@article{25357,
  author       = {{Zhang, Xinyue and Wang, Meng and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille and Qi, Guilin and Wang, Haofen}},
  journal      = {{CoRR}},
  title        = {{{Revealing Secrets in SPARQL Session Level}}},
  volume       = {{abs/2009.06625}},
  year         = {{2020}},
}

@article{25358,
  author       = {{Moussallem, Diego and Gnaneshwar, Dwaraknath and Castro Ferreira, Thiago and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{NABU - Multilingual Graph-based Neural RDF Verbalizer}}},
  volume       = {{abs/2009.07728}},
  year         = {{2020}},
}

@article{25359,
  author       = {{Ali, Waqas and Saleem, Muhammad and Yao, Bin and Hogan, Aidan and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey}}},
  volume       = {{abs/2009.10331}},
  year         = {{2020}},
}

@article{25360,
  author       = {{Mihindukulasooriya, Nandana and Dubey, Mohnish and Gliozzo, Alfio and Lehmann, Jens and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}},
  journal      = {{CoRR}},
  title        = {{{SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge}}},
  volume       = {{abs/2012.00555}},
  year         = {{2020}},
}

@inproceedings{20141,
  author       = {{Heindorf, Stefan and Scholten, Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin}},
  booktitle    = {{Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020)}},
  pages        = {{3023--3030}},
  title        = {{{CauseNet: Towards a Causality Graph Extracted from the Web}}},
  doi          = {{10.1145/3340531.3412763}},
  year         = {{2020}},
}

@inbook{29042,
  author       = {{Röder, Michael and Sherif, Mohamed and Saleem, Muhammad and Conrads, Felix and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges}},
  editor       = {{Tiddi, Ilaria and Lécué, Freddy and Hitzler, Pascal}},
  keywords     = {{dice group_aksw roeder sherif saleem fconrads ngonga}},
  pages        = {{73--97}},
  publisher    = {{IOS Press}},
  title        = {{{Benchmarking the Lifecycle of Knowledge Graphs}}},
  doi          = {{10.3233/SSW200012}},
  year         = {{2020}},
}

@inproceedings{29009,
  abstract     = {{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       = {{Georgala, Kleanthi and Röder, Michael and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of Ontology Matching Workshop 2020}},
  keywords     = {{2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal roeder georgala}},
  title        = {{{Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy}}},
  year         = {{2020}},
}

@inproceedings{29010,
  abstract     = {{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       = {{Sherif, Mohamed and Dreßler}, Kevin and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of Ontology Matching Workshop 2020}},
  keywords     = {{2020 dice simba sherif ligon ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal kevin}},
  title        = {{{LIGON – Link Discovery with Noisy Oracles}}},
  year         = {{2020}},
}

@article{29039,
  author       = {{Bigerl, Alexander and Conrads, Felix and Behning, Charlotte and Sherif, Mohamed and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{The Semantic Web -- ISWC 2020}},
  keywords     = {{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}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Tentris – A Tensor-Based Triple Store}}},
  year         = {{2020}},
}

@inproceedings{29007,
  abstract     = {{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       = {{Georgala, Kleanthi and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of Ontology Matching Workshop 2020}},
  keywords     = {{2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal georgala}},
  title        = {{{LIGER – Link Discovery with Partial Recall}}},
  year         = {{2020}},
}

@inbook{55066,
  author       = {{Moussallem, Diego and Speck, René and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges}},
  pages        = {{213–241}},
  publisher    = {{IOS Press}},
  title        = {{{Generating explanations in natural language from knowledge graphs}}},
  year         = {{2020}},
}

@article{25361,
  author       = {{Mehmood, Qaiser and Saleem, Muhammad and Sahay, Ratnesh and Ngonga Ngomo, Axel-Cyrille and d'Aquin, Mathieu}},
  journal      = {{{IEEE} Access}},
  pages        = {{101031--101045}},
  title        = {{{QPPDs: Querying Property Paths Over Distributed RDF Datasets}}},
  doi          = {{10.1109/ACCESS.2019.2930416}},
  volume       = {{7}},
  year         = {{2019}},
}

@article{25362,
  author       = {{Usbeck, Ricardo and Röder, Michael and Hoffmann, Michael and Conrads, Felix and Huthmann, Jonathan and Ngonga Ngomo, Axel-Cyrille and Demmler, Christian and Unger, Christina}},
  journal      = {{Semantic Web}},
  number       = {{2}},
  pages        = {{293--304}},
  title        = {{{Benchmarking question answering systems}}},
  doi          = {{10.3233/SW-180312}},
  volume       = {{10}},
  year         = {{2019}},
}

@article{25363,
  author       = {{Rula, Anisa and Palmonari, Matteo and Rubinacci, Simone and Ngonga Ngomo, Axel-Cyrille and Lehmann, Jens and Maurino, Andrea and Esteves, Diego}},
  journal      = {{J. Web Semant.}},
  pages        = {{72--86}},
  title        = {{{TISCO: Temporal scoping of facts}}},
  doi          = {{10.1016/j.websem.2018.09.002}},
  volume       = {{54}},
  year         = {{2019}},
}

@article{25364,
  author       = {{Speck, René and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{J. Web Semant.}},
  pages        = {{102--107}},
  title        = {{{Leopard - A baseline approach to attribute prediction and validation for knowledge graph population}}},
  doi          = {{10.1016/j.websem.2018.12.006}},
  volume       = {{55}},
  year         = {{2019}},
}

@inproceedings{25365,
  author       = {{Jalota, Rricha and Trivedi, Priyansh and Maheshwari, Gaurav and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}},
  booktitle    = {{Chatbot Research and Design - Third International Workshop, {CONVERSATIONS} 2019, Amsterdam, The Netherlands, November 19-20, 2019, Revised Selected Papers}},
  editor       = {{Følstad, Asbjørn and B. Araujo, Theo and Papadopoulos, Symeon and Lai-Chong Law, Effie and Granmo, Ole-Christoffer and Luger, Ewa and Bae Brandtzæg, Petter}},
  pages        = {{19--33}},
  publisher    = {{Springer}},
  title        = {{{An Approach for Ex-Post-Facto Analysis of Knowledge Graph-Driven Chatbots - The DBpedia Chatbot}}},
  doi          = {{10.1007/978-3-030-39540-7\_2}},
  volume       = {{11970}},
  year         = {{2019}},
}

@inproceedings{25366,
  author       = {{Athanasiou, Spiros and Giannopoulos, Giorgos and Graux, Damien and Karagiannakis, Nikos and Lehmann, Jens and Ngonga Ngomo, Axel-Cyrille and Patroumpas, Kostas and Ahmed Sherif, Mohamed and Skoutas, Dimitrios}},
  booktitle    = {{Advances in Database Technology - 22nd International Conference on Extending Database Technology, {EDBT} 2019, Lisbon, Portugal, March 26-29, 2019}},
  editor       = {{Herschel, Melanie and Galhardas, Helena and Reinwald, Berthold and Fundulaki, Irini and Binnig, Carsten and Kaoudi, Zoi}},
  pages        = {{477--488}},
  publisher    = {{OpenProceedings.org}},
  title        = {{{Big POI data integration with Linked Data technologies}}},
  doi          = {{10.5441/002/edbt.2019.44}},
  year         = {{2019}},
}

@inproceedings{25367,
  author       = {{Amer Desouki, Abdelmoneim and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 30th {ACM} Conference on Hypertext and Social Media, {HT} 2019, Hof, Germany, September 17-20, 2019}},
  editor       = {{Atzenbeck, Claus and Rubart, Jessica and E. Millard, David}},
  pages        = {{163--171}},
  publisher    = {{{ACM}}},
  title        = {{{Ranking on Very Large Knowledge Graphs}}},
  doi          = {{10.1145/3342220.3343660}},
  year         = {{2019}},
}

@inproceedings{25368,
  author       = {{Hari Gusmita, Ria and Jalota, Rricha and Vollmers, Daniel and Reineke, Jan and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}},
  booktitle    = {{Semantic Systems. The Power of {AI} and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings}},
  editor       = {{Acosta, Maribel and Cudré-Mauroux, Philippe and Maleshkova, Maria and Pellegrini, Tassilo and Sack, Harald and Sure-Vetter, York}},
  pages        = {{343--358}},
  publisher    = {{Springer}},
  title        = {{{QUANT - Question Answering Benchmark Curator}}},
  doi          = {{10.1007/978-3-030-33220-4\_25}},
  volume       = {{11702}},
  year         = {{2019}},
}

@inproceedings{25369,
  author       = {{Kaur, Paramjot and Blücher, Vincent and Jalota, Rricha and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}},
  booktitle    = {{Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019), Karlsruhe, Germany, September 9th - to - 12th, 2019}},
  editor       = {{Alam, Mehwish and Usbeck, Ricardo and Pellegrini, Tassilo and Sack, Harald and Sure-Vetter, York}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Offline Question Answering over Linked Data using Limited Resources}}},
  volume       = {{2451}},
  year         = {{2019}},
}