable Information
    from Disaster-Related Tweets.” <i>IEEE Open Access</i>, 2021.'
  short: 'H.M.A. Zahera, R. Jalota, M. Sherif, A.-C. Ngonga Ngomo, in: IEEE Open Access,
    2021.'
date_created: 2021-12-17T10:06:30Z
date_updated: 2023-08-16T09:35:42Z
keyword:
- sys:relevantFor:infai sys:relevantFor:DAIKIRI ngonga zahera sherif daikiriproject
  dice simba
language:
- iso: eng
publication: IEEE Open Access
status: public
title: 'I-AID: Identifying Actionable Information from Disaster-related Tweets'
type: conference
user_id: '67234'
year: '2021'
...
---
_id: '29005'
abstract:
- lang: eng
  text: The number and size of datasets abiding by the Linked Data paradigm increase
    every day. 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. Understanding such LS is not a trivial task for non-expert users.
    Particularly when such users are interested in generating LS to match their needs.
    Even if the user applies a machine learning algorithm for the automatic generation
    of the required LS, the challenge of explaining the resultant LS persists. Hence,
    providing explainable LS is the key challenge to enable users who are unfamiliar
    with underlying LS technologies to use them effectively and efficiently. In this
    paper, we extend our previous work (Ahmed et al., 2019) by proposing a generic
    multilingual approach that allows verbalization of LS in many languages, i.e.,
    converts LS into understandable natural language text. In this work, we ported
    our LS verbalization framework into German and Spanish, in addition to English
    language. Our adequacy and fluency evaluations show that our approach can generate
    complete and easily understandable natural language descriptions even by lay users.
    Moreover, we devised an experimental neural approach for improving the quality
    of our generated texts. Our neural approach achieves promising results in terms
    of BLEU, METEOR and chrF++.
author:
- first_name: Abdullah
  full_name: Fathi Ahmed, Abdullah
  last_name: Fathi Ahmed
- 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: Fathi Ahmed A, Sherif M, Moussallem D, Ngonga Ngomo A-C. Multilingual Verbalization
    and Summarization for Explainable Link Discovery. <i>Data &#38; Knowledge Engineering</i>.
    Published online 2021:101874. doi:<a href="https://doi.org/10.1016/j.datak.2021.101874">https://doi.org/10.1016/j.datak.2021.101874</a>
  apa: Fathi Ahmed, A., Sherif, M., Moussallem, D., &#38; Ngonga Ngomo, A.-C. (2021).
    Multilingual Verbalization and Summarization for Explainable Link Discovery. <i>Data
    &#38; Knowledge Engineering</i>, 101874. <a href="https://doi.org/10.1016/j.datak.2021.101874">https://doi.org/10.1016/j.datak.2021.101874</a>
  bibtex: '@article{Fathi Ahmed_Sherif_Moussallem_Ngonga Ngomo_2021, title={Multilingual
    Verbalization and Summarization for Explainable Link Discovery}, DOI={<a href="https://doi.org/10.1016/j.datak.2021.101874">https://doi.org/10.1016/j.datak.2021.101874</a>},
    journal={Data &#38; Knowledge Engineering}, author={Fathi Ahmed, Abdullah and
    Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2021},
    pages={101874} }'
  chicago: Fathi Ahmed, Abdullah, Mohamed Sherif, Diego Moussallem, and Axel-Cyrille
    Ngonga Ngomo. “Multilingual Verbalization and Summarization for Explainable Link
    Discovery.” <i>Data &#38; Knowledge Engineering</i>, 2021, 101874. <a href="https://doi.org/10.1016/j.datak.2021.101874">https://doi.org/10.1016/j.datak.2021.101874</a>.
  ieee: 'A. Fathi Ahmed, M. Sherif, D. Moussallem, and A.-C. Ngonga Ngomo, “Multilingual
    Verbalization and Summarization for Explainable Link Discovery,” <i>Data &#38;
    Knowledge Engineering</i>, p. 101874, 2021, doi: <a href="https://doi.org/10.1016/j.datak.2021.101874">https://doi.org/10.1016/j.datak.2021.101874</a>.'
  mla: Fathi Ahmed, Abdullah, et al. “Multilingual Verbalization and Summarization
    for Explainable Link Discovery.” <i>Data &#38; Knowledge Engineering</i>, 2021,
    p. 101874, doi:<a href="https://doi.org/10.1016/j.datak.2021.101874">https://doi.org/10.1016/j.datak.2021.101874</a>.
  short: A. Fathi Ahmed, M. Sherif, D. Moussallem, A.-C. Ngonga Ngomo, Data &#38;
    Knowledge Engineering (2021) 101874.
date_created: 2021-12-17T09:51:15Z
date_updated: 2023-08-16T10:26:16Z
doi: https://doi.org/10.1016/j.datak.2021.101874
keyword:
- 2021 sys:relevantFor:infai simba sherif ngonga ahmed limes dice raki moussallem
  libo opal knowgraphs
language:
- iso: eng
page: '101874'
publication: Data & Knowledge Engineering
publication_identifier:
  issn:
  - 0169-023X
status: public
title: Multilingual Verbalization and Summarization for Explainable Link Discovery
type: journal_article
user_id: '67234'
year: '2021'
...
---
_id: '29044'
author:
- first_name: Jaydeep
  full_name: Chakraborty, Jaydeep
  last_name: Chakraborty
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Srividya
  full_name: Bansal, Srividya
  last_name: Bansal
citation:
  ama: 'Chakraborty J, Sherif M, Zahera HMA, Bansal S. OntoConnect: Domain-Agnostic
    Ontology Alignment using Graph Embedding with Negative Sampling. In: <i>Proceedings
    of the IEEE International Conference on Machine Learning and Applications</i>.
    ; 2021.'
  apa: 'Chakraborty, J., Sherif, M., Zahera, H. M. A., &#38; Bansal, S. (2021). OntoConnect:
    Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling.
    <i>Proceedings of the IEEE International Conference on Machine Learning and Applications</i>.'
  bibtex: '@inproceedings{Chakraborty_Sherif_Zahera_Bansal_2021, title={OntoConnect:
    Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling},
    booktitle={Proceedings of the IEEE International Conference on Machine Learning
    and Applications}, author={Chakraborty, Jaydeep and Sherif, Mohamed and Zahera,
    Hamada Mohamed Abdelsamee and Bansal, Srividya}, year={2021} }'
  chicago: 'Chakraborty, Jaydeep, Mohamed Sherif, Hamada Mohamed Abdelsamee Zahera,
    and Srividya Bansal. “OntoConnect: Domain-Agnostic Ontology Alignment Using Graph
    Embedding with Negative Sampling.” In <i>Proceedings of the IEEE International
    Conference on Machine Learning and Applications</i>, 2021.'
  ieee: 'J. Chakraborty, M. Sherif, H. M. A. Zahera, and S. Bansal, “OntoConnect:
    Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling,”
    2021.'
  mla: 'Chakraborty, Jaydeep, et al. “OntoConnect: Domain-Agnostic Ontology Alignment
    Using Graph Embedding with Negative Sampling.” <i>Proceedings of the IEEE International
    Conference on Machine Learning and Applications</i>, 2021.'
  short: 'J. Chakraborty, M. Sherif, H.M.A. Zahera, S. Bansal, in: Proceedings of
    the IEEE International Conference on Machine Learning and Applications, 2021.'
date_created: 2021-12-17T10:06:45Z
date_updated: 2023-08-16T10:25:55Z
keyword:
- dice sherif hamada
language:
- iso: eng
publication: Proceedings of the IEEE International Conference on Machine Learning
  and Applications
status: public
title: 'OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with
  Negative Sampling'
type: conference
user_id: '67234'
year: '2021'
...
---
_id: '29042'
author:
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Muhammad
  full_name: Saleem, Muhammad
  last_name: Saleem
- first_name: Felix
  full_name: Conrads, Felix
  last_name: Conrads
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Röder M, Sherif M, Saleem M, Conrads F, Ngonga Ngomo A-C. Benchmarking the
    Lifecycle of Knowledge Graphs. In: Tiddi I, Lécué F, Hitzler P, eds. <i>Knowledge
    Graphs for EXplainable Artificial Intelligence: Foundations, Applications and
    Challenges</i>. IOS Press; 2020:73-97. doi:<a href="https://doi.org/10.3233/SSW200012">10.3233/SSW200012</a>'
  apa: 'Röder, M., Sherif, M., Saleem, M., Conrads, F., &#38; Ngonga Ngomo, A.-C.
    (2020). Benchmarking the Lifecycle of Knowledge Graphs. In I. Tiddi, F. Lécué,
    &#38; P. Hitzler (Eds.), <i>Knowledge Graphs for eXplainable Artificial Intelligence:
    Foundations, Applications and Challenges</i> (pp. 73–97). IOS Press. <a href="https://doi.org/10.3233/SSW200012">https://doi.org/10.3233/SSW200012</a>'
  bibtex: '@inbook{Röder_Sherif_Saleem_Conrads_Ngonga Ngomo_2020, title={Benchmarking
    the Lifecycle of Knowledge Graphs}, DOI={<a href="https://doi.org/10.3233/SSW200012">10.3233/SSW200012</a>},
    booktitle={Knowledge Graphs for eXplainable Artificial Intelligence: Foundations,
    Applications and Challenges}, publisher={IOS Press}, author={Röder, Michael and
    Sherif, Mohamed and Saleem, Muhammad and Conrads, Felix and Ngonga Ngomo, Axel-Cyrille},
    editor={Tiddi, Ilaria and Lécué, Freddy and Hitzler, Pascal}, year={2020}, pages={73–97}
    }'
  chicago: 'Röder, Michael, Mohamed Sherif, Muhammad Saleem, Felix Conrads, and Axel-Cyrille
    Ngonga Ngomo. “Benchmarking the Lifecycle of Knowledge Graphs.” In <i>Knowledge
    Graphs for EXplainable Artificial Intelligence: Foundations, Applications and
    Challenges</i>, edited by Ilaria Tiddi, Freddy Lécué, and Pascal Hitzler, 73–97.
    IOS Press, 2020. <a href="https://doi.org/10.3233/SSW200012">https://doi.org/10.3233/SSW200012</a>.'
  ieee: 'M. Röder, M. Sherif, M. Saleem, F. Conrads, and A.-C. Ngonga Ngomo, “Benchmarking
    the Lifecycle of Knowledge Graphs,” in <i>Knowledge Graphs for eXplainable Artificial
    Intelligence: Foundations, Applications and Challenges</i>, I. Tiddi, F. Lécué,
    and P. Hitzler, Eds. IOS Press, 2020, pp. 73–97.'
  mla: 'Röder, Michael, et al. “Benchmarking the Lifecycle of Knowledge Graphs.” <i>Knowledge
    Graphs for EXplainable Artificial Intelligence: Foundations, Applications and
    Challenges</i>, edited by Ilaria Tiddi et al., IOS Press, 2020, pp. 73–97, doi:<a
    href="https://doi.org/10.3233/SSW200012">10.3233/SSW200012</a>.'
  short: 'M. Röder, M. Sherif, M. Saleem, F. Conrads, A.-C. Ngonga Ngomo, in: I. Tiddi,
    F. Lécué, P. Hitzler (Eds.), Knowledge Graphs for EXplainable Artificial Intelligence:
    Foundations, Applications and Challenges, IOS Press, 2020, pp. 73–97.'
date_created: 2021-12-17T10:06:12Z
date_updated: 2023-08-16T09:32:51Z
department:
- _id: '574'
doi: 10.3233/SSW200012
editor:
- first_name: Ilaria
  full_name: Tiddi, Ilaria
  last_name: Tiddi
- first_name: Freddy
  full_name: Lécué, Freddy
  last_name: Lécué
- first_name: Pascal
  full_name: Hitzler, Pascal
  last_name: Hitzler
keyword:
- dice group_aksw roeder sherif saleem fconrads ngonga
language:
- iso: eng
page: 73-97
publication: 'Knowledge Graphs for eXplainable Artificial Intelligence: Foundations,
  Applications and Challenges'
publisher: IOS Press
status: public
title: Benchmarking the Lifecycle of Knowledge Graphs
type: book_chapter
user_id: '67234'
year: '2020'
...
---
_id: '29009'
abstract:
- lang: eng
  text: With the growth in number and variety of RDF datasets comes an in- creasing
    need for both scalable and accurate solutions to support link discovery at instance
    level within and across these datasets. In contrast to ontology matching, most
    linking frameworks rely solely on string similarities to this end. The limited
    use of semantic similarities when linking instances is partly due to the current
    literature stating that they (1) do not improve the F-measure of instance linking
    approaches and (2) are impractical to use because they lack time efficiency. We
    revisit the combination of string and semantic similarities for linking instances.
    Contrary to the literature, our results suggest that this combination can improve
    the F-measure achieved by instance linking systems when the combination of the
    measures is performed by a machine learning approach. To achieve this in- sight,
    we had to address the scalability of semantic similarities. We hence present a
    framework for the rapid computation of semantic similarities based on edge counting.
    This runtime improvement allowed us to run an evaluation of 5 bench- mark datasets.
    Our results suggest that combining string and semantic similarities can improve
    the F-measure by up to 6% absolute.
author:
- first_name: Kleanthi
  full_name: Georgala, Kleanthi
  last_name: Georgala
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- 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: 'Georgala K, Röder M, Sherif M, Ngonga Ngomo A-C. Applying edge-counting semantic
    similarities to Link Discovery: Scalability and Accuracy. In: <i>Proceedings of
    Ontology Matching Workshop 2020</i>. ; 2020.'
  apa: 'Georgala, K., Röder, M., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2020). Applying
    edge-counting semantic similarities to Link Discovery: Scalability and Accuracy.
    <i>Proceedings of Ontology Matching Workshop 2020</i>.'
  bibtex: '@inproceedings{Georgala_Röder_Sherif_Ngonga Ngomo_2020, title={Applying
    edge-counting semantic similarities to Link Discovery: Scalability and Accuracy},
    booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Georgala,
    Kleanthi and Röder, Michael and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille},
    year={2020} }'
  chicago: 'Georgala, Kleanthi, Michael Röder, Mohamed Sherif, and Axel-Cyrille Ngonga
    Ngomo. “Applying Edge-Counting Semantic Similarities to Link Discovery: Scalability
    and Accuracy.” In <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.'
  ieee: 'K. Georgala, M. Röder, M. Sherif, and A.-C. Ngonga Ngomo, “Applying edge-counting
    semantic similarities to Link Discovery: Scalability and Accuracy,” 2020.'
  mla: 'Georgala, Kleanthi, et al. “Applying Edge-Counting Semantic Similarities to
    Link Discovery: Scalability and Accuracy.” <i>Proceedings of Ontology Matching
    Workshop 2020</i>, 2020.'
  short: 'K. Georgala, M. Röder, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of
    Ontology Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:53:49Z
date_updated: 2023-08-16T09:34:31Z
keyword:
- 2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject
  sys:relevantFor:infai sys:relevantFor:bis limes limbo opal roeder georgala
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: 'Applying edge-counting semantic similarities to Link Discovery: Scalability
  and Accuracy'
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29010'
abstract:
- lang: eng
  text: Link discovery plays a key role in the integration and use of data across
    RDF knowledge graphs. Active learning approaches are a common family of solutions
    to address the problem of learning how to compute links from users. So far, only
    active learning from perfect oracles has been considered in the literature. However,
    real oracles are often far from perfect (e.g., in crowdsourcing). We hence study
    the problem of learning how to compute links across knowledge graphs from noisy
    oracles, i.e., oracles that are not guaranteed to return correct classification
    results. We present a novel approach for link discovery based on a probabilistic
    model, with which we estimate the joint odds of the oracles’ guesses. We combine
    this approach with an iterative learning approach based on refinements. The resulting
    method, Ligon, is evaluated on 10 benchmark datasets. Our results suggest that
    Ligon configured with 10 iterations and 10 training examples per iteration achieves
    more than 95% of the F-measure achieved by state-of-the-art algorithms trained
    with a perfect oracle. Moreover, Ligon outperforms batch learning approaches devised
    to be trained with small amounts of training data by more than 40% F-measure on
    average.
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Kevin
  full_name: Dreßler}, Kevin
  last_name: Dreßler}
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Sherif M, Dreßler} K, Ngonga Ngomo A-C. LIGON – Link Discovery with Noisy
    Oracles. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.'
  apa: Sherif, M., Dreßler}, K., &#38; Ngonga Ngomo, A.-C. (2020). LIGON – Link Discovery
    with Noisy Oracles. <i>Proceedings of Ontology Matching Workshop 2020</i>.
  bibtex: '@inproceedings{Sherif_Dreßler}_Ngonga Ngomo_2020, title={LIGON – Link Discovery
    with Noisy Oracles}, booktitle={Proceedings of Ontology Matching Workshop 2020},
    author={Sherif, Mohamed and Dreßler}, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2020}
    }'
  chicago: Sherif, Mohamed, Kevin Dreßler}, and Axel-Cyrille Ngonga Ngomo. “LIGON
    – Link Discovery with Noisy Oracles.” In <i>Proceedings of Ontology Matching Workshop
    2020</i>, 2020.
  ieee: M. Sherif, K. Dreßler}, and A.-C. Ngonga Ngomo, “LIGON – Link Discovery with
    Noisy Oracles,” 2020.
  mla: Sherif, Mohamed, et al. “LIGON – Link Discovery with Noisy Oracles.” <i>Proceedings
    of Ontology Matching Workshop 2020</i>, 2020.
  short: 'M. Sherif, K. Dreßler}, A.-C. Ngonga Ngomo, in: Proceedings of Ontology
    Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:54:05Z
date_updated: 2023-08-16T09:34:11Z
keyword:
- 2020 dice simba sherif ligon ngonga knowgraphs sys:relevantFor:limboproject limboproject
  sys:relevantFor:infai sys:relevantFor:bis limes limbo opal kevin
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: LIGON – Link Discovery with Noisy Oracles
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29039'
author:
- first_name: Alexander
  full_name: Bigerl, Alexander
  id: '72857'
  last_name: Bigerl
- first_name: Felix
  full_name: Conrads, Felix
  last_name: Conrads
- first_name: Charlotte
  full_name: Behning, Charlotte
  last_name: Behning
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- 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: Bigerl A, Conrads F, Behning C, Sherif M, Saleem M, Ngonga Ngomo A-C. Tentris
    – A Tensor-Based Triple Store. <i>The Semantic Web -- ISWC 2020</i>. Published
    online 2020.
  apa: Bigerl, A., Conrads, F., Behning, C., Sherif, M., Saleem, M., &#38; Ngonga
    Ngomo, A.-C. (2020). Tentris – A Tensor-Based Triple Store. <i>The Semantic Web
    -- ISWC 2020</i>.
  bibtex: '@article{Bigerl_Conrads_Behning_Sherif_Saleem_Ngonga Ngomo_2020, title={Tentris
    – A Tensor-Based Triple Store}, journal={The Semantic Web -- ISWC 2020}, publisher={Springer
    International Publishing}, author={Bigerl, Alexander and Conrads, Felix and Behning,
    Charlotte and Sherif, Mohamed and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille},
    year={2020} }'
  chicago: Bigerl, Alexander, Felix Conrads, Charlotte Behning, Mohamed Sherif, Muhammad
    Saleem, and Axel-Cyrille Ngonga Ngomo. “Tentris – A Tensor-Based Triple Store.”
    <i>The Semantic Web -- ISWC 2020</i>, 2020.
  ieee: A. Bigerl, F. Conrads, C. Behning, M. Sherif, M. Saleem, and A.-C. Ngonga
    Ngomo, “Tentris – A Tensor-Based Triple Store,” <i>The Semantic Web -- ISWC 2020</i>,
    2020.
  mla: Bigerl, Alexander, et al. “Tentris – A Tensor-Based Triple Store.” <i>The Semantic
    Web -- ISWC 2020</i>, Springer International Publishing, 2020.
  short: A. Bigerl, F. Conrads, C. Behning, M. Sherif, M. Saleem, A.-C. Ngonga Ngomo,
    The Semantic Web -- ISWC 2020 (2020).
date_created: 2021-12-17T10:05:41Z
date_updated: 2023-08-16T10:06:33Z
keyword:
- sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba sys:relevantFor:limbo
  sys:relevantFor:raki daikiri speaker tentris knowgraphs bigerl fconrads saleem sherif
  ngonga group_aksw dice
language:
- iso: eng
publication: The Semantic Web -- ISWC 2020
publisher: Springer International Publishing
status: public
title: Tentris – A Tensor-Based Triple Store
type: journal_article
user_id: '67234'
year: '2020'
...
---
_id: '29040'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
citation:
  ama: 'Zahera HMA, Sherif M. ProBERT: Product Data Classification with Fine-tuning
    BERT Model. In: <i>Proceedings of Mining the Web of HTML-Embedded Product Data
    Workshop (MWPD2020)</i>. ; 2020.'
  apa: 'Zahera, H. M. A., &#38; Sherif, M. (2020). ProBERT: Product Data Classification
    with Fine-tuning BERT Model. <i>Proceedings of Mining the Web of HTML-Embedded
    Product Data Workshop (MWPD2020)</i>.'
  bibtex: '@inproceedings{Zahera_Sherif_2020, title={ProBERT: Product Data Classification
    with Fine-tuning BERT Model}, booktitle={Proceedings of Mining the Web of HTML-embedded
    Product Data Workshop (MWPD2020)}, author={Zahera, Hamada Mohamed Abdelsamee and
    Sherif, Mohamed}, year={2020} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product
    Data Classification with Fine-Tuning BERT Model.” In <i>Proceedings of Mining
    the Web of HTML-Embedded Product Data Workshop (MWPD2020)</i>, 2020.'
  ieee: 'H. M. A. Zahera and M. Sherif, “ProBERT: Product Data Classification with
    Fine-tuning BERT Model,” 2020.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product Data
    Classification with Fine-Tuning BERT Model.” <i>Proceedings of Mining the Web
    of HTML-Embedded Product Data Workshop (MWPD2020)</i>, 2020.'
  short: 'H.M.A. Zahera, M. Sherif, in: Proceedings of Mining the Web of HTML-Embedded
    Product Data Workshop (MWPD2020), 2020.'
date_created: 2021-12-17T10:05:42Z
date_updated: 2023-08-16T10:06:10Z
keyword:
- 2020 dice zahera sherif knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai
  sys:relevantFor:bis limes limbo opal
language:
- iso: eng
publication: Proceedings of Mining the Web of HTML-embedded Product Data Workshop
  (MWPD2020)
status: public
title: 'ProBERT: Product Data Classification with Fine-tuning BERT Model'
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29007'
abstract:
- lang: eng
  text: Modern data-driven frameworks often have to process large amounts of data
    periodically. Hence, they often operate under time or space constraints. This
    also holds for Linked Data-driven frameworks when processing RDF data, in particular,
    when they perform link discovery tasks. In this work, we present a novel approach
    for link discovery under constraints pertaining to the expected recall of a link
    discovery task. Given a link specification, the approach aims to find a subsumed
    link specification that achieves a lower run time than the input specification
    while abiding by a predefined constraint on the expected recall it has to achieve.
    Our approach, dubbed LIGER, combines downward refinement oper- ators with monotonicity
    assumptions to detect such specifications. We evaluate our approach on seven datasets.
    Our results suggest that the different implemen- tations of LIGER can detect subsumed
    specifications that abide by expected recall constraints efficiently, thus leading
    to significantly shorter overall run times than our baseline.
author:
- first_name: Kleanthi
  full_name: Georgala, Kleanthi
  last_name: Georgala
- 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: 'Georgala K, Sherif M, Ngonga Ngomo A-C. LIGER – Link Discovery with Partial
    Recall. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.'
  apa: Georgala, K., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2020). LIGER – Link Discovery
    with Partial Recall. <i>Proceedings of Ontology Matching Workshop 2020</i>.
  bibtex: '@inproceedings{Georgala_Sherif_Ngonga Ngomo_2020, title={LIGER – Link Discovery
    with Partial Recall}, booktitle={Proceedings of Ontology Matching Workshop 2020},
    author={Georgala, Kleanthi and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille},
    year={2020} }'
  chicago: Georgala, Kleanthi, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “LIGER
    – Link Discovery with Partial Recall.” In <i>Proceedings of Ontology Matching
    Workshop 2020</i>, 2020.
  ieee: K. Georgala, M. Sherif, and A.-C. Ngonga Ngomo, “LIGER – Link Discovery with
    Partial Recall,” 2020.
  mla: Georgala, Kleanthi, et al. “LIGER – Link Discovery with Partial Recall.” <i>Proceedings
    of Ontology Matching Workshop 2020</i>, 2020.
  short: 'K. Georgala, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology
    Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:53:07Z
date_updated: 2023-08-16T10:27:11Z
keyword:
- 2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject
  sys:relevantFor:infai sys:relevantFor:bis limes limbo opal georgala
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: LIGER – Link Discovery with Partial Recall
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29037'
abstract:
- lang: eng
  text: Existing technologies employ different machine learning approaches to predict
    disasters from historical environmental data. However, for short-term disasters
    (e.g., earthquakes), historical data alone has a limited prediction capability.
    In this work, we consider social media as a supplementary source of knowledge
    in addition to historical environmental data. Further, we build a joint model
    that learns from disaster-related tweets and environmental data to improve prediction.
    We propose the combination of semantically-enriched word embedding to represent
    entities in tweets with their semantics representations computed with the traditional
    word2vec. Our experiments show that our proposed approach outperforms the accuracy
    of state-of-the-art models in disaster prediction.
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- 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: 'Zahera HMA, Sherif M, Ngonga Ngomo A-C. Jointly Learning from Social Media
    and Environmental Data for Typhoon Intensity Prediction. In: <i>K-CAP 2019: Knowledge
    Capture Conference</i>. ; 2019:4.'
  apa: 'Zahera, H. M. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). Jointly Learning
    from Social Media and Environmental Data for Typhoon Intensity Prediction. <i>K-CAP
    2019: Knowledge Capture Conference</i>, 4.'
  bibtex: '@inproceedings{Zahera_Sherif_Ngonga Ngomo_2019, title={Jointly Learning
    from Social Media and Environmental Data for Typhoon Intensity Prediction}, booktitle={K-CAP
    2019: Knowledge Capture Conference}, author={Zahera, Hamada Mohamed Abdelsamee
    and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019}, pages={4} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Mohamed Sherif, and Axel-Cyrille Ngonga
    Ngomo. “Jointly Learning from Social Media and Environmental Data for Typhoon
    Intensity Prediction.” In <i>K-CAP 2019: Knowledge Capture Conference</i>, 4,
    2019.'
  ieee: 'H. M. A. Zahera, M. Sherif, and A.-C. Ngonga Ngomo, “Jointly Learning from
    Social Media and Environmental Data for Typhoon Intensity Prediction,” in <i>K-CAP
    2019: Knowledge Capture Conference</i>, 2019, p. 4.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “Jointly Learning from Social Media
    and Environmental Data for Typhoon Intensity Prediction.” <i>K-CAP 2019: Knowledge
    Capture Conference</i>, 2019, p. 4.'
  short: 'H.M.A. Zahera, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge
    Capture Conference, 2019, p. 4.'
date_created: 2021-12-17T10:05:07Z
date_updated: 2023-08-16T09:24:21Z
keyword:
- sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera
  sherif solide limboproject opal group\_aksw dice
language:
- iso: eng
page: '4'
publication: 'K-CAP 2019: Knowledge Capture Conference'
status: public
title: Jointly Learning from Social Media and Environmental Data for Typhoon Intensity
  Prediction
type: conference
user_id: '67234'
year: '2019'
...
---
_id: '29011'
abstract:
- lang: eng
  text: In this paper we present LimesWebUI, our web interface of Limes. Limes, the
    Link Discovery Framework for Metric Spaces, is a framework for dis- covering links
    between entities contained in Linked Data sources. LimesWebUI assists the end
    user during the link discovery process. By representing the link specifications
    (LS) as interlocking blocks, our interface eases the manual creation of links
    for users who already know which LS they would like to execute. How- ever, most
    users do not know which LS suits their linking task best and therefore need help
    throughout this process. Hence, our interface provides wizards which allow the
    easy configuration of many link discovery machine learning algorithms, that does
    not require the user to enter a manual LS. We evaluate the usability of the interface
    by using the standard system usability scale questionnaire. Our over- all usability
    score of 76.5 suggests that the online interface is consistent, easy to use, and
    the various functions of the system are well integrated.
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Svetlana
  full_name: Pestryakova, Svetlana
  last_name: Pestryakova
- first_name: Kevin
  full_name: Dreßler, Kevin
  id: '78256'
  last_name: Dreßler
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Sherif M, Pestryakova S, Dreßler K, Ngonga Ngomo A-C. LimesWebUI – Link Discovery
    Made Simple. In: <i>18th International Semantic Web Conference (ISWC 2019)</i>.
    CEUR-WS.org; 2019.'
  apa: Sherif, M., Pestryakova, S., Dreßler, K., &#38; Ngonga Ngomo, A.-C. (2019).
    LimesWebUI – Link Discovery Made Simple. <i>18th International Semantic Web Conference
    (ISWC 2019)</i>.
  bibtex: '@inproceedings{Sherif_Pestryakova_Dreßler_Ngonga Ngomo_2019, title={LimesWebUI
    – Link Discovery Made Simple}, booktitle={18th International Semantic Web Conference
    (ISWC 2019)}, publisher={CEUR-WS.org}, author={Sherif, Mohamed and Pestryakova,
    Svetlana and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2019} }'
  chicago: Sherif, Mohamed, Svetlana Pestryakova, Kevin Dreßler, and Axel-Cyrille
    Ngonga Ngomo. “LimesWebUI – Link Discovery Made Simple.” In <i>18th International
    Semantic Web Conference (ISWC 2019)</i>. CEUR-WS.org, 2019.
  ieee: M. Sherif, S. Pestryakova, K. Dreßler, and A.-C. Ngonga Ngomo, “LimesWebUI
    – Link Discovery Made Simple,” 2019.
  mla: Sherif, Mohamed, et al. “LimesWebUI – Link Discovery Made Simple.” <i>18th
    International Semantic Web Conference (ISWC 2019)</i>, CEUR-WS.org, 2019.
  short: 'M. Sherif, S. Pestryakova, K. Dreßler, A.-C. Ngonga Ngomo, in: 18th International
    Semantic Web Conference (ISWC 2019), CEUR-WS.org, 2019.'
date_created: 2021-12-17T09:54:17Z
date_updated: 2023-08-16T09:25:11Z
keyword:
- 2019 sys:relevantFor:infai group\_aksw simba sherif kevin ngonga Svetlana slipo
  limes dice sage limbo opal
language:
- iso: eng
publication: 18th International Semantic Web Conference (ISWC 2019)
publisher: CEUR-WS.org
status: public
title: LimesWebUI – Link Discovery Made Simple
type: conference
user_id: '67234'
year: '2019'
...
---
_id: '29003'
abstract:
- lang: eng
  text: In this paper, we describe our approach to classify disaster-related tweets
    into multilabel information types (ie, labels). We aim to filter first relevant
    tweets during disasters. Then, we assign tweets relevant information types. Information
    types can be SearchAndRescue, MovePeople and Volunteer. We employ a fine-tuned
    BERT model with 10 BERT layers. Further, we submitted our approach to the TREC-IS
    2019 challenge, the evaluation results showed that our approach outperforms the
    F1-score of median score in identifying actionable information.
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Ibrahim
  full_name: A. Elgendy, Ibrahim
  last_name: A. Elgendy
- first_name: Rricha
  full_name: Jalota, Rricha
  id: '69526'
  last_name: Jalota
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
citation:
  ama: 'Zahera HMA, A. Elgendy I, Jalota R, Sherif M. Fine-tuned BERT Model for Multi-Label
    Tweets Classification. In: <i>Proceedings of the Twenty-Eighth Text REtrieval
    Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>.
    ; 2019.'
  apa: Zahera, H. M. A., A. Elgendy, I., Jalota, R., &#38; Sherif, M. (2019). Fine-tuned
    BERT Model for Multi-Label Tweets Classification. <i>Proceedings of the Twenty-Eighth
    Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November
    13-15, 2019</i>.
  bibtex: '@inproceedings{Zahera_A. Elgendy_Jalota_Sherif_2019, title={Fine-tuned
    BERT Model for Multi-Label Tweets Classification}, booktitle={Proceedings of the
    Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland,
    USA, November 13-15, 2019}, author={Zahera, Hamada Mohamed Abdelsamee and A. Elgendy,
    Ibrahim and Jalota, Rricha and Sherif, Mohamed}, year={2019} }'
  chicago: Zahera, Hamada Mohamed Abdelsamee, Ibrahim A. Elgendy, Rricha Jalota, and
    Mohamed Sherif. “Fine-Tuned BERT Model for Multi-Label Tweets Classification.”
    In <i>Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019,
    Gaithersburg, Maryland, USA, November 13-15, 2019</i>, 2019.
  ieee: H. M. A. Zahera, I. A. Elgendy, R. Jalota, and M. Sherif, “Fine-tuned BERT
    Model for Multi-Label Tweets Classification,” 2019.
  mla: Zahera, Hamada Mohamed Abdelsamee, et al. “Fine-Tuned BERT Model for Multi-Label
    Tweets Classification.” <i>Proceedings of the Twenty-Eighth Text REtrieval Conference,
    {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>, 2019.
  short: 'H.M.A. Zahera, I. A. Elgendy, R. Jalota, M. Sherif, in: Proceedings of the
    Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland,
    USA, November 13-15, 2019, 2019.'
date_created: 2021-12-17T09:48:17Z
date_updated: 2023-08-16T09:25:34Z
keyword:
- zahera elgendy jalota sherif dice
language:
- iso: eng
publication: Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019,
  Gaithersburg, Maryland, USA, November 13-15, 2019
status: public
title: Fine-tuned BERT Model for Multi-Label Tweets Classification
type: conference
user_id: '67234'
year: '2019'
...
---
_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'
...
---
_id: '29012'
abstract:
- lang: eng
  text: An increasing number and size of datasets abiding by the Linked Data paradigm
    are published everyday. Discovering links between these datasets is thus central
    to achieve 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. Understanding such LS is not a trivial task for
    non-expert users, particularly when such users are interested in generating LS
    to match their needs. Even if the user applies a machine learning algorithm for
    the automatic generation of the required LS, the challenge of explaining the resultant
    LS persists. Hence, providing explainable LS is the key challenge to enable users
    who are unfamiliar with underlying LS technologies to use them effectively and
    efficiently. In this paper, we address this problem by proposing a generic approach
    that allows a LS to be verbalized, i.e., converted into understandable natural
    language. We propose a summarization approach to the verbalized LS based on the
    selectivity of the underlying LS. Our adequacy and fluency evaluations show that
    our approach can generate complete and easily understandable natural language
    descriptions even by lay users.
author:
- first_name: 'Abdullah '
  full_name: 'Fathi Ahmed, Abdullah '
  last_name: Fathi 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: 'Fathi Ahmed A, Sherif M, Ngonga Ngomo A-C. LSVS: Link Specification Verbalization
    and Summarization. In: <i>24th International Conference on Applications of Natural
    Language to Information Systems (NLDB 2019)</i>. Springer; 2019.'
  apa: 'Fathi Ahmed, A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). LSVS: Link
    Specification Verbalization and Summarization. <i>24th International Conference
    on Applications of Natural Language to Information Systems (NLDB 2019)</i>.'
  bibtex: '@inproceedings{Fathi Ahmed_Sherif_Ngonga Ngomo_2019, title={LSVS: Link
    Specification Verbalization and Summarization}, booktitle={24th International
    Conference on Applications of Natural Language to Information Systems (NLDB 2019)},
    publisher={Springer}, author={Fathi Ahmed, Abdullah  and Sherif, Mohamed and Ngonga
    Ngomo, Axel-Cyrille}, year={2019} }'
  chicago: 'Fathi Ahmed, Abdullah , Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo.
    “LSVS: Link Specification Verbalization and Summarization.” In <i>24th International
    Conference on Applications of Natural Language to Information Systems (NLDB 2019)</i>.
    Springer, 2019.'
  ieee: 'A. Fathi Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “LSVS: Link Specification
    Verbalization and Summarization,” 2019.'
  mla: 'Fathi Ahmed, Abdullah, et al. “LSVS: Link Specification Verbalization and
    Summarization.” <i>24th International Conference on Applications of Natural Language
    to Information Systems (NLDB 2019)</i>, Springer, 2019.'
  short: 'A. Fathi Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: 24th International Conference
    on Applications of Natural Language to Information Systems (NLDB 2019), Springer,
    2019.'
date_created: 2021-12-17T09:54:40Z
date_updated: 2023-08-16T10:06:20Z
keyword:
- 2019 sys:relevantFor:infai group\_aksw simba sherif ngonga ahmed slipo limes dice
  sage limbo opal
language:
- iso: eng
publication: 24th International Conference on Applications of Natural Language to
  Information Systems (NLDB 2019)
publisher: Springer
status: public
title: 'LSVS: Link Specification Verbalization and Summarization'
type: conference
user_id: '67234'
year: '2019'
...
---
_id: '29013'
abstract:
- lang: eng
  text: Point of Interest (POI) data constitute the cornerstone of any application,
    service or product even remotely related to our physical surroundings. From navigation
    applications to social networks, tourism, and logistics, we use POI data to search,
    communicate, decide and plan our actions. POIs are semantically diverse and spatio-temporally
    evolving entities, having geographical, temporal and thematic relations. Currently,
    integrating POI data to increase their coverage, timeliness, accuracy and value
    is a resource-intensive and mostly manual process, with no specialized software
    available to address the specific challenges of this task. In this paper, we present
    an integrated toolkit for transforming, linking, fusing and enriching POI data,
    and extracting additional value from them. In particular, we demonstrate how Linked
    Data technologies can address the limitations, gaps and challenges of the current
    landscape in Big POI data integration. We have built a prototype application that
    enables users to define, manage and execute scalable POI data integration workflows
    built on top of state-of-the-art software for geospatial Linked Data. The application
    abstracts and hides away the underlying complexity, automates quality-assured
    integration, scales efficiently for world-scale integration tasks and lowers the
    entry barrier for end-users. Validated against real-world POI datasets in several
    application domains, our system has shown great potential to address the requirements
    and needs of cross-sector, cross-border and cross-lingual integration of Big POI
    data.
author:
- first_name: Spiros
  full_name: Athanasiou, Spiros
  last_name: Athanasiou
- first_name: Giannopoulos
  full_name: Giorgos, Giannopoulos
  last_name: Giorgos
- first_name: Graux
  full_name: Damien, Graux
  last_name: Damien
- first_name: Karagiannakis
  full_name: Nikos, Karagiannakis
  last_name: Nikos
- first_name: Lehmann
  full_name: Jens, Lehmann
  last_name: Jens
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
- first_name: Patroumpas
  full_name: Kostas, Patroumpas
  last_name: Kostas
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Dimitrios
  full_name: Skoutas, Dimitrios
  last_name: Skoutas
citation:
  ama: 'Athanasiou S, Giorgos G, Damien G, et al. Big POI data integration with Linked
    Data technologies. In: <i>International Conference on Extending Database Technology
    2019, EDBT19</i>. ; 2019.'
  apa: Athanasiou, S., Giorgos, G., Damien, G., Nikos, K., Jens, L., Ngonga Ngomo,
    A.-C., Kostas, P., Sherif, M., &#38; Skoutas, D. (2019). Big POI data integration
    with Linked Data technologies. <i>International Conference on Extending Database
    Technology 2019, EDBT19</i>.
  bibtex: '@inproceedings{Athanasiou_Giorgos_Damien_Nikos_Jens_Ngonga Ngomo_Kostas_Sherif_Skoutas_2019,
    title={Big POI data integration with Linked Data technologies}, booktitle={International
    Conference on Extending Database Technology 2019, EDBT19}, author={Athanasiou,
    Spiros and Giorgos, Giannopoulos and Damien, Graux and Nikos, Karagiannakis and
    Jens, Lehmann and Ngonga Ngomo, Axel-Cyrille and Kostas, Patroumpas and Sherif,
    Mohamed and Skoutas, Dimitrios}, year={2019} }'
  chicago: Athanasiou, Spiros, Giannopoulos Giorgos, Graux Damien, Karagiannakis Nikos,
    Lehmann Jens, Axel-Cyrille Ngonga Ngomo, Patroumpas Kostas, Mohamed Sherif, and
    Dimitrios Skoutas. “Big POI Data Integration with Linked Data Technologies.” In
    <i>International Conference on Extending Database Technology 2019, EDBT19</i>,
    2019.
  ieee: S. Athanasiou <i>et al.</i>, “Big POI data integration with Linked Data technologies,”
    2019.
  mla: Athanasiou, Spiros, et al. “Big POI Data Integration with Linked Data Technologies.”
    <i>International Conference on Extending Database Technology 2019, EDBT19</i>,
    2019.
  short: 'S. Athanasiou, G. Giorgos, G. Damien, K. Nikos, L. Jens, A.-C. Ngonga Ngomo,
    P. Kostas, M. Sherif, D. Skoutas, in: International Conference on Extending Database
    Technology 2019, EDBT19, 2019.'
date_created: 2021-12-17T09:54:54Z
date_updated: 2023-08-16T10:29:26Z
keyword:
- 2019 sys:relevantFor:infai group\_aksw simba sherif ngonga lehmann slipo limes dice
  deer
language:
- iso: eng
publication: International Conference on Extending Database Technology 2019, EDBT19
status: public
title: Big POI data integration with Linked Data technologies
type: conference
user_id: '67234'
year: '2019'
...
---
_id: '29029'
author:
- first_name: Abdullah
  full_name: Fathi Ahmed, Abdullah
  last_name: Fathi Ahmed
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Fathi Ahmed A, Sherif M, Ngonga Ngomo A-C. RADON2: A buffered-Intersection
    Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge
    Bases (OAEI2018 Results). In: <i>Proceedings of Ontology Matching Workshop 2018</i>.
    ; 2018.'
  apa: 'Fathi Ahmed, A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2018). RADON2: A buffered-Intersection
    Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge
    Bases (OAEI2018 Results). <i>Proceedings of Ontology Matching Workshop 2018</i>.'
  bibtex: '@inproceedings{Fathi Ahmed_Sherif_Ngonga Ngomo_2018, title={RADON2: A buffered-Intersection
    Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge
    Bases (OAEI2018 Results)}, booktitle={Proceedings of Ontology Matching Workshop
    2018}, author={Fathi Ahmed, Abdullah and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille},
    year={2018} }'
  chicago: 'Fathi Ahmed, Abdullah, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo.
    “RADON2: A Buffered-Intersection Matrix Computing Approach To Accelerate Link
    Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results).” In <i>Proceedings
    of Ontology Matching Workshop 2018</i>, 2018.'
  ieee: 'A. Fathi Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “RADON2: A buffered-Intersection
    Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge
    Bases (OAEI2018 Results),” 2018.'
  mla: 'Fathi Ahmed, Abdullah, et al. “RADON2: A Buffered-Intersection Matrix Computing
    Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018
    Results).” <i>Proceedings of Ontology Matching Workshop 2018</i>, 2018.'
  short: 'A. Fathi Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology
    Matching Workshop 2018, 2018.'
date_created: 2021-12-17T09:59:59Z
date_updated: 2022-04-05T10:28:29Z
keyword:
- 2018 simba dice radon abdullah sherif ngonga slipo sage geiser hobbit group\_aksw
  sys:relevantFor:infai sys:relevantFor:bis limes linkinglod sake diesel sys:relevantFor:leds
  leds
language:
- iso: eng
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of Ontology Matching Workshop 2018
status: public
title: 'RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link
  Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results)'
type: conference
user_id: '67234'
year: '2018'
...
---
_id: '29021'
author:
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- 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: Esteves, Diego
  last_name: Esteves
- first_name: Marcos
  full_name: Zampieri, Marcos
  last_name: Zampieri
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Moussallem D, Sherif M, Esteves D, Zampieri M, Ngonga Ngomo A-C. LIdioms:
    A Multilingual Linked Idioms Data Set. In: <i>The 11th Edition of the Language
    Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)</i>. ; 2018.'
  apa: 'Moussallem, D., Sherif, M., Esteves, D., Zampieri, M., &#38; Ngonga Ngomo,
    A.-C. (2018). LIdioms: A Multilingual Linked Idioms Data Set. <i>The 11th Edition
    of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)</i>.'
  bibtex: '@inproceedings{Moussallem_Sherif_Esteves_Zampieri_Ngonga Ngomo_2018, title={LIdioms:
    A Multilingual Linked Idioms Data Set}, booktitle={The 11th edition of the Language
    Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)}, author={Moussallem,
    Diego and Sherif, Mohamed and Esteves, Diego and Zampieri, Marcos and Ngonga Ngomo,
    Axel-Cyrille}, year={2018} }'
  chicago: 'Moussallem, Diego, Mohamed Sherif, Diego Esteves, Marcos Zampieri, and
    Axel-Cyrille Ngonga Ngomo. “LIdioms: A Multilingual Linked Idioms Data Set.” In
    <i>The 11th Edition of the Language Resources and Evaluation Conference, 7-12
    May 2018, Miyazaki (Japan)</i>, 2018.'
  ieee: 'D. Moussallem, M. Sherif, D. Esteves, M. Zampieri, and A.-C. Ngonga Ngomo,
    “LIdioms: A Multilingual Linked Idioms Data Set,” 2018.'
  mla: 'Moussallem, Diego, et al. “LIdioms: A Multilingual Linked Idioms Data Set.”
    <i>The 11th Edition of the Language Resources and Evaluation Conference, 7-12
    May 2018, Miyazaki (Japan)</i>, 2018.'
  short: 'D. Moussallem, M. Sherif, D. Esteves, M. Zampieri, A.-C. Ngonga Ngomo, in:
    The 11th Edition of the Language Resources and Evaluation Conference, 7-12 May
    2018, Miyazaki (Japan), 2018.'
date_created: 2021-12-17T09:57:23Z
date_updated: 2023-08-16T09:33:54Z
keyword:
- lidiom sys:relevantFor:infai sys:relevantFor:bis group\_aksw sherif simba dice moussallem
  esteves ngonga slipo sage projecthobbit geiser diesel simba
language:
- iso: eng
publication: The 11th edition of the Language Resources and Evaluation Conference,
  7-12 May 2018, Miyazaki (Japan)
status: public
title: 'LIdioms: A Multilingual Linked Idioms Data Set'
type: conference
user_id: '67234'
year: '2018'
...
---
_id: '46539'
abstract:
- lang: eng
  text: This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign.
    Like in 2012, when we transitioned the evaluation to the SEALS platform, we have
    also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT
    platform. We report the experiences of this precampaign and discuss the future
    steps for the OAEI.
author:
- first_name: Ernesto
  full_name: Jiménez-Ruiz, Ernesto
  last_name: Jiménez-Ruiz
- first_name: Tzanina
  full_name: Saveta, Tzanina
  last_name: Saveta
- first_name: Ondrej
  full_name: Zamazal, Ondrej
  last_name: Zamazal
- first_name: Sven
  full_name: Hertling, Sven
  last_name: Hertling
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- first_name: Irini
  full_name: Fundulaki, Irini
  last_name: Fundulaki
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Amina
  full_name: Annane, Amina
  last_name: Annane
- first_name: Zohra
  full_name: Bellahsene, Zohra
  last_name: Bellahsene
- first_name: Sadok Ben
  full_name: Yahia, Sadok Ben
  last_name: Yahia
- first_name: Gayo
  full_name: Diallo, Gayo
  last_name: Diallo
- first_name: Daniel
  full_name: Faria, Daniel
  last_name: Faria
- first_name: Marouen
  full_name: Kachroudi, Marouen
  last_name: Kachroudi
- first_name: Abderrahmane
  full_name: Khiat, Abderrahmane
  last_name: Khiat
- first_name: Patrick
  full_name: Lambrix, Patrick
  last_name: Lambrix
- first_name: Huanyu
  full_name: Li, Huanyu
  last_name: Li
- first_name: Maximilian
  full_name: Mackeprang, Maximilian
  last_name: Mackeprang
- first_name: Majid
  full_name: Mohammadi, Majid
  last_name: Mohammadi
- first_name: Maciej
  full_name: Rybinski, Maciej
  last_name: Rybinski
- first_name: Booma Sowkarthiga
  full_name: Balasubramani, Booma Sowkarthiga
  last_name: Balasubramani
- first_name: Cassia
  full_name: Trojahn, Cassia
  last_name: Trojahn
citation:
  ama: 'Jiménez-Ruiz E, Saveta T, Zamazal O, et al. Introducing the HOBBIT platform
    into the Ontology Alignment Evaluation Campaign. In: <i>Proceedings of the Ontology
    Matching Workshop 2018</i>. ; 2018.'
  apa: Jiménez-Ruiz, E., Saveta, T., Zamazal, O., Hertling, S., Röder, M., Fundulaki,
    I., Ngonga Ngomo, A.-C., Sherif, M., Annane, A., Bellahsene, Z., Yahia, S. B.,
    Diallo, G., Faria, D., Kachroudi, M., Khiat, A., Lambrix, P., Li, H., Mackeprang,
    M., Mohammadi, M., … Trojahn, C. (2018). Introducing the HOBBIT platform into
    the Ontology Alignment Evaluation Campaign. <i>Proceedings of the Ontology Matching
    Workshop 2018</i>.
  bibtex: '@inproceedings{Jiménez-Ruiz_Saveta_Zamazal_Hertling_Röder_Fundulaki_Ngonga
    Ngomo_Sherif_Annane_Bellahsene_et al._2018, title={Introducing the HOBBIT platform
    into the Ontology Alignment Evaluation Campaign}, booktitle={Proceedings of the
    Ontology Matching Workshop 2018}, author={Jiménez-Ruiz, Ernesto and Saveta, Tzanina
    and Zamazal, Ondrej and Hertling, Sven and Röder, Michael and Fundulaki, Irini
    and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Annane, Amina and Bellahsene,
    Zohra and et al.}, year={2018} }'
  chicago: Jiménez-Ruiz, Ernesto, Tzanina Saveta, Ondrej Zamazal, Sven Hertling, Michael
    Röder, Irini Fundulaki, Axel-Cyrille Ngonga Ngomo, et al. “Introducing the HOBBIT
    Platform into the Ontology Alignment Evaluation Campaign.” In <i>Proceedings of
    the Ontology Matching Workshop 2018</i>, 2018.
  ieee: E. Jiménez-Ruiz <i>et al.</i>, “Introducing the HOBBIT platform into the Ontology
    Alignment Evaluation Campaign,” 2018.
  mla: Jiménez-Ruiz, Ernesto, et al. “Introducing the HOBBIT Platform into the Ontology
    Alignment Evaluation Campaign.” <i>Proceedings of the Ontology Matching Workshop
    2018</i>, 2018.
  short: 'E. Jiménez-Ruiz, T. Saveta, O. Zamazal, S. Hertling, M. Röder, I. Fundulaki,
    A.-C. Ngonga Ngomo, M. Sherif, A. Annane, Z. Bellahsene, S.B. Yahia, G. Diallo,
    D. Faria, M. Kachroudi, A. Khiat, P. Lambrix, H. Li, M. Mackeprang, M. Mohammadi,
    M. Rybinski, B.S. Balasubramani, C. Trojahn, in: Proceedings of the Ontology Matching
    Workshop 2018, 2018.'
date_created: 2023-08-16T10:31:40Z
date_updated: 2026-03-09T12:49:48Z
department:
- _id: '574'
keyword:
- 2018 DICE SIMBA group_aksw ngonga projecthobbit roeder sherif
language:
- iso: eng
publication: Proceedings of the Ontology Matching Workshop 2018
status: public
title: Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign
type: conference
user_id: '14972'
year: '2018'
...
---
_id: '29032'
abstract:
- lang: eng
  text: Large amounts of geo-spatial information have been made available with the
    growth of the Web of Data. While discovering links between resources on the Web
    of Data has been shown to be a demanding task, discovering links between geo-spatial
    resources proves to be even more challenging. This is partly due to the resources
    being described by the means of vector geometry. Especially, discrepancies in
    granularity and error measurements across data sets render the selection of appropriate
    distance measures for geo-spatial resources difficult. In this paper, we survey
    existing literature for point-set measures that can be used to measure the similarity
    of vector geometries. We then present and evaluate the ten measures that we derived
    from literature. We evaluate these measures with respect to their time-efficiency
    and their robustness against discrepancies in measurement and in granularity.
    To this end, we use samples of real data sets of different granularity as input
    for our evaluation framework. The results obtained on three different data sets
    suggest that most distance approaches can be led to scale. Moreover, while some
    distance measures are significantly slower than other measures, distance measure
    based on means, surjections and sums of minimal distances are robust against the
    different types of discrepancies.
author:
- 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: Sherif M, Ngonga Ngomo A-C. A Systematic Survey of Point Set Distance Measures
    for Link Discovery. <i>Semantic Web Journal</i>. Published online 2017.
  apa: Sherif, M., &#38; Ngonga Ngomo, A.-C. (2017). A Systematic Survey of Point
    Set Distance Measures for Link Discovery. <i>Semantic Web Journal</i>.
  bibtex: '@article{Sherif_Ngonga Ngomo_2017, title={A Systematic Survey of Point
    Set Distance Measures for Link Discovery}, journal={Semantic Web Journal}, author={Sherif,
    Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2017} }'
  chicago: Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “A Systematic Survey of
    Point Set Distance Measures for Link Discovery.” <i>Semantic Web Journal</i>,
    2017.
  ieee: M. Sherif and A.-C. Ngonga Ngomo, “A Systematic Survey of Point Set Distance
    Measures for Link Discovery,” <i>Semantic Web Journal</i>, 2017.
  mla: Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “A Systematic Survey of Point
    Set Distance Measures for Link Discovery.” <i>Semantic Web Journal</i>, 2017.
  short: M. Sherif, A.-C. Ngonga Ngomo, Semantic Web Journal (2017).
date_created: 2021-12-17T10:00:03Z
date_updated: 2023-08-16T09:18:34Z
keyword:
- 2017 group\_aksw slipo sys:relevantFor:infai sys:relevantFor:bis ngonga simba DICE
  sherif geo-distance limes
language:
- iso: eng
publication: Semantic Web Journal
status: public
title: A Systematic Survey of Point Set Distance Measures for Link Discovery
type: journal_article
user_id: '67234'
year: '2017'
...
---
_id: '29018'
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Kevin
  full_name: Dreßler, Kevin
  id: '78256'
  last_name: Dreßler
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Sherif M, Dreßler K, Ngonga Ngomo A-C. RADON results for OAEI 2017. In: <i>Proceedings
    of Ontology Matching Workshop 2017</i>. ; 2017.'
  apa: Sherif, M., Dreßler, K., &#38; Ngonga Ngomo, A.-C. (2017). RADON results for
    OAEI 2017. <i>Proceedings of Ontology Matching Workshop 2017</i>.
  bibtex: '@inproceedings{Sherif_Dreßler_Ngonga Ngomo_2017, title={RADON results for
    OAEI 2017}, booktitle={Proceedings of Ontology Matching Workshop 2017}, author={Sherif,
    Mohamed and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2017} }'
  chicago: Sherif, Mohamed, Kevin Dreßler, and Axel-Cyrille Ngonga Ngomo. “RADON Results
    for OAEI 2017.” In <i>Proceedings of Ontology Matching Workshop 2017</i>, 2017.
  ieee: M. Sherif, K. Dreßler, and A.-C. Ngonga Ngomo, “RADON results for OAEI 2017,”
    2017.
  mla: Sherif, Mohamed, et al. “RADON Results for OAEI 2017.” <i>Proceedings of Ontology
    Matching Workshop 2017</i>, 2017.
  short: 'M. Sherif, K. Dreßler, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching
    Workshop 2017, 2017.'
date_created: 2021-12-17T09:56:11Z
date_updated: 2023-08-16T09:23:30Z
keyword:
- 2017 dice simba sherif radon ngonga slipo sage geiser hobbit group\_aksw sys:relevantFor:infai
  sys:relevantFor:bis limes linkinglod sake diesel kevin sys:relevantFor:leds leds
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2017
status: public
title: RADON results for OAEI 2017
type: conference
user_id: '67234'
year: '2017'
...