@inproceedings{31806,
  abstract     = {{The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds}},
  author       = {{Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}},
  keywords     = {{2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba}},
  location     = {{Barcelona (Spain)}},
  title        = {{{ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}}},
  doi          = {{10.1145/3511095.3531287}},
  year         = {{2022}},
}

@article{29004,
  abstract     = {{The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well. Solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. The current version of the LIMES framework is the product of seven years of research on these two challenges. A series of machine learning techniques and efficient computation approaches were developed and integrated into this framework to address the link discovery problem. The framework combines these diverse algorithms within a generic and extensible architecture. In this article, we give an overview of version 1.7.4 of the open-source release of the framework. In particular, we focus on an overview of the architecture of the framework, an intuition of its inner workings and a brief overview of the approaches it contains. Some descriptions of the applications within which the framework was used complete the paper. Our framework is open-source and available under a GNU license at https: //github.com/dice-group/LIMES together with a user manual and a developer manual.}},
  author       = {{Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Georgala, Kleanthi and Hassan, Mofeed and Dreßler, Kevin and Lyko, Klaus and Obraczka, Daniel and Soru, Tommaso}},
  journal      = {{KI - K{\"u}nstliche Intelligenz, German Journal of Artificial Intelligence - Organ des Fachbereichs "Künstliche Intelligenz" der Gesellschaft für Informatik e.V.}},
  keywords     = {{2021 dice simba sherif limes ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limbo opal georgala kevin slipo sage}},
  publisher    = {{Springer}},
  title        = {{{LIMES - A Framework for Link Discovery on the Semantic Web}}},
  doi          = {{10.1007/s13218-021-00713-x}},
  year         = {{2021}},
}

@inproceedings{25340,
  author       = {{Ahmed Sherif, Mohamed and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 15th International Workshop on Ontology Matching co-located with the 19th International Semantic Web Conference {(ISWC} 2020), Virtual conference (originally planned to be in Athens, Greece), November 2, 2020}},
  editor       = {{Shvaiko, Pavel and Euzenat, Jérôme and Jimenez-Ruiz, Ernesto and Hassanzadeh, Oktie and Trojahn, Cássia}},
  pages        = {{48--59}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{LIGON - link discovery with noisy oracles}}},
  volume       = {{2788}},
  year         = {{2020}},
}

@inproceedings{25382,
  author       = {{Ahmed Sherif, Mohamed and Svetlana, Pestryakova and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the {ISWC} 2019 Satellite Tracks (Posters {\&} Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference {(ISWC} 2019), Auckland, New Zealand, October 26-30, 2019}},
  editor       = {{Carmen Suárez-Figueroa, Mari and Cheng, Gong and Lisa Gentile, Anna and Guéret, Christophe and Maria Keet, C. and Bernstein, Abraham}},
  pages        = {{205--208}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{LimesWebUI - Link Discovery Made Simple}}},
  volume       = {{2456}},
  year         = {{2019}},
}

@inproceedings{29011,
  abstract     = {{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       = {{Sherif, Mohamed and Pestryakova, Svetlana and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{18th International Semantic Web Conference (ISWC 2019)}},
  keywords     = {{2019 sys:relevantFor:infai group\_aksw simba sherif kevin ngonga Svetlana slipo limes dice sage limbo opal}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{LimesWebUI – Link Discovery Made Simple}}},
  year         = {{2019}},
}

@article{26544,
  author       = {{Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{Semantic Web}},
  number       = {{2}},
  pages        = {{185--196}},
  title        = {{{On the efficient execution of bounded Jaro-Winkler distances}}},
  doi          = {{10.3233/SW-150209}},
  volume       = {{8}},
  year         = {{2017}},
}

@inproceedings{26548,
  author       = {{Ahmed Sherif, Mohamed and Dreßler, Kevin and Smeros, Panayiotis and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, {USA}}},
  editor       = {{P. Singh, Satinder and Markovitch, Shaul}},
  pages        = {{175--181}},
  publisher    = {{{AAAI} Press}},
  title        = {{{Radon - Rapid Discovery of Topological Relations}}},
  year         = {{2017}},
}

@inproceedings{26573,
  author       = {{Dreßler, Kevin and Ahmed Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 12th International Workshop on Ontology Matching co-located with the 16th International Semantic Web Conference {(ISWC} 2017), Vienna, Austria, October 21, 2017}},
  editor       = {{Shvaiko, Pavel and Euzenat, Jérôme and Jiménez-Ruiz, Ernesto and Cheatham, Michelle and Hassanzadeh, Oktie}},
  pages        = {{178--184}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Radon results for OAEI 2017}}},
  volume       = {{2032}},
  year         = {{2017}},
}

@inproceedings{29018,
  author       = {{Sherif, Mohamed and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of Ontology Matching Workshop 2017}},
  keywords     = {{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}},
  title        = {{{RADON results for OAEI 2017}}},
  year         = {{2017}},
}

@inproceedings{29030,
  abstract     = {{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       = {{Sherif, Mohamed and Dreßler, Kevin and Smeros, Panayiotis and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)}},
  keywords     = {{radon sherif limes projecthobbit hobbit geiser group\_aksw SIMBA DICE sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:leds leds ngonga bioasq kevin}},
  title        = {{{RADON - Rapid Discovery of Topological Relations}}},
  year         = {{2017}},
}

@article{26486,
  author       = {{Ahmed Sherif, Mohamed and Dreßler, Kevin and Smeros, Panayiotis and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{Annex: Radon - Rapid Discovery of Topological Relations}}},
  volume       = {{abs/1611.06128}},
  year         = {{2016}},
}

@inproceedings{25504,
  author       = {{Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 9th International Workshop on Ontology Matching collocated with the 13th International Semantic Web Conference {(ISWC} 2014), Riva del Garda, Trentino, Italy, October 20, 2014}},
  editor       = {{Shvaiko, Pavel and Euzenat, Jérôme and Mao, Ming and Jiménez-Ruiz, Ernesto and Li, Juanzi and Ngonga, Axel}},
  pages        = {{37--48}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Time-efficient execution of bounded Jaro-Winkler distances}}},
  volume       = {{1317}},
  year         = {{2014}},
}