---
_id: '29030'
abstract:
- lang: eng
  text: Geospatial data is at the core of the Semantic Web, of which the largest knowledge
    base contains more than 30 billions facts. Reasoning on these large amounts of
    geospatial data requires efficient methods for the computation of links between
    the resources contained in these knowledge bases. In this paper, we present RADON
    - efficient solution for the discovery of topological relations between geospatial
    resources according to the DE9-IM standard. Our evaluation shows that we outperform
    the state of the art significantly and by several orders of magnitude.
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: Panayiotis
  full_name: Smeros, Panayiotis
  last_name: Smeros
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Sherif M, Dreßler K, Smeros P, Ngonga Ngomo A-C. RADON - Rapid Discovery of
    Topological Relations. In: <i>Proceedings of The Thirty-First AAAI Conference
    on Artificial Intelligence (AAAI-17)</i>. ; 2017.'
  apa: Sherif, M., Dreßler, K., Smeros, P., &#38; Ngonga Ngomo, A.-C. (2017). RADON
    - Rapid Discovery of Topological Relations. <i>Proceedings of The Thirty-First
    AAAI Conference on Artificial Intelligence (AAAI-17)</i>.
  bibtex: '@inproceedings{Sherif_Dreßler_Smeros_Ngonga Ngomo_2017, title={RADON -
    Rapid Discovery of Topological Relations}, booktitle={Proceedings of The Thirty-First
    AAAI Conference on Artificial Intelligence (AAAI-17)}, author={Sherif, Mohamed
    and Dreßler, Kevin and Smeros, Panayiotis and Ngonga Ngomo, Axel-Cyrille}, year={2017}
    }'
  chicago: Sherif, Mohamed, Kevin Dreßler, Panayiotis Smeros, and Axel-Cyrille Ngonga
    Ngomo. “RADON - Rapid Discovery of Topological Relations.” In <i>Proceedings of
    The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)</i>, 2017.
  ieee: M. Sherif, K. Dreßler, P. Smeros, and A.-C. Ngonga Ngomo, “RADON - Rapid Discovery
    of Topological Relations,” 2017.
  mla: Sherif, Mohamed, et al. “RADON - Rapid Discovery of Topological Relations.”
    <i>Proceedings of The Thirty-First AAAI Conference on Artificial Intelligence
    (AAAI-17)</i>, 2017.
  short: 'M. Sherif, K. Dreßler, P. Smeros, A.-C. Ngonga Ngomo, in: Proceedings of
    The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 2017.'
date_created: 2021-12-17T10:00:01Z
date_updated: 2023-08-16T10:05:32Z
keyword:
- radon sherif limes projecthobbit hobbit geiser group\_aksw SIMBA DICE sys:relevantFor:infai
  sys:relevantFor:bis sys:relevantFor:leds leds ngonga bioasq kevin
language:
- iso: eng
publication: Proceedings of The Thirty-First AAAI Conference on Artificial Intelligence
  (AAAI-17)
status: public
title: RADON - Rapid Discovery of Topological Relations
type: conference
user_id: '67234'
year: '2017'
...
---
_id: '29024'
abstract:
- lang: eng
  text: A significant portion of the evolution of Linked Data datasets lies in updating
    the links to other datasets. An important challenge when aiming to update these
    links automatically under the open-world assumption is the fact that usually only
    positive examples for the links exist. We address this challenge by presenting
    and evaluating WOMBAT , a novel approach for the discovery of links between knowledge
    bases that relies exclusively on positive examples. WOMBAT is based on generalisation
    via an upward refinement operator to traverse the space of link specification.
    We study the theoretical characteristics of WOMBAT and evaluate it on 8 different
    benchmark datasets. Our evaluation suggests that WOMBAT outperforms state-of-the-art
    supervised approaches while relying on less information. Moreover, our evaluation
    suggests that WOMBAT ’s pruning algorithm allows it to scale well even on large
    datasets.
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
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
citation:
  ama: 'Sherif M, Ngonga Ngomo A-C, Lehmann J. WOMBAT - A Generalization Approach
    for Automatic Link Discovery. In: <i>14th Extended Semantic Web Conference, Portorož,
    Slovenia, 28th May - 1st June 2017</i>. Springer; 2017.'
  apa: Sherif, M., Ngonga Ngomo, A.-C., &#38; Lehmann, J. (2017). WOMBAT - A Generalization
    Approach for Automatic Link Discovery. <i>14th Extended Semantic Web Conference,
    Portorož, Slovenia, 28th May - 1st June 2017</i>.
  bibtex: '@inproceedings{Sherif_Ngonga Ngomo_Lehmann_2017, title={WOMBAT - A Generalization
    Approach for Automatic Link Discovery}, booktitle={14th Extended Semantic Web
    Conference, Portorož, Slovenia, 28th May - 1st June 2017}, publisher={Springer},
    author={Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille and Lehmann, Jens}, year={2017}
    }'
  chicago: Sherif, Mohamed, Axel-Cyrille Ngonga Ngomo, and Jens Lehmann. “WOMBAT -
    A Generalization Approach for Automatic Link Discovery.” In <i>14th Extended Semantic
    Web Conference, Portorož, Slovenia, 28th May - 1st June 2017</i>. Springer, 2017.
  ieee: M. Sherif, A.-C. Ngonga Ngomo, and J. Lehmann, “WOMBAT - A Generalization
    Approach for Automatic Link Discovery,” 2017.
  mla: Sherif, Mohamed, et al. “WOMBAT - A Generalization Approach for Automatic Link
    Discovery.” <i>14th Extended Semantic Web Conference, Portorož, Slovenia, 28th
    May - 1st June 2017</i>, Springer, 2017.
  short: 'M. Sherif, A.-C. Ngonga Ngomo, J. Lehmann, in: 14th Extended Semantic Web
    Conference, Portorož, Slovenia, 28th May - 1st June 2017, Springer, 2017.'
date_created: 2021-12-17T09:59:53Z
date_updated: 2023-08-16T10:34:35Z
keyword:
- 2017 group\_aksw sys:relevantFor:geoknow sys:relevantFor:infai sys:relevantFor:bis
  ngonga simba dice sherif group\_aksw geoknow wombat lehmann MOLE
language:
- iso: eng
publication: 14th Extended Semantic Web Conference, Portorož, Slovenia, 28th May -
  1st June 2017
publication_status: published
publisher: Springer
status: public
title: WOMBAT - A Generalization Approach for Automatic Link Discovery
type: conference
user_id: '67234'
year: '2017'
...
---
_id: '29027'
abstract:
- lang: eng
  text: Over the last years, the Linked Open Data (LOD) has evolved from a mere 12
    to more than 10, 000 knowledge bases. These knowledge bases come from diverse
    domains including (but not limited to) publications, life sciences, social networking,
    government, media, linguistics. Moreover, the LOD cloud also contains a large
    number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge
    bases are commonly managed in a decentralized fashion and contain partly overlapping
    information. This architectural choice has led to knowledge pertaining to the
    same domain being published by independent entities in the LOD cloud. For example,
    information on drugs can be found in Diseasome as well as DBpedia and Drugbank.
    Furthermore, certain knowledge bases such as DBLP have been published by several
    bodies, which in turn has lead to duplicated content in the LOD. In addition,
    large amounts of geo-spatial information have been made available with the growth
    of heterogeneous Web of Data. The concurrent publication of knowledge bases containing
    related information promises to become a phenomenon of increasing importance with
    the growth of the number of independent data providers. Enabling the joint use
    of the knowledge bases published by these providers for tasks such as federated
    queries, cross-ontology question answering and data integration is most commonly
    tackled by creating links between the resources described within these knowledge
    bases. Within this thesis, we spur the transition from isolated knowledge bases
    to enriched Linked Data sets where information can be easily integrated and processed.
    To achieve this goal, we provide concepts, approaches and use cases that facilitate
    the integration and enrichment of information with other data types that are already
    present on the Linked Data Web with a focus on geo-spatial data. The first challenge
    that motivates our work is the lack of measures that use the geographic data for
    linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources
    being described by the means of vector geometry. In particular, discrepancies
    in granularity and error measurements across knowledge bases render the selection
    of appropriate distance measures for geo-spatial resources difficult. We address
    this challenge by evaluating existing literature for pointset measures that can
    be used to measure the similarity of vector geometries. Then, we present and evaluate
    the ten measures that we derived from the literature on samples of three real
    knowledge bases. The second challenge we address in this thesis is the lack of
    automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge
    bases with missing and erroneous data. To this end,we present Colibri, an unsupervised
    approach that allows discovering links between knowledge bases while improving
    the quality of the instance data in these knowledge bases. A Colibri iteration
    begins by generating links between knowledge bases. Then, the approach makes use
    of these links to detect resources with probably erroneous or missing information.
    This erroneous or missing infor- mation detected by the approach is finally corrected
    or added. The third challenge we address is the lack of scalable LD approaches
    for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm
    Optimization (DPSO), a novel load balancing technique for LD on parallel hardware
    based on particle-swarm optimization. We combine this approach with the Orchid
    algorithm for geo-spatial linking and evaluate it on real and artificial data
    sets. The lack of approaches for automatic updating of links of an evolving knowledge
    base is our fourth challenge. This challenge is addressed in this thesis by the
    Wombat algorithm. Wombat is a novel approach for the discovery of links between
    knowledge bases that relies exclusively on positive examples. Wombat is based
    on generalisation via an upward refinement operator to traverse the space of Link
    Specifications (LS). We study the theoretical characteristics of Wombat and evaluate
    it on different benchmark data sets. The last challenge addressed herein is the
    lack of automatic approaches for geo-spatial knowledge base enrichment. Thus,
    we propose Deer, a supervised learning approach based on a refinement operator
    for enriching Resource Description Framework (RDF) data sets. We show how we can
    use exemplary descriptions of enriched resources to generate accurate enrichment
    pipelines. We evaluate our approach against manually defined enrichment pipelines
    and show that our approach can learn accurate pipelines even when provided with
    a small number of training examples. Each of the proposed approaches is implemented
    and evaluated against state-of-the-art approaches on real and/or artificial data
    sets. Moreover, all approaches are peer-reviewed and published in a con- ference
    or a journal paper. Throughout this thesis, we detail the ideas, implementation
    and the evaluation of each of the approaches. Moreover, we discuss each approach
    and present lessons learned. Finally, we conclude this thesis by presenting a
    set of possible future extensions and use cases for each of the proposed approaches.
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
citation:
  ama: Sherif M. <i>Automating Geospatial RDF Dataset Integration and Enrichment</i>.
    University of Leipzig; 2016.
  apa: Sherif, M. (2016). <i>Automating Geospatial RDF Dataset Integration and Enrichment</i>.
    University of Leipzig.
  bibtex: '@book{Sherif_2016, place={Leipzig, Germany}, title={Automating Geospatial
    RDF Dataset Integration and Enrichment}, publisher={University of Leipzig}, author={Sherif,
    Mohamed}, year={2016} }'
  chicago: 'Sherif, Mohamed. <i>Automating Geospatial RDF Dataset Integration and
    Enrichment</i>. Leipzig, Germany: University of Leipzig, 2016.'
  ieee: 'M. Sherif, <i>Automating Geospatial RDF Dataset Integration and Enrichment</i>.
    Leipzig, Germany: University of Leipzig, 2016.'
  mla: Sherif, Mohamed. <i>Automating Geospatial RDF Dataset Integration and Enrichment</i>.
    University of Leipzig, 2016.
  short: M. Sherif, Automating Geospatial RDF Dataset Integration and Enrichment,
    University of Leipzig, Leipzig, Germany, 2016.
date_created: 2021-12-17T09:59:57Z
date_updated: 2024-05-08T10:40:28Z
keyword:
- 2016 group\_aksw sys:relevantFor:geoknow sys:relevantFor:infai sys:relevantFor:bis
  ngonga simba dice sherif group\_aksw geoknow deer lehmann MOLE
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.qucosa.de/landing-page/?tx_dlf%5Bid%5D=https%3A%2F%2Fwww.qucosa.de%2Fapi%2Fqucosa%253A15175%2Fmets%2F&cHash=22c1b49c76de010dc4fb42260d8a1cf6
oa: '1'
place: Leipzig, Germany
publisher: University of Leipzig
status: public
supervisor:
- first_name: 'Klaus-Peter '
  full_name: 'Fähnrich, Klaus-Peter '
  last_name: Fähnrich
- first_name: 'Jens '
  full_name: 'Lehmann, Jens '
  last_name: Lehmann
title: Automating Geospatial RDF Dataset Integration and Enrichment
type: dissertation
user_id: '67234'
year: '2016'
...
---
_id: '29031'
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Mofeed
  full_name: Hassan, Mofeed
  last_name: Hassan
- first_name: Tommaso
  full_name: Soru, Tommaso
  last_name: Soru
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
citation:
  ama: 'Sherif M, Hassan M, Soru T, Ngonga Ngomo A-C, Lehmann J. Lion’s Den: Feeding
    the LinkLion. In: <i>Proceedings of Ontology Matching Workshop</i>. ; 2016.'
  apa: 'Sherif, M., Hassan, M., Soru, T., Ngonga Ngomo, A.-C., &#38; Lehmann, J. (2016).
    Lion’s Den: Feeding the LinkLion. <i>Proceedings of Ontology Matching Workshop</i>.'
  bibtex: '@inproceedings{Sherif_Hassan_Soru_Ngonga Ngomo_Lehmann_2016, title={Lion’s
    Den: Feeding the LinkLion}, booktitle={Proceedings of Ontology Matching Workshop},
    author={Sherif, Mohamed and Hassan, Mofeed and Soru, Tommaso and Ngonga Ngomo,
    Axel-Cyrille and Lehmann, Jens}, year={2016} }'
  chicago: 'Sherif, Mohamed, Mofeed Hassan, Tommaso Soru, Axel-Cyrille Ngonga Ngomo,
    and Jens Lehmann. “Lion’s Den: Feeding the LinkLion.” In <i>Proceedings of Ontology
    Matching Workshop</i>, 2016.'
  ieee: 'M. Sherif, M. Hassan, T. Soru, A.-C. Ngonga Ngomo, and J. Lehmann, “Lion’s
    Den: Feeding the LinkLion,” 2016.'
  mla: 'Sherif, Mohamed, et al. “Lion’s Den: Feeding the LinkLion.” <i>Proceedings
    of Ontology Matching Workshop</i>, 2016.'
  short: 'M. Sherif, M. Hassan, T. Soru, A.-C. Ngonga Ngomo, J. Lehmann, in: Proceedings
    of Ontology Matching Workshop, 2016.'
date_created: 2021-12-17T10:00:02Z
date_updated: 2023-08-16T10:01:07Z
keyword:
- sherif hassan soru lehmann ngonga geoknow group\_aksw SIMBA DICE sys:relevantFor:infai
  sys:relevantFor:bis limes
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop
status: public
title: 'Lion''s Den: Feeding the LinkLion'
type: conference
user_id: '67234'
year: '2016'
...
---
_id: '29016'
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. An Efficient Approach for the Generation
    of Allen Relations. In: <i>Proceedings of the 22nd European Conference on Artificial
    Intelligence (ECAI) 2016, The Hague, 29. August - 02. September 2016</i>. ; 2016.'
  apa: Georgala, K., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2016). An Efficient Approach
    for the Generation of Allen Relations. <i>Proceedings of the 22nd European Conference
    on Artificial Intelligence (ECAI) 2016, The Hague, 29. August - 02. September
    2016</i>.
  bibtex: '@inproceedings{Georgala_Sherif_Ngonga Ngomo_2016, title={An Efficient Approach
    for the Generation of Allen Relations}, booktitle={Proceedings of the 22nd European
    Conference on Artificial Intelligence (ECAI) 2016, The Hague, 29. August - 02.
    September 2016}, author={Georgala, Kleanthi and Sherif, Mohamed and Ngonga Ngomo,
    Axel-Cyrille}, year={2016} }'
  chicago: Georgala, Kleanthi, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “An
    Efficient Approach for the Generation of Allen Relations.” In <i>Proceedings of
    the 22nd European Conference on Artificial Intelligence (ECAI) 2016, The Hague,
    29. August - 02. September 2016</i>, 2016.
  ieee: K. Georgala, M. Sherif, and A.-C. Ngonga Ngomo, “An Efficient Approach for
    the Generation of Allen Relations,” 2016.
  mla: Georgala, Kleanthi, et al. “An Efficient Approach for the Generation of Allen
    Relations.” <i>Proceedings of the 22nd European Conference on Artificial Intelligence
    (ECAI) 2016, The Hague, 29. August - 02. September 2016</i>, 2016.
  short: 'K. Georgala, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 22nd
    European Conference on Artificial Intelligence (ECAI) 2016, The Hague, 29. August
    - 02. September 2016, 2016.'
date_created: 2021-12-17T09:55:45Z
date_updated: 2023-08-16T10:05:14Z
keyword:
- sys:relevantFor:infai group\_aksw simba georgala sherif ngonga sake projecthobbit
  limes dice
language:
- iso: eng
publication: Proceedings of the 22nd European Conference on Artificial Intelligence
  (ECAI) 2016, The Hague, 29. August - 02. September 2016
status: public
title: An Efficient Approach for the Generation of Allen Relations
type: conference
user_id: '67234'
year: '2016'
...
---
_id: '29026'
abstract:
- lang: eng
  text: With the adoption of RDF across several domains, come growing requirements
    pertaining to the completeness and quality of RDF datasets. Currently, this problem
    is most commonly addressed by manually devising means of enriching an input dataset.
    The few tools that aim at supporting this endeavour usually focus on supporting
    the manual definition of enrichment pipelines. In this paper, we present a supervised
    learning approach based on a refinement operator for enriching RDF datasets. We
    show how we can use exemplary descriptions of enriched resources to generate accurate
    enrichment pipelines. We evaluate our approach against eight manually defined
    enrichment pipelines and show that our approach can learn accurate pipelines even
    when provided with a small number of training examples.
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
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
citation:
  ama: 'Sherif M, Ngonga Ngomo A-C, Lehmann J. Automating RDF Dataset Transformation
    and Enrichment. In: <i>12th Extended Semantic Web Conference, Portorož, Slovenia,
    31st May - 4th June 2015</i>. Springer; 2015.'
  apa: Sherif, M., Ngonga Ngomo, A.-C., &#38; Lehmann, J. (2015). Automating RDF Dataset
    Transformation and Enrichment. <i>12th Extended Semantic Web Conference, Portorož,
    Slovenia, 31st May - 4th June 2015</i>.
  bibtex: '@inproceedings{Sherif_Ngonga Ngomo_Lehmann_2015, title={Automating RDF
    Dataset Transformation and Enrichment}, booktitle={12th Extended Semantic Web
    Conference, Portorož, Slovenia, 31st May - 4th June 2015}, publisher={Springer},
    author={Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille and Lehmann, Jens}, year={2015}
    }'
  chicago: Sherif, Mohamed, Axel-Cyrille Ngonga Ngomo, and Jens Lehmann. “Automating
    RDF Dataset Transformation and Enrichment.” In <i>12th Extended Semantic Web Conference,
    Portorož, Slovenia, 31st May - 4th June 2015</i>. Springer, 2015.
  ieee: M. Sherif, A.-C. Ngonga Ngomo, and J. Lehmann, “Automating RDF Dataset Transformation
    and Enrichment,” 2015.
  mla: Sherif, Mohamed, et al. “Automating RDF Dataset Transformation and Enrichment.”
    <i>12th Extended Semantic Web Conference, Portorož, Slovenia, 31st May - 4th June
    2015</i>, Springer, 2015.
  short: 'M. Sherif, A.-C. Ngonga Ngomo, J. Lehmann, in: 12th Extended Semantic Web
    Conference, Portorož, Slovenia, 31st May - 4th June 2015, Springer, 2015.'
date_created: 2021-12-17T09:59:55Z
date_updated: 2023-08-16T09:37:18Z
keyword:
- 2015 group\_aksw sys:relevantFor:geoknow sys:relevantFor:infai sys:relevantFor:bis
  ngonga simba dice sherif group\_aksw geoknow deer lehmann MOLE
language:
- iso: eng
publication: 12th Extended Semantic Web Conference, Portorož, Slovenia, 31st May -
  4th June 2015
publisher: Springer
status: public
title: Automating RDF Dataset Transformation and Enrichment
type: conference
user_id: '67234'
year: '2015'
...
---
_id: '29035'
abstract:
- lang: eng
  text: The combination of the advantages of widely used relational databases and
    semantic technologies has attracted significant research over the past decade.
    In particular, mapping languages for the conversion of databases to RDF knowledge
    bases have been developed and standardized in the form of R2RML. In this article,
    we first review those mapping languages and then devise work towards a unified
    formal model for them. Based on this, we present the Sparqlification Mapping Language
    (SML), which provides an intuitive way to declare mappings based on SQL VIEWS
    and SPARQL construct queries. We show that SML has the same expressivity as R2RML
    by enumerating the language features and show the correspondences, and we outline
    how one syntax can be converted into the other. A conducted user study for this
    paper juxtaposing SML and R2RML provides evidence that SML is a more compact syntax
    which is easier to understand and read and thus lowers the barrier to offer SPARQL
    access to relational databases.
author:
- first_name: Claus
  full_name: Stadler, Claus
  last_name: Stadler
- first_name: Joerg
  full_name: Unbehauen, Joerg
  last_name: Unbehauen
- first_name: Patrick
  full_name: Westphal, Patrick
  last_name: Westphal
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
citation:
  ama: 'Stadler C, Unbehauen J, Westphal P, Sherif M, Lehmann J. Simplified RDB2RDF
    Mapping. In: <i>Proceedings of the 8th Workshop on Linked Data on the Web (LDOW2015),
    Florence, Italy</i>. ; 2015.'
  apa: Stadler, C., Unbehauen, J., Westphal, P., Sherif, M., &#38; Lehmann, J. (2015).
    Simplified RDB2RDF Mapping. <i>Proceedings of the 8th Workshop on Linked Data
    on the Web (LDOW2015), Florence, Italy</i>.
  bibtex: '@inproceedings{Stadler_Unbehauen_Westphal_Sherif_Lehmann_2015, title={Simplified
    RDB2RDF Mapping}, booktitle={Proceedings of the 8th Workshop on Linked Data on
    the Web (LDOW2015), Florence, Italy}, author={Stadler, Claus and Unbehauen, Joerg
    and Westphal, Patrick and Sherif, Mohamed and Lehmann, Jens}, year={2015} }'
  chicago: Stadler, Claus, Joerg Unbehauen, Patrick Westphal, Mohamed Sherif, and
    Jens Lehmann. “Simplified RDB2RDF Mapping.” In <i>Proceedings of the 8th Workshop
    on Linked Data on the Web (LDOW2015), Florence, Italy</i>, 2015.
  ieee: C. Stadler, J. Unbehauen, P. Westphal, M. Sherif, and J. Lehmann, “Simplified
    RDB2RDF Mapping,” 2015.
  mla: Stadler, Claus, et al. “Simplified RDB2RDF Mapping.” <i>Proceedings of the
    8th Workshop on Linked Data on the Web (LDOW2015), Florence, Italy</i>, 2015.
  short: 'C. Stadler, J. Unbehauen, P. Westphal, M. Sherif, J. Lehmann, in: Proceedings
    of the 8th Workshop on Linked Data on the Web (LDOW2015), Florence, Italy, 2015.'
date_created: 2021-12-17T10:04:19Z
date_updated: 2023-08-16T09:36:55Z
keyword:
- 2015 group\_aksw group\_mole mole stadler lehmann sherif simba dice sys:relevantFor:geoknow
  geoknow peer-reviewed MOLE westphal
language:
- iso: eng
publication: Proceedings of the 8th Workshop on Linked Data on the Web (LDOW2015),
  Florence, Italy
status: public
title: Simplified RDB2RDF Mapping
type: conference
user_id: '67234'
year: '2015'
...
---
_id: '29033'
abstract:
- lang: eng
  text: Many of the available RDF datasets describe millions of resources by using
    billions of triples. Consequently, millions of links can potentially exist among
    such datasets. While parallel implementations of link discovery approaches have
    been developed in the past, load balancing approaches for local implementations
    of link discovery algorithms have been paid little attention to. In this paper,
    we thus present a novel load balancing technique for link discovery on parallel
    hardware based on particle-swarm optimization. We combine this approach with the
    Orchid algorithm for geo-spatial linking and evaluate it on real and artificial
    datasets. Our evaluation suggests that while naïve approaches can be super-linear
    on small data sets, our deterministic particle swarm optimization outperforms
    both naïve and classical load balancing approaches such as greedy load balancing
    on large datasets.
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. An Optimization Approach for Load Balancing in
    Parallel Link Discovery. In: <i>SEMANTiCS 2015</i>. ; 2015.'
  apa: Sherif, M., &#38; Ngonga Ngomo, A.-C. (2015). An Optimization Approach for
    Load Balancing in Parallel Link Discovery. <i>SEMANTiCS 2015</i>.
  bibtex: '@inproceedings{Sherif_Ngonga Ngomo_2015, title={An Optimization Approach
    for Load Balancing in Parallel Link Discovery}, booktitle={SEMANTiCS 2015}, author={Sherif,
    Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2015} }'
  chicago: Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “An Optimization Approach
    for Load Balancing in Parallel Link Discovery.” In <i>SEMANTiCS 2015</i>, 2015.
  ieee: M. Sherif and A.-C. Ngonga Ngomo, “An Optimization Approach for Load Balancing
    in Parallel Link Discovery,” 2015.
  mla: Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “An Optimization Approach for
    Load Balancing in Parallel Link Discovery.” <i>SEMANTiCS 2015</i>, 2015.
  short: 'M. Sherif, A.-C. Ngonga Ngomo, in: SEMANTiCS 2015, 2015.'
date_created: 2021-12-17T10:00:05Z
date_updated: 2023-08-16T09:37:10Z
keyword:
- 2015 sys:relevantFor:geoknow geoknow ngonga sherif simba dice group\_aksw sys:relevantFor:infai
  sys:relevantFor:bis SIMBA limes
language:
- iso: eng
publication: SEMANTiCS 2015
status: public
title: An Optimization Approach for Load Balancing in Parallel Link Discovery
type: conference
user_id: '67234'
year: '2015'
...
---
_id: '29020'
author:
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
- first_name: Spiros
  full_name: Athanasiou, Spiros
  last_name: Athanasiou
- first_name: Andreas
  full_name: Both, Andreas
  last_name: Both
- first_name: Alejandra
  full_name: Garcia-Rojas, Alejandra
  last_name: Garcia-Rojas
- first_name: Giorgos
  full_name: Giannopoulos, Giorgos
  last_name: Giannopoulos
- first_name: Daniel
  full_name: Hladky, Daniel
  last_name: Hladky
- first_name: Konrad
  full_name: Hoeffner, Konrad
  last_name: Hoeffner
- first_name: Jon
  full_name: Jay Le Grange, Jon
  last_name: Jay Le Grange
- 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: Claus
  full_name: Stadler, Claus
  last_name: Stadler
- first_name: Matthias
  full_name: Wauer, Matthias
  last_name: Wauer
- first_name: Patrick
  full_name: Westphal, Patrick
  last_name: Westphal
- first_name: Vadim
  full_name: Zaslawski, Vadim
  last_name: Zaslawski
citation:
  ama: 'Lehmann J, Athanasiou S, Both A, et al. Managing Geospatial Linked Data in
    the GeoKnow Project. In: <i>The Semantic Web in Earth and Space Science. Current
    Status and Future Directions</i>. Vol Volume 20. Studies on the Semantic Web .
    IOS Press; 2015:51–78. doi:<a href="https://doi.org/ 10.3233/978-1-61499-501-2-51">
    10.3233/978-1-61499-501-2-51</a>'
  apa: 'Lehmann, J., Athanasiou, S., Both, A., Garcia-Rojas, A., Giannopoulos, G.,
    Hladky, D., Hoeffner, K., Jay Le Grange, J., Ngonga Ngomo, A.-C., Sherif, M.,
    Stadler, C., Wauer, M., Westphal, P., &#38; Zaslawski, V. (2015). Managing Geospatial
    Linked Data in the GeoKnow Project. In <i>The Semantic Web in Earth and Space
    Science. Current Status and Future Directions: Vol. Volume 20</i> (pp. 51–78).
    IOS Press. <a href="https://doi.org/ 10.3233/978-1-61499-501-2-51">https://doi.org/
    10.3233/978-1-61499-501-2-51</a>'
  bibtex: '@inbook{Lehmann_Athanasiou_Both_Garcia-Rojas_Giannopoulos_Hladky_Hoeffner_Jay
    Le Grange_Ngonga Ngomo_Sherif_et al._2015, series={Studies on the Semantic Web
    }, title={Managing Geospatial Linked Data in the GeoKnow Project}, volume={Volume
    20}, DOI={<a href="https://doi.org/ 10.3233/978-1-61499-501-2-51"> 10.3233/978-1-61499-501-2-51</a>},
    booktitle={The Semantic Web in Earth and Space Science. Current Status and Future
    Directions}, publisher={IOS Press}, author={Lehmann, Jens and Athanasiou, Spiros
    and Both, Andreas and Garcia-Rojas, Alejandra and Giannopoulos, Giorgos and Hladky,
    Daniel and Hoeffner, Konrad and Jay Le Grange, Jon and Ngonga Ngomo, Axel-Cyrille
    and Sherif, Mohamed and et al.}, year={2015}, pages={51–78}, collection={Studies
    on the Semantic Web } }'
  chicago: Lehmann, Jens, Spiros Athanasiou, Andreas Both, Alejandra Garcia-Rojas,
    Giorgos Giannopoulos, Daniel Hladky, Konrad Hoeffner, et al. “Managing Geospatial
    Linked Data in the GeoKnow Project.” In <i>The Semantic Web in Earth and Space
    Science. Current Status and Future Directions</i>, Volume 20:51–78. Studies on
    the Semantic Web . IOS Press, 2015. <a href="https://doi.org/ 10.3233/978-1-61499-501-2-51">https://doi.org/
    10.3233/978-1-61499-501-2-51</a>.
  ieee: J. Lehmann <i>et al.</i>, “Managing Geospatial Linked Data in the GeoKnow
    Project,” in <i>The Semantic Web in Earth and Space Science. Current Status and
    Future Directions</i>, vol. Volume 20, IOS Press, 2015, pp. 51–78.
  mla: Lehmann, Jens, et al. “Managing Geospatial Linked Data in the GeoKnow Project.”
    <i>The Semantic Web in Earth and Space Science. Current Status and Future Directions</i>,
    vol. Volume 20, IOS Press, 2015, pp. 51–78, doi:<a href="https://doi.org/ 10.3233/978-1-61499-501-2-51">
    10.3233/978-1-61499-501-2-51</a>.
  short: 'J. Lehmann, S. Athanasiou, A. Both, A. Garcia-Rojas, G. Giannopoulos, D.
    Hladky, K. Hoeffner, J. Jay Le Grange, A.-C. Ngonga Ngomo, M. Sherif, C. Stadler,
    M. Wauer, P. Westphal, V. Zaslawski, in: The Semantic Web in Earth and Space Science.
    Current Status and Future Directions, IOS Press, 2015, pp. 51–78.'
date_created: 2021-12-17T09:56:31Z
date_updated: 2023-08-16T09:42:23Z
doi: ' 10.3233/978-1-61499-501-2-51'
keyword:
- 2015 group\_aksw sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:geoknow
  lehmann ngonga MOLE sherif simba dice hoeffner geoknow wauer westphal
language:
- iso: eng
page: 51–78
publication: The Semantic Web in Earth and Space Science. Current Status and Future
  Directions
publisher: IOS Press
series_title: 'Studies on the Semantic Web '
status: public
title: Managing Geospatial Linked Data in the GeoKnow Project
type: book_chapter
user_id: '67234'
volume: ' Volume 20'
year: '2015'
...
---
_id: '29019'
author:
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
- first_name: Spiros
  full_name: Athanasiou, Spiros
  last_name: Athanasiou
- first_name: Andreas
  full_name: Both, Andreas
  last_name: Both
- first_name: Lorenz
  full_name: Buehmann, Lorenz
  last_name: Buehmann
- first_name: Alejandra
  full_name: Garcia-Rojas, Alejandra
  last_name: Garcia-Rojas
- first_name: Giorgos
  full_name: Giannopoulos, Giorgos
  last_name: Giannopoulos
- first_name: Daniel
  full_name: Hladky, Daniel
  last_name: Hladky
- first_name: Konrad
  full_name: Hoeffner, Konrad
  last_name: Hoeffner
- first_name: Jon
  full_name: Jay Le Grange, Jon
  last_name: Jay Le Grange
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
- first_name: Rene
  full_name: Pietzsch, Rene
  last_name: Pietzsch
- first_name: Robert
  full_name: Isele, Robert
  last_name: Isele
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Claus
  full_name: Stadler, Claus
  last_name: Stadler
- first_name: Matthias
  full_name: Wauer, Matthias
  last_name: Wauer
- first_name: Patrick
  full_name: Westphal, Patrick
  last_name: Westphal
citation:
  ama: Lehmann J, Athanasiou S, Both A, et al. <i>The GeoKnow Handbook</i>.; 2015.
  apa: Lehmann, J., Athanasiou, S., Both, A., Buehmann, L., Garcia-Rojas, A., Giannopoulos,
    G., Hladky, D., Hoeffner, K., Jay Le Grange, J., Ngonga Ngomo, A.-C., Pietzsch,
    R., Isele, R., Sherif, M., Stadler, C., Wauer, M., &#38; Westphal, P. (2015).
    <i>The GeoKnow Handbook</i>.
  bibtex: '@book{Lehmann_Athanasiou_Both_Buehmann_Garcia-Rojas_Giannopoulos_Hladky_Hoeffner_Jay
    Le Grange_Ngonga Ngomo_et al._2015, title={The GeoKnow Handbook}, author={Lehmann,
    Jens and Athanasiou, Spiros and Both, Andreas and Buehmann, Lorenz and Garcia-Rojas,
    Alejandra and Giannopoulos, Giorgos and Hladky, Daniel and Hoeffner, Konrad and
    Jay Le Grange, Jon and Ngonga Ngomo, Axel-Cyrille and et al.}, year={2015} }'
  chicago: Lehmann, Jens, Spiros Athanasiou, Andreas Both, Lorenz Buehmann, Alejandra
    Garcia-Rojas, Giorgos Giannopoulos, Daniel Hladky, et al. <i>The GeoKnow Handbook</i>,
    2015.
  ieee: J. Lehmann <i>et al.</i>, <i>The GeoKnow Handbook</i>. 2015.
  mla: Lehmann, Jens, et al. <i>The GeoKnow Handbook</i>. 2015.
  short: J. Lehmann, S. Athanasiou, A. Both, L. Buehmann, A. Garcia-Rojas, G. Giannopoulos,
    D. Hladky, K. Hoeffner, J. Jay Le Grange, A.-C. Ngonga Ngomo, R. Pietzsch, R.
    Isele, M. Sherif, C. Stadler, M. Wauer, P. Westphal, The GeoKnow Handbook, 2015.
date_created: 2021-12-17T09:56:29Z
date_updated: 2023-08-16T09:42:50Z
keyword:
- 2015 group\_aksw sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:geoknow
  lehmann ngonga MOLE sherif simba dice hoeffner geoknow westphal buehmann
language:
- iso: eng
status: public
title: The GeoKnow Handbook
type: report
user_id: '67234'
year: '2015'
...
---
_id: '29023'
abstract:
- lang: eng
  text: It is widely accepted that food supply and quality are major problems in the
    21st century. Due to the growth of the world's population, there is a pressing
    need to improve the productivity of agricultural crops, which hinges on different
    factors such as geographical location, soil type, weather condition and particular
    attributes of the crops to plant. In many regions of the world, information about
    those factors is not readily accessible and dispersed across a multitude of different
    sources. One of those regions is Nepal, in which the lack of access to this knowledge
    poses a significant burden for agricultural planning and decision making. Making
    such knowledge more accessible can boot up a farmer's living standard and increase
    their competitiveness on national and global markets. In this article, we show
    how we converted several available, although not easily accessible, datasets to
    RDF, thereby lowering the barrier for data re-usage and integration. We describe
    the conversion, linking, and publication process as well as use cases, which can
    be implemented using the farming datasets in Nepal.
author:
- first_name: Suresh
  full_name: Pokharel, Suresh
  last_name: Pokharel
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
citation:
  ama: 'Pokharel S, Sherif M, Lehmann J. Ontology Based Data Access and Integration
    for Improving the Effectiveness of Farming in Nepal. In: <i>Proc. of the International
    Conference on Web Intelligence</i>. ; 2014.'
  apa: Pokharel, S., Sherif, M., &#38; Lehmann, J. (2014). Ontology Based Data Access
    and Integration for Improving the Effectiveness of Farming in Nepal. <i>Proc.
    of the International Conference on Web Intelligence</i>.
  bibtex: '@inproceedings{Pokharel_Sherif_Lehmann_2014, title={Ontology Based Data
    Access and Integration for Improving the Effectiveness of Farming in Nepal}, booktitle={Proc.
    of the International Conference on Web Intelligence}, author={Pokharel, Suresh
    and Sherif, Mohamed and Lehmann, Jens}, year={2014} }'
  chicago: Pokharel, Suresh, Mohamed Sherif, and Jens Lehmann. “Ontology Based Data
    Access and Integration for Improving the Effectiveness of Farming in Nepal.” In
    <i>Proc. of the International Conference on Web Intelligence</i>, 2014.
  ieee: S. Pokharel, M. Sherif, and J. Lehmann, “Ontology Based Data Access and Integration
    for Improving the Effectiveness of Farming in Nepal,” 2014.
  mla: Pokharel, Suresh, et al. “Ontology Based Data Access and Integration for Improving
    the Effectiveness of Farming in Nepal.” <i>Proc. of the International Conference
    on Web Intelligence</i>, 2014.
  short: 'S. Pokharel, M. Sherif, J. Lehmann, in: Proc. of the International Conference
    on Web Intelligence, 2014.'
date_created: 2021-12-17T09:59:51Z
date_updated: 2023-08-16T09:20:12Z
keyword:
- group\_aksw MOLE 2014 sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:geoknow
  topic\_geospatial lehmann sherif simba dice
language:
- iso: eng
publication: Proc. of the International Conference on Web Intelligence
status: public
title: Ontology Based Data Access and Integration for Improving the Effectiveness
  of Farming in Nepal
type: conference
user_id: '67234'
year: '2014'
...
---
_id: '29028'
abstract:
- lang: eng
  text: In the last couple of years the amount of structured open government data
    has increased significantly. Already now, citizens are able to leverage the advantages
    of open data through increased transparency and better opportunities to take part
    in governmental decision making processes. Our approach increases the interoperability
    of existing but distributed open governmental datasets by converting them to the
    RDF-based NLP Interchange Format (NIF). Furthermore, we integrate the converted
    data into a geodata store and present a user interface for querying this data
    via a keyword-based search. The language resource generated in this project is
    publicly available for download and via a dedicated SPARQL endpoint.
author:
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Sandro
  full_name: Coelho, Sandro
  last_name: Coelho
- first_name: Ricardo
  full_name: Usbeck, Ricardo
  last_name: Usbeck
- first_name: Sebastian
  full_name: Hellmann, Sebastian
  last_name: Hellmann
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
- first_name: Martin
  full_name: Brümmer, Martin
  last_name: Brümmer
- first_name: Andreas
  full_name: Both, Andreas
  last_name: Both
citation:
  ama: 'Sherif M, Coelho S, Usbeck R, et al. NIF4OGGD - NLP Interchange Format for
    Open German Governmental Data. In: <i>The 9th Edition of the Language Resources
    and Evaluation Conference, 26-31 May, Reykjavik, Iceland</i>. ; 2014.'
  apa: Sherif, M., Coelho, S., Usbeck, R., Hellmann, S., Lehmann, J., Brümmer, M.,
    &#38; Both, A. (2014). NIF4OGGD - NLP Interchange Format for Open German Governmental
    Data. <i>The 9th Edition of the Language Resources and Evaluation Conference,
    26-31 May, Reykjavik, Iceland</i>.
  bibtex: '@inproceedings{Sherif_Coelho_Usbeck_Hellmann_Lehmann_Brümmer_Both_2014,
    title={NIF4OGGD - NLP Interchange Format for Open German Governmental Data}, booktitle={The
    9th edition of the Language Resources and Evaluation Conference, 26-31 May, Reykjavik,
    Iceland}, author={Sherif, Mohamed and Coelho, Sandro and Usbeck, Ricardo and Hellmann,
    Sebastian and Lehmann, Jens and Brümmer, Martin and Both, Andreas}, year={2014}
    }'
  chicago: Sherif, Mohamed, Sandro Coelho, Ricardo Usbeck, Sebastian Hellmann, Jens
    Lehmann, Martin Brümmer, and Andreas Both. “NIF4OGGD - NLP Interchange Format
    for Open German Governmental Data.” In <i>The 9th Edition of the Language Resources
    and Evaluation Conference, 26-31 May, Reykjavik, Iceland</i>, 2014.
  ieee: M. Sherif <i>et al.</i>, “NIF4OGGD - NLP Interchange Format for Open German
    Governmental Data,” 2014.
  mla: Sherif, Mohamed, et al. “NIF4OGGD - NLP Interchange Format for Open German
    Governmental Data.” <i>The 9th Edition of the Language Resources and Evaluation
    Conference, 26-31 May, Reykjavik, Iceland</i>, 2014.
  short: 'M. Sherif, S. Coelho, R. Usbeck, S. Hellmann, J. Lehmann, M. Brümmer, A.
    Both, in: The 9th Edition of the Language Resources and Evaluation Conference,
    26-31 May, Reykjavik, Iceland, 2014.'
date_created: 2021-12-17T09:59:58Z
date_updated: 2023-08-16T09:19:54Z
keyword:
- 2014 dice simba sherif sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:geoknow
  hellmann kilt lehmann usbeck bruemmer nif4oggd group\_aksw kilt Lidmole MOLE
language:
- iso: eng
publication: The 9th edition of the Language Resources and Evaluation Conference,
  26-31 May, Reykjavik, Iceland
status: public
title: NIF4OGGD - NLP Interchange Format for Open German Governmental Data
type: conference
user_id: '67234'
year: '2014'
...
---
_id: '29022'
abstract:
- lang: eng
  text: The Linked Data Web has developed into a compendium of partly very large datasets.
    Devising efficient approaches to compute links between these datasets is thus
    central to achieve the vision behind the Data Web. Several unsupervised approaches
    have been developed to achieve this goal. Yet, so far, none of these approaches
    makes use of the replication of resources across several knowledge bases to improve
    the accuracy it achieves while linking. In this paper, we present Colibri, an
    iterative unsupervised approach for link discovery. Colibri allows discovering
    links between n datasets (n ≥ 2) while improving the quality of the instance data
    in these datasets. To this end, Colibri combines error detection and correction
    with unsupervised link discovery. We evaluate our approach on benchmark datasets
    with respect to the F-score itachieves. Our results suggest that Colibri can significantly
    improve the results of unsupervised machine-learning approaches for link discovery
    while correctly detecting erroneous resources.
author:
- 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: Klaus
  full_name: Lyko, Klaus
  last_name: Lyko
citation:
  ama: 'Ngonga Ngomo A-C, Sherif M, Lyko K. Unsupervised Link Discovery Through Knowledge
    Base Repair. In: <i>Extended Semantic Web Conference (ESWC 2014)</i>. ; 2014.'
  apa: Ngonga Ngomo, A.-C., Sherif, M., &#38; Lyko, K. (2014). Unsupervised Link Discovery
    Through Knowledge Base Repair. <i>Extended Semantic Web Conference (ESWC 2014)</i>.
  bibtex: '@inproceedings{Ngonga Ngomo_Sherif_Lyko_2014, title={Unsupervised Link
    Discovery Through Knowledge Base Repair}, booktitle={Extended Semantic Web Conference
    (ESWC 2014)}, author={Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Lyko,
    Klaus}, year={2014} }'
  chicago: Ngonga Ngomo, Axel-Cyrille, Mohamed Sherif, and Klaus Lyko. “Unsupervised
    Link Discovery Through Knowledge Base Repair.” In <i>Extended Semantic Web Conference
    (ESWC 2014)</i>, 2014.
  ieee: A.-C. Ngonga Ngomo, M. Sherif, and K. Lyko, “Unsupervised Link Discovery Through
    Knowledge Base Repair,” 2014.
  mla: Ngonga Ngomo, Axel-Cyrille, et al. “Unsupervised Link Discovery Through Knowledge
    Base Repair.” <i>Extended Semantic Web Conference (ESWC 2014)</i>, 2014.
  short: 'A.-C. Ngonga Ngomo, M. Sherif, K. Lyko, in: Extended Semantic Web Conference
    (ESWC 2014), 2014.'
date_created: 2021-12-17T09:57:25Z
date_updated: 2023-08-16T09:23:54Z
keyword:
- ngonga sherif lyko simba group\_aksw sys:relevantFor:infai sys:relevantFor:bis SIMBA
  DICE limes
language:
- iso: eng
publication: Extended Semantic Web Conference (ESWC 2014)
status: public
title: Unsupervised Link Discovery Through Knowledge Base Repair
type: conference
user_id: '67234'
year: '2014'
...
---
_id: '29034'
abstract:
- lang: eng
  text: In this paper we describe the Semantic Quran dataset, a multilingual RDF representation
    of translations of the Quran. The dataset was created by integrating data from
    two different semi-structured sources and aligned to an ontology designed to represent
    multilingual data from sources with a hierarchical structure. The resulting RDF
    data encompasses 43 different languages which belong to the most under-represented
    languages in the Linked Data Cloud, including Arabic, Amharic and Amazigh. We
    designed the dataset to be easily usable in natural-language processing applications
    with the goal of facilitating the development of knowledge extraction tools for
    these languages. In particular, the Semantic Quran is compatible with the Natural-Language
    Interchange Format and contains explicit morpho-syntactic information on the utilized
    terms. We present the ontology devised for structuring the data. We also provide
    the transformation rules implemented in our extraction framework. Finally, we
    detail the link creation process as well as possible usage scenarios for the Semantic
    Quran dataset.
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. Semantic Quran: A Multilingual Resource for Natural-Language
    Processing. <i>Semantic Web Journal</i>. 2014;XXX:1–5.'
  apa: 'Sherif, M., &#38; Ngonga Ngomo, A.-C. (2014). Semantic Quran: A Multilingual
    Resource for Natural-Language Processing. <i>Semantic Web Journal</i>, <i>XXX</i>,
    1–5.'
  bibtex: '@article{Sherif_Ngonga Ngomo_2014, title={Semantic Quran: A Multilingual
    Resource for Natural-Language Processing}, volume={XXX}, journal={Semantic Web
    Journal}, author={Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2014},
    pages={1–5} }'
  chicago: 'Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “Semantic Quran: A Multilingual
    Resource for Natural-Language Processing.” <i>Semantic Web Journal</i> XXX (2014):
    1–5.'
  ieee: 'M. Sherif and A.-C. Ngonga Ngomo, “Semantic Quran: A Multilingual Resource
    for Natural-Language Processing,” <i>Semantic Web Journal</i>, vol. XXX, pp. 1–5,
    2014.'
  mla: 'Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “Semantic Quran: A Multilingual
    Resource for Natural-Language Processing.” <i>Semantic Web Journal</i>, vol. XXX,
    2014, pp. 1–5.'
  short: M. Sherif, A.-C. Ngonga Ngomo, Semantic Web Journal XXX (2014) 1–5.
date_created: 2021-12-17T10:00:06Z
date_updated: 2023-08-16T09:36:14Z
keyword:
- group\_aksw SIMBA sys:relevantFor:infai sys:relevantFor:bis ngonga limes simba dice
  sherif 2014 limes semanticquran
language:
- iso: eng
page: 1–5
publication: Semantic Web Journal
status: public
title: 'Semantic Quran: A Multilingual Resource for Natural-Language Processing'
type: journal_article
user_id: '67234'
volume: XXX
year: '2014'
...
---
_id: '29017'
author:
- first_name: Jon
  full_name: Jay Le Grange, Jon
  last_name: Jay Le Grange
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
- first_name: Spiros
  full_name: Athanasiou, Spiros
  last_name: Athanasiou
- first_name: Alejandra
  full_name: Garcia Rojas, Alejandra
  last_name: Garcia Rojas
- first_name: Giorgos
  full_name: Giannopoulos, Giorgos
  last_name: Giannopoulos
- first_name: Daniel
  full_name: Hladky, Daniel
  last_name: Hladky
- first_name: Robert
  full_name: Isele, Robert
  last_name: Isele
- 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: Claus
  full_name: Stadler, Claus
  last_name: Stadler
- first_name: Matthias
  full_name: Wauer, Matthias
  last_name: Wauer
citation:
  ama: 'Jay Le Grange J, Lehmann J, Athanasiou S, et al. The GeoKnow Generator: Managing
    Geospatial Data in the Linked Data Web. In: <i>Proceedings of the Linking Geospatial
    Data Workshop</i>. ; 2014.'
  apa: 'Jay Le Grange, J., Lehmann, J., Athanasiou, S., Garcia Rojas, A., Giannopoulos,
    G., Hladky, D., Isele, R., Ngonga Ngomo, A.-C., Sherif, M., Stadler, C., &#38;
    Wauer, M. (2014). The GeoKnow Generator: Managing Geospatial Data in the Linked
    Data Web. <i>Proceedings of the Linking Geospatial Data Workshop</i>.'
  bibtex: '@inproceedings{Jay Le Grange_Lehmann_Athanasiou_Garcia Rojas_Giannopoulos_Hladky_Isele_Ngonga
    Ngomo_Sherif_Stadler_et al._2014, title={The GeoKnow Generator: Managing Geospatial
    Data in the Linked Data Web}, booktitle={Proceedings of the Linking Geospatial
    Data Workshop}, author={Jay Le Grange, Jon and Lehmann, Jens and Athanasiou, Spiros
    and Garcia Rojas, Alejandra and Giannopoulos, Giorgos and Hladky, Daniel and Isele,
    Robert and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Stadler, Claus and
    et al.}, year={2014} }'
  chicago: 'Jay Le Grange, Jon, Jens Lehmann, Spiros Athanasiou, Alejandra Garcia
    Rojas, Giorgos Giannopoulos, Daniel Hladky, Robert Isele, et al. “The GeoKnow
    Generator: Managing Geospatial Data in the Linked Data Web.” In <i>Proceedings
    of the Linking Geospatial Data Workshop</i>, 2014.'
  ieee: 'J. Jay Le Grange <i>et al.</i>, “The GeoKnow Generator: Managing Geospatial
    Data in the Linked Data Web,” 2014.'
  mla: 'Jay Le Grange, Jon, et al. “The GeoKnow Generator: Managing Geospatial Data
    in the Linked Data Web.” <i>Proceedings of the Linking Geospatial Data Workshop</i>,
    2014.'
  short: 'J. Jay Le Grange, J. Lehmann, S. Athanasiou, A. Garcia Rojas, G. Giannopoulos,
    D. Hladky, R. Isele, A.-C. Ngonga Ngomo, M. Sherif, C. Stadler, M. Wauer, in:
    Proceedings of the Linking Geospatial Data Workshop, 2014.'
date_created: 2021-12-17T09:55:59Z
date_updated: 2023-08-16T09:36:34Z
keyword:
- 2014 group\_aksw group\_mole mole ngonga lehmann sherif topic\_Lifecycle sys:relevantFor:infai
  sys:relevantFor:bis sys:relevantFor:lod2 sys:relevantFor:geoknow geoknow lod lod2page
  peer-reviewed MOLE simba dice wauer stadler
language:
- iso: eng
publication: Proceedings of the Linking Geospatial Data Workshop
status: public
title: 'The GeoKnow Generator: Managing Geospatial Data in the Linked Data Web'
type: conference
user_id: '67234'
year: '2014'
...
---
_id: '29036'
abstract:
- lang: eng
  text: The improvement of public health is one of the main indicators for societal
    progress. Statistical data for monitoring public health is highly relevant for
    a number of sectors, such as research (e.g. in the life sciences or economy),
    policy making, health care, pharmaceutical industry, insurances etc. Such data
    is meanwhile available even on a global scale, e.g. in the Global Health Observatory
    (GHO) of the United Nations's World Health Organization (WHO). GHO comprises more
    than 50 different datasets, it covers all 198 WHO member countries and is updated
    as more recent or revised data becomes available or when there are changes to
    the methodology being used. However, this data is only accessible via complex
    spreadsheets and, therefore, queries over the 50 different datasets as well as
    combinations with other datasets are very tedious and require a significant amount
    of manual work. By making the data available as RDF, we lower the barrier for
    data re-use and integration. In this article, we describe the conversion and publication
    process as well as use cases, which can be implemented using the GHO data.
author:
- first_name: Amrapali
  full_name: Zaveri, Amrapali
  last_name: Zaveri
- first_name: Jens
  full_name: Lehmann, Jens
  last_name: Lehmann
- first_name: Sören
  full_name: Auer, Sören
  last_name: Auer
- first_name: Mofeed
  full_name: M. Hassan, Mofeed
  last_name: M. Hassan
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Michael
  full_name: Martin, Michael
  last_name: Martin
citation:
  ama: Zaveri A, Lehmann J, Auer S, M. Hassan M, Sherif M, Martin M. Publishing and
    Interlinking the Global Health Observatory Dataset. <i>Semantic Web Journal</i>.
    2013;Special Call for Linked Dataset descriptions(3):315–322.
  apa: Zaveri, A., Lehmann, J., Auer, S., M. Hassan, M., Sherif, M., &#38; Martin,
    M. (2013). Publishing and Interlinking the Global Health Observatory Dataset.
    <i>Semantic Web Journal</i>, <i>Special Call for Linked Dataset descriptions</i>(3),
    315–322.
  bibtex: '@article{Zaveri_Lehmann_Auer_M. Hassan_Sherif_Martin_2013, title={Publishing
    and Interlinking the Global Health Observatory Dataset}, volume={Special Call
    for Linked Dataset descriptions}, number={3}, journal={Semantic Web Journal},
    author={Zaveri, Amrapali and Lehmann, Jens and Auer, Sören and M. Hassan, Mofeed
    and Sherif, Mohamed and Martin, Michael}, year={2013}, pages={315–322} }'
  chicago: 'Zaveri, Amrapali, Jens Lehmann, Sören Auer, Mofeed M. Hassan, Mohamed
    Sherif, and Michael Martin. “Publishing and Interlinking the Global Health Observatory
    Dataset.” <i>Semantic Web Journal</i> Special Call for Linked Dataset descriptions,
    no. 3 (2013): 315–322.'
  ieee: A. Zaveri, J. Lehmann, S. Auer, M. M. Hassan, M. Sherif, and M. Martin, “Publishing
    and Interlinking the Global Health Observatory Dataset,” <i>Semantic Web Journal</i>,
    vol. Special Call for Linked Dataset descriptions, no. 3, pp. 315–322, 2013.
  mla: Zaveri, Amrapali, et al. “Publishing and Interlinking the Global Health Observatory
    Dataset.” <i>Semantic Web Journal</i>, vol. Special Call for Linked Dataset descriptions,
    no. 3, 2013, pp. 315–322.
  short: A. Zaveri, J. Lehmann, S. Auer, M. M. Hassan, M. Sherif, M. Martin, Semantic
    Web Journal Special Call for Linked Dataset descriptions (2013) 315–322.
date_created: 2021-12-17T10:04:46Z
date_updated: 2023-08-16T09:19:33Z
issue: '3'
keyword:
- 2013 MOLE group\_aksw zaveri martin lehmann auer hassan sherif simba dice sys:relevantFor:infai
  sys:relevantFor:bis sys:relevantFor:lod2 lod2page peer-reviewed gho
language:
- iso: eng
page: 315–322
publication: Semantic Web Journal
status: public
title: Publishing and Interlinking the Global Health Observatory Dataset
type: journal_article
user_id: '67234'
volume: Special Call for Linked Dataset descriptions
year: '2013'
...