2022

• Reimann, L., Kniesel-Wünsche, G. (2022) “Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping”, In New Ideas and Emerging Results (ICSE-NIER’22).

  BibTeXEndNoteDOI

  @article{reimann2022improving,
  author = {Reimann, Lars and Kniesel-Wünsche, Günter},
  journal = {In New Ideas and Emerging Results (ICSE-NIER’22)},
  keywords = {simpleml},
  month = {may},
  title = {Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping},
  year = 2022
  }
  

  %0 Journal Article
  %1 reimann2022improving
  %A Reimann, Lars
  %A Kniesel-Wünsche, Günter
  %D 2022
  %J In New Ideas and Emerging Results (ICSE-NIER’22)
  %R https://doi.org/10.1145/3510455.3512789
  %T Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping
  

• Gurtovoy, D., Gottschalk, S. (2022) “Linking Streets in OpenStreetMap to Persons in Wikidata”, The Web Conference.

  BibTeXEndNote

  @article{gurtovoy2022linking,
  author = {Gurtovoy, Daria and Gottschalk, Simon},
  journal = {The Web Conference},
  keywords = {myown simpleml},
  title = {Linking Streets in OpenStreetMap to Persons in Wikidata},
  year = 2022
  }
  

  %0 Journal Article
  %1 gurtovoy2022linking
  %A Gurtovoy, Daria
  %A Gottschalk, Simon
  %D 2022
  %J The Web Conference
  %T Linking Streets in OpenStreetMap to Persons in Wikidata
  

• Gottschalk, S., Demidova, E. (2022) “Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles”, Semantic Web Journal, Vol 13/3, Special Issue on Deep Learning and Knowledge Graphs.

  BibTeXEndNote

  @article{gottschalk2022tab2kg,
  author = {Gottschalk, Simon and Demidova, Elena},
  journal = {Semantic Web Journal},
  keywords = {myown simpleml},
  title = {Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles},
  volume = {Vol 13/3, Special Issue on Deep Learning and Knowledge Graphs},
  year = 2022
  }
  

  %0 Journal Article
  %1 gottschalk2022tab2kg
  %A Gottschalk, Simon
  %A Demidova, Elena
  %D 2022
  %J Semantic Web Journal
  %T Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles
  %V Vol 13/3, Special Issue on Deep Learning and Knowledge Graphs
  

• Demidova, E., Dsouza, A., Gottschalk, S., Tempelmeier, N., Yu, R. (2022) “Creating Knowledge Graphs for Geographic Data on the Web”, SIGWEB Newsletter, (Winter), available: https://doi.org/10.1145/3522598.3522602.

  AbstractURLBibTeXEndNoteDOI

  Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.

  @article{10.1145/3522598.3522602,
  abstract = {Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.},
  address = {New York, NY, USA},
  author = {Demidova, Elena and Dsouza, Alishiba and Gottschalk, Simon and Tempelmeier, Nicolas and Yu, Ran},
  journal = {SIGWEB Newsletter},
  keywords = {myown simpleml},
  month = {mar},
  number = {Winter},
  publisher = {Association for Computing Machinery},
  title = {Creating Knowledge Graphs for Geographic Data on the Web},
  year = 2022
  }
  

  %0 Journal Article
  %1 10.1145/3522598.3522602
  %A Demidova, Elena
  %A Dsouza, Alishiba
  %A Gottschalk, Simon
  %A Tempelmeier, Nicolas
  %A Yu, Ran
  %C New York, NY, USA
  %D 2022
  %I Association for Computing Machinery
  %J SIGWEB Newsletter
  %N Winter
  %R 10.1145/3522598.3522602
  %T Creating Knowledge Graphs for Geographic Data on the Web
  %U https://doi.org/10.1145/3522598.3522602
  %X Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.
  

2021

• Latif, S., Agarwal, S., Gottschalk, S., Chrosch, C., Feit, F., Jahn, J., Braun, T., Tchenko, Y.C., Demidova, E., Beck, F. (2021) “Visually Connecting Historical Figures Through Event Knowledge Graphs”, in 2021 IEEE Visualization Conference (VIS), 156–160, available: https://ieeexplore.ieee.org/document/9623313/.

  AbstractURLBibTeXEndNoteDOI

  Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user’s query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.

  @inproceedings{9623313,
  abstract = {Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user’s query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.},
  author = {Latif, Shahid and Agarwal, Shivam and Gottschalk, Simon and Chrosch, Carina and Feit, Felix and Jahn, Johannes and Braun, Tobias and Tchenko, Yanick Christian and Demidova, Elena and Beck, Fabian},
  booktitle = {2021 IEEE Visualization Conference (VIS)},
  keywords = {bpa myown simpleml worldkg},
  month = {oct},
  pages = {156-160},
  title = {Visually Connecting Historical Figures Through Event Knowledge Graphs},
  year = 2021
  }
  

  %0 Conference Paper
  %1 9623313
  %A Latif, Shahid
  %A Agarwal, Shivam
  %A Gottschalk, Simon
  %A Chrosch, Carina
  %A Feit, Felix
  %A Jahn, Johannes
  %A Braun, Tobias
  %A Tchenko, Yanick Christian
  %A Demidova, Elena
  %A Beck, Fabian
  %B 2021 IEEE Visualization Conference (VIS)
  %D 2021
  %P 156-160
  %R 10.1109/VIS49827.2021.9623313
  %T Visually Connecting Historical Figures Through Event Knowledge Graphs
  %U https://ieeexplore.ieee.org/document/9623313/
  %X Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user’s query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.
  

• Tempelmeier, N., Gottschalk, S., Demidova, E. (2021) “GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on a World Scale”, in In Proc. Of the 30th ACM International Conference on Information and Knowledge Management (ACM CIKM 2021).

  BibTeXEndNote

  @inproceedings{tempelmeier2021geovectors,
  author = {Tempelmeier, Nicolas and Gottschalk, Simon and Demidova, Elena},
  booktitle = {In Proc. of the 30th ACM International Conference on Information and Knowledge Management (ACM CIKM 2021)},
  keywords = {myown simpleml},
  title = {GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on a World Scale},
  year = 2021
  }
  

  %0 Conference Paper
  %1 tempelmeier2021geovectors
  %A Tempelmeier, Nicolas
  %A Gottschalk, Simon
  %A Demidova, Elena
  %B In Proc. of the 30th ACM International Conference on Information and Knowledge Management (ACM CIKM 2021)
  %D 2021
  %T GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on a World Scale
  

• Grubenmann, T., Lehmann, J. (2021) “Geolog: Scalable Logic Programming on Spatial Data”, in 37th International Conference on Logic Programming (ICLP), available: https://arxiv.org/pdf/2109.08295.pdf.

  URLBibTeXEndNote

  @inproceedings{grubenmann2021geolog,
  author = {Grubenmann, Tobias and Lehmann, Jens},
  booktitle = {37th International Conference on Logic Programming (ICLP)},
  keywords = {simpleml},
  title = {Geolog: Scalable Logic Programming on Spatial Data},
  year = 2021
  }
  

  %0 Conference Paper
  %1 grubenmann2021geolog
  %A Grubenmann, Tobias
  %A Lehmann, Jens
  %B 37th International Conference on Logic Programming (ICLP)
  %D 2021
  %T Geolog: Scalable Logic Programming on Spatial Data
  %U https://arxiv.org/pdf/2109.08295.pdf
  

2020

• Hube, C., Idahl, M., Fetahu, B. (2020) “Debiasing Word Embeddings from Sentiment Associations in Names.”, in Caverlee, J., Hu, X. (Ben), Lalmas, M. and Wang, W., eds., WSDM, ACM, 259–267, available: http://dblp.uni-trier.de/db/conf/wsdm/wsdm2020.html#HubeIF20.

  URLBibTeXEndNote

  @inproceedings{conf/wsdm/HubeIF20,
  author = {Hube, Christoph and Idahl, Maximilian and Fetahu, Besnik},
  booktitle = {WSDM},
  crossref = {conf/wsdm/2020},
  editor = {Caverlee, James and Hu, Xia (Ben) and Lalmas, Mounia and Wang, Wei},
  keywords = {simpleml},
  pages = {259-267},
  publisher = {ACM},
  title = {Debiasing Word Embeddings from Sentiment Associations in Names.},
  year = 2020
  }
  

  %0 Conference Paper
  %1 conf/wsdm/HubeIF20
  %A Hube, Christoph
  %A Idahl, Maximilian
  %A Fetahu, Besnik
  %B WSDM
  %D 2020
  %E Caverlee, James
  %E Hu, Xia (Ben)
  %E Lalmas, Mounia
  %E Wang, Wei
  %I ACM
  %P 259-267
  %T Debiasing Word Embeddings from Sentiment Associations in Names.
  %U http://dblp.uni-trier.de/db/conf/wsdm/wsdm2020.html#HubeIF20
  %@ 978-1-4503-6822-3
  

• Reimann, L., Kniesel-Wünsche, G. (2020) “Achieving guidance in applied machine learning through software engineering techniques.”, in Aguiar, A., Chiba, S. and Boix, E.G., eds., Programming, ACM, 7–12, available: http://dblp.uni-trier.de/db/conf/programming/programming2020.html#ReimannK20.

  URLBibTeXEndNote

  @inproceedings{conf/programming/ReimannK20,
  author = {Reimann, Lars and Kniesel-Wünsche, Günter},
  booktitle = {Programming},
  crossref = {conf/programming/2020},
  editor = {Aguiar, Ademar and Chiba, Shigeru and Boix, Elisa Gonzalez},
  keywords = {simpleml},
  pages = {7-12},
  publisher = {ACM},
  title = {Achieving guidance in applied machine learning through software engineering techniques.},
  year = 2020
  }
  

  %0 Conference Paper
  %1 conf/programming/ReimannK20
  %A Reimann, Lars
  %A Kniesel-Wünsche, Günter
  %B Programming
  %D 2020
  %E Aguiar, Ademar
  %E Chiba, Shigeru
  %E Boix, Elisa Gonzalez
  %I ACM
  %P 7-12
  %T Achieving guidance in applied machine learning through software engineering techniques.
  %U http://dblp.uni-trier.de/db/conf/programming/programming2020.html#ReimannK20
  %@ 978-1-4503-7507-8
  

• Gottschalk, S., Demidova, E. (2020) “EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph.”, in ESWC (Satellite Events), Lecture Notes in Computer Science, Springer, 91–97, available: http://dblp.uni-trier.de/db/conf/esws/eswc2020s.html#GottschalkD20.

  URLBibTeXEndNote

  @inproceedings{conf/esws/GottschalkD20,
  author = {Gottschalk, Simon and Demidova, Elena},
  booktitle = {ESWC (Satellite Events)},
  crossref = {conf/esws/2020s},
  keywords = {myown simpleml},
  pages = {91-97},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph.},
  volume = 12124,
  year = 2020
  }
  

  %0 Conference Paper
  %1 conf/esws/GottschalkD20
  %A Gottschalk, Simon
  %A Demidova, Elena
  %B ESWC (Satellite Events)
  %D 2020
  %I Springer
  %P 91-97
  %T EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph.
  %U http://dblp.uni-trier.de/db/conf/esws/eswc2020s.html#GottschalkD20
  %V 12124
  %@ 978-3-030-62327-2
  

2019

• Kassawat, F., Chaudhuri, D., Lehmann, J. (2019) “Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning.”, in Extended Semantic Web Conference (ESWC), Lecture Notes in Computer Science, Springer, 225–239, available: http://dblp.uni-trier.de/db/conf/esws/eswc2019.html#KassawatC019.

  URLBibTeXEndNote

  @inproceedings{conf/esws/KassawatC019,
  author = {Kassawat, Firas and Chaudhuri, Debanjan and Lehmann, Jens},
  booktitle = {Extended Semantic Web Conference (ESWC)},
  crossref = {conf/esws/2019},
  keywords = {simpleml},
  pages = {225-239},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning.},
  volume = 11503,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/esws/KassawatC019
  %A Kassawat, Firas
  %A Chaudhuri, Debanjan
  %A Lehmann, Jens
  %B Extended Semantic Web Conference (ESWC)
  %D 2019
  %I Springer
  %P 225-239
  %T Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning.
  %U http://dblp.uni-trier.de/db/conf/esws/eswc2019.html#KassawatC019
  %V 11503
  %@ 978-3-030-21348-0
  

• Gottschalk, S., Demidova, E. (2019) “HapPenIng: Happen, Predict, Infer --- Event Series Completion in a Knowledge Graph”, in Proc. Of the 18th International Semantic Web Conference (ISWC 2019), Springer.

  AbstractBibTeXEndNote

  Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.

  @inproceedings{gottschalk2019happening,
  abstract = {Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.},
  author = {Gottschalk, Simon and Demidova, Elena},
  booktitle = {Proc. of the 18th International Semantic Web Conference (ISWC 2019)},
  keywords = {cleopatra myown simpleml},
  publisher = {Springer},
  title = {HapPenIng: Happen, Predict, Infer --- Event Series Completion in a Knowledge Graph},
  year = 2019
  }
  

  %0 Conference Paper
  %1 gottschalk2019happening
  %A Gottschalk, Simon
  %A Demidova, Elena
  %B Proc. of the 18th International Semantic Web Conference (ISWC 2019)
  %D 2019
  %I Springer
  %T HapPenIng: Happen, Predict, Infer --- Event Series Completion in a Knowledge Graph
  %X Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: 1) prediction of sub-event relations, and 2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52 percentage points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
  

• Gottschalk, S., Tempelmeier, N., Kniesel, G., Iosifidis, V., Fetahu, B., Demidova, E. (2019) “Simple-ML: Towards a Framework for Semantic Data Analytics Workflows”, in Proc. Of SEMANTiCS 2019.

  BibTeXEndNote

  @inproceedings{gottschalk2019simpleml,
  author = {Gottschalk, Simon and Tempelmeier, Nicolas and Kniesel, Günter and Iosifidis, Vasileios and Fetahu, Besnik and Demidova, Elena},
  booktitle = {Proc. of SEMANTiCS 2019},
  keywords = {myown simple-ml simpleml},
  title = {Simple-ML: Towards a Framework for Semantic Data Analytics Workflows},
  year = 2019
  }
  

  %0 Conference Paper
  %1 gottschalk2019simpleml
  %A Gottschalk, Simon
  %A Tempelmeier, Nicolas
  %A Kniesel, Günter
  %A Iosifidis, Vasileios
  %A Fetahu, Besnik
  %A Demidova, Elena
  %B Proc. of SEMANTiCS 2019
  %D 2019
  %T Simple-ML: Towards a Framework for Semantic Data Analytics Workflows
  

• Redi, M., Fetahu, B., Morgan, J., Taraborelli, D. (2019) “Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia’s Verifiability”, in In Proceedings of The Web Conference (WWW), San Francisco, USA.

  BibTeXEndNoteDOI

  @inproceedings{redi2019citation,
  author = {Redi, Miriam and Fetahu, Besnik and Morgan, Jonathan and Taraborelli, Dario},
  booktitle = {In Proceedings of The Web Conference (WWW), San Francisco, USA.},
  keywords = {sys:relevantfor:l3s alexandria myown simpleml},
  title = {Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability},
  year = 2019
  }
  

  %0 Conference Paper
  %1 redi2019citation
  %A Redi, Miriam
  %A Fetahu, Besnik
  %A Morgan, Jonathan
  %A Taraborelli, Dario
  %B In Proceedings of The Web Conference (WWW), San Francisco, USA.
  %D 2019
  %R 10.1145/3308558.3313618
  %T Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability
  

• Fetahu, B., Anand, A., Koutraki, M. (2019) “TableNet: A Knowledge Graph of Interlinked Wikipedia Tables”, in In Proceedings of The Web Conference (WWW), San Francisco, USA.

  BibTeXEndNoteDOI

  @inproceedings{fetahu2019tablenet,
  author = {Fetahu, Besnik and Anand, Avishek and Koutraki, Maria},
  booktitle = {In Proceedings of The Web Conference (WWW), San Francisco, USA.},
  keywords = {sys:relevantfor:l3s afel alexandria myown simpleml},
  title = {TableNet: A Knowledge Graph of Interlinked Wikipedia Tables},
  year = 2019
  }
  

  %0 Conference Paper
  %1 fetahu2019tablenet
  %A Fetahu, Besnik
  %A Anand, Avishek
  %A Koutraki, Maria
  %B In Proceedings of The Web Conference (WWW), San Francisco, USA.
  %D 2019
  %R 10.1145/3308558.3313629
  %T TableNet: A Knowledge Graph of Interlinked Wikipedia Tables
  

• Hube, C., Fetahu, B. (2019) “Neural Based Statement Classification for Biased Language”, in 12th ACM International Conference on Web Search and Data Mining (WSDM).

  BibTeXEndNote

  @inproceedings{hube2019neural,
  author = {Hube, Christoph and Fetahu, Besnik},
  booktitle = {12th ACM International Conference on Web Search and Data Mining (WSDM)},
  keywords = {sys:relevantfor:l3s afel alexandria bias myown nlp simpleml},
  title = {Neural Based Statement Classification for Biased Language},
  year = 2019
  }
  

  %0 Conference Paper
  %1 hube2019neural
  %A Hube, Christoph
  %A Fetahu, Besnik
  %B 12th ACM International Conference on Web Search and Data Mining (WSDM)
  %D 2019
  %T Neural Based Statement Classification for Biased Language