[{"citation":{"apa":"Zahera, H. M. A., Jalota, R., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2021). I-AID: Identifying Actionable Information from Disaster-related Tweets. <i>IEEE Open Access</i>.","short":"H.M.A. Zahera, R. Jalota, M. Sherif, A.-C. Ngonga Ngomo, in: IEEE Open Access, 2021.","bibtex":"@inproceedings{Zahera_Jalota_Sherif_Ngonga Ngomo_2021, title={I-AID: Identifying Actionable Information from Disaster-related Tweets}, booktitle={IEEE Open Access}, author={Zahera, Hamada Mohamed Abdelsamee and Jalota, Rricha and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2021} }","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “I-AID: Identifying Actionable Information from Disaster-Related Tweets.” <i>IEEE Open Access</i>, 2021.","chicago":"Zahera, Hamada Mohamed Abdelsamee, Rricha Jalota, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information from Disaster-Related Tweets.” In <i>IEEE Open Access</i>, 2021.","ieee":"H. M. A. Zahera, R. Jalota, M. Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying Actionable Information from Disaster-related Tweets,” 2021.","ama":"Zahera HMA, Jalota R, Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable Information from Disaster-related Tweets. In: <i>IEEE Open Access</i>. ; 2021."},"year":"2021","author":[{"first_name":"Hamada Mohamed Abdelsamee","id":"72768","full_name":"Zahera, Hamada Mohamed Abdelsamee","orcid":"0000-0003-0215-1278","last_name":"Zahera"},{"first_name":"Rricha","full_name":"Jalota, Rricha","id":"69526","last_name":"Jalota"},{"first_name":"Mohamed","full_name":"Sherif, Mohamed","id":"67234","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"}],"date_created":"2021-12-17T10:06:30Z","date_updated":"2023-08-16T09:35:42Z","title":"I-AID: Identifying Actionable Information from Disaster-related Tweets","type":"conference","publication":"IEEE Open Access","status":"public","abstract":[{"lang":"eng","text":"Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. Recent studies highlight the need to filter information on social media into fine-grained content labels. However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. In this paper, we propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types and filter critical information from the enormous volume of social media data. I-AID incorporates three main components: i) a BERT- based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets’ words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information types in a supervised way. We conducted several experiments on two real publicly-available datasets. Our results indicate that I-AID outperforms state-of- the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively."}],"user_id":"67234","_id":"29043","language":[{"iso":"eng"}],"keyword":["sys:relevantFor:infai sys:relevantFor:DAIKIRI ngonga zahera sherif daikiriproject dice simba"]},{"year":"2021","page":"101874","citation":{"ama":"Fathi Ahmed A, Sherif M, Moussallem D, Ngonga Ngomo A-C. Multilingual Verbalization and Summarization for Explainable Link Discovery. <i>Data &#38; Knowledge Engineering</i>. Published online 2021:101874. doi:<a href=\"https://doi.org/10.1016/j.datak.2021.101874\">https://doi.org/10.1016/j.datak.2021.101874</a>","chicago":"Fathi Ahmed, Abdullah, Mohamed Sherif, Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “Multilingual Verbalization and Summarization for Explainable Link Discovery.” <i>Data &#38; Knowledge Engineering</i>, 2021, 101874. <a href=\"https://doi.org/10.1016/j.datak.2021.101874\">https://doi.org/10.1016/j.datak.2021.101874</a>.","ieee":"A. Fathi Ahmed, M. Sherif, D. Moussallem, and A.-C. Ngonga Ngomo, “Multilingual Verbalization and Summarization for Explainable Link Discovery,” <i>Data &#38; Knowledge Engineering</i>, p. 101874, 2021, doi: <a href=\"https://doi.org/10.1016/j.datak.2021.101874\">https://doi.org/10.1016/j.datak.2021.101874</a>.","apa":"Fathi Ahmed, A., Sherif, M., Moussallem, D., &#38; Ngonga Ngomo, A.-C. (2021). Multilingual Verbalization and Summarization for Explainable Link Discovery. <i>Data &#38; Knowledge Engineering</i>, 101874. <a href=\"https://doi.org/10.1016/j.datak.2021.101874\">https://doi.org/10.1016/j.datak.2021.101874</a>","mla":"Fathi Ahmed, Abdullah, et al. “Multilingual Verbalization and Summarization for Explainable Link Discovery.” <i>Data &#38; Knowledge Engineering</i>, 2021, p. 101874, doi:<a href=\"https://doi.org/10.1016/j.datak.2021.101874\">https://doi.org/10.1016/j.datak.2021.101874</a>.","short":"A. Fathi Ahmed, M. Sherif, D. Moussallem, A.-C. Ngonga Ngomo, Data &#38; Knowledge Engineering (2021) 101874.","bibtex":"@article{Fathi Ahmed_Sherif_Moussallem_Ngonga Ngomo_2021, title={Multilingual Verbalization and Summarization for Explainable Link Discovery}, DOI={<a href=\"https://doi.org/10.1016/j.datak.2021.101874\">https://doi.org/10.1016/j.datak.2021.101874</a>}, journal={Data &#38; Knowledge Engineering}, author={Fathi Ahmed, Abdullah and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2021}, pages={101874} }"},"publication_identifier":{"issn":["0169-023X"]},"title":"Multilingual Verbalization and Summarization for Explainable Link Discovery","doi":"https://doi.org/10.1016/j.datak.2021.101874","date_updated":"2023-08-16T10:26:16Z","author":[{"first_name":"Abdullah","full_name":"Fathi Ahmed, Abdullah","last_name":"Fathi Ahmed"},{"first_name":"Mohamed","full_name":"Sherif, Mohamed","id":"67234","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203"},{"first_name":"Diego","full_name":"Moussallem, Diego","id":"71635","last_name":"Moussallem"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"date_created":"2021-12-17T09:51:15Z","abstract":[{"text":"The number and size of datasets abiding by the Linked Data paradigm increase every day. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users. Particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we extend our previous work (Ahmed et al., 2019) by proposing a generic multilingual approach that allows verbalization of LS in many languages, i.e., converts LS into understandable natural language text. In this work, we ported our LS verbalization framework into German and Spanish, in addition to English language. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users. Moreover, we devised an experimental neural approach for improving the quality of our generated texts. Our neural approach achieves promising results in terms of BLEU, METEOR and chrF++.","lang":"eng"}],"status":"public","publication":"Data & Knowledge Engineering","type":"journal_article","keyword":["2021 sys:relevantFor:infai simba sherif ngonga ahmed limes dice raki moussallem libo opal knowgraphs"],"language":[{"iso":"eng"}],"_id":"29005","user_id":"67234"},{"year":"2021","citation":{"mla":"Chakraborty, Jaydeep, et al. “OntoConnect: Domain-Agnostic Ontology Alignment Using Graph Embedding with Negative Sampling.” <i>Proceedings of the IEEE International Conference on Machine Learning and Applications</i>, 2021.","short":"J. Chakraborty, M. Sherif, H.M.A. Zahera, S. Bansal, in: Proceedings of the IEEE International Conference on Machine Learning and Applications, 2021.","bibtex":"@inproceedings{Chakraborty_Sherif_Zahera_Bansal_2021, title={OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling}, booktitle={Proceedings of the IEEE International Conference on Machine Learning and Applications}, author={Chakraborty, Jaydeep and Sherif, Mohamed and Zahera, Hamada Mohamed Abdelsamee and Bansal, Srividya}, year={2021} }","apa":"Chakraborty, J., Sherif, M., Zahera, H. M. A., &#38; Bansal, S. (2021). OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling. <i>Proceedings of the IEEE International Conference on Machine Learning and Applications</i>.","ama":"Chakraborty J, Sherif M, Zahera HMA, Bansal S. OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling. In: <i>Proceedings of the IEEE International Conference on Machine Learning and Applications</i>. ; 2021.","chicago":"Chakraborty, Jaydeep, Mohamed Sherif, Hamada Mohamed Abdelsamee Zahera, and Srividya Bansal. “OntoConnect: Domain-Agnostic Ontology Alignment Using Graph Embedding with Negative Sampling.” In <i>Proceedings of the IEEE International Conference on Machine Learning and Applications</i>, 2021.","ieee":"J. Chakraborty, M. Sherif, H. M. A. Zahera, and S. Bansal, “OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling,” 2021."},"title":"OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling","date_updated":"2023-08-16T10:25:55Z","author":[{"first_name":"Jaydeep","last_name":"Chakraborty","full_name":"Chakraborty, Jaydeep"},{"orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","full_name":"Sherif, Mohamed","id":"67234","first_name":"Mohamed"},{"first_name":"Hamada Mohamed Abdelsamee","full_name":"Zahera, Hamada Mohamed Abdelsamee","id":"72768","last_name":"Zahera","orcid":"0000-0003-0215-1278"},{"full_name":"Bansal, Srividya","last_name":"Bansal","first_name":"Srividya"}],"date_created":"2021-12-17T10:06:45Z","status":"public","publication":"Proceedings of the IEEE International Conference on Machine Learning and Applications","type":"conference","keyword":["dice sherif hamada"],"language":[{"iso":"eng"}],"_id":"29044","user_id":"67234"},{"year":"2020","page":"73-97","citation":{"apa":"Röder, M., Sherif, M., Saleem, M., Conrads, F., &#38; Ngonga Ngomo, A.-C. (2020). Benchmarking the Lifecycle of Knowledge Graphs. In I. Tiddi, F. Lécué, &#38; P. Hitzler (Eds.), <i>Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges</i> (pp. 73–97). IOS Press. <a href=\"https://doi.org/10.3233/SSW200012\">https://doi.org/10.3233/SSW200012</a>","mla":"Röder, Michael, et al. “Benchmarking the Lifecycle of Knowledge Graphs.” <i>Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges</i>, edited by Ilaria Tiddi et al., IOS Press, 2020, pp. 73–97, doi:<a href=\"https://doi.org/10.3233/SSW200012\">10.3233/SSW200012</a>.","short":"M. Röder, M. Sherif, M. Saleem, F. Conrads, A.-C. Ngonga Ngomo, in: I. Tiddi, F. Lécué, P. Hitzler (Eds.), Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges, IOS Press, 2020, pp. 73–97.","bibtex":"@inbook{Röder_Sherif_Saleem_Conrads_Ngonga Ngomo_2020, title={Benchmarking the Lifecycle of Knowledge Graphs}, DOI={<a href=\"https://doi.org/10.3233/SSW200012\">10.3233/SSW200012</a>}, booktitle={Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges}, publisher={IOS Press}, author={Röder, Michael and Sherif, Mohamed and Saleem, Muhammad and Conrads, Felix and Ngonga Ngomo, Axel-Cyrille}, editor={Tiddi, Ilaria and Lécué, Freddy and Hitzler, Pascal}, year={2020}, pages={73–97} }","chicago":"Röder, Michael, Mohamed Sherif, Muhammad Saleem, Felix Conrads, and Axel-Cyrille Ngonga Ngomo. “Benchmarking the Lifecycle of Knowledge Graphs.” In <i>Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges</i>, edited by Ilaria Tiddi, Freddy Lécué, and Pascal Hitzler, 73–97. IOS Press, 2020. <a href=\"https://doi.org/10.3233/SSW200012\">https://doi.org/10.3233/SSW200012</a>.","ieee":"M. Röder, M. Sherif, M. Saleem, F. Conrads, and A.-C. Ngonga Ngomo, “Benchmarking the Lifecycle of Knowledge Graphs,” in <i>Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges</i>, I. Tiddi, F. Lécué, and P. Hitzler, Eds. IOS Press, 2020, pp. 73–97.","ama":"Röder M, Sherif M, Saleem M, Conrads F, Ngonga Ngomo A-C. Benchmarking the Lifecycle of Knowledge Graphs. In: Tiddi I, Lécué F, Hitzler P, eds. <i>Knowledge Graphs for EXplainable Artificial Intelligence: Foundations, Applications and Challenges</i>. IOS Press; 2020:73-97. doi:<a href=\"https://doi.org/10.3233/SSW200012\">10.3233/SSW200012</a>"},"date_updated":"2023-08-16T09:32:51Z","publisher":"IOS Press","date_created":"2021-12-17T10:06:12Z","author":[{"first_name":"Michael","full_name":"Röder, Michael","last_name":"Röder"},{"first_name":"Mohamed","id":"67234","full_name":"Sherif, Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203"},{"first_name":"Muhammad","last_name":"Saleem","full_name":"Saleem, Muhammad"},{"full_name":"Conrads, Felix","last_name":"Conrads","first_name":"Felix"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"title":"Benchmarking the Lifecycle of Knowledge Graphs","doi":"10.3233/SSW200012","publication":"Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges","type":"book_chapter","editor":[{"first_name":"Ilaria","last_name":"Tiddi","full_name":"Tiddi, Ilaria"},{"last_name":"Lécué","full_name":"Lécué, Freddy","first_name":"Freddy"},{"first_name":"Pascal","full_name":"Hitzler, Pascal","last_name":"Hitzler"}],"status":"public","_id":"29042","department":[{"_id":"574"}],"user_id":"67234","keyword":["dice group_aksw roeder sherif saleem fconrads ngonga"],"language":[{"iso":"eng"}]},{"abstract":[{"lang":"eng","text":"With the growth in number and variety of RDF datasets comes an in- creasing need for both scalable and accurate solutions to support link discovery at instance level within and across these datasets. In contrast to ontology matching, most linking frameworks rely solely on string similarities to this end. The limited use of semantic similarities when linking instances is partly due to the current literature stating that they (1) do not improve the F-measure of instance linking approaches and (2) are impractical to use because they lack time efficiency. We revisit the combination of string and semantic similarities for linking instances. Contrary to the literature, our results suggest that this combination can improve the F-measure achieved by instance linking systems when the combination of the measures is performed by a machine learning approach. To achieve this in- sight, we had to address the scalability of semantic similarities. We hence present a framework for the rapid computation of semantic similarities based on edge counting. This runtime improvement allowed us to run an evaluation of 5 bench- mark datasets. Our results suggest that combining string and semantic similarities can improve the F-measure by up to 6% absolute."}],"status":"public","type":"conference","publication":"Proceedings of Ontology Matching Workshop 2020","keyword":["2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal roeder georgala"],"language":[{"iso":"eng"}],"_id":"29009","user_id":"67234","year":"2020","citation":{"ama":"Georgala K, Röder M, Sherif M, Ngonga Ngomo A-C. Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.","ieee":"K. Georgala, M. Röder, M. Sherif, and A.-C. Ngonga Ngomo, “Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy,” 2020.","chicago":"Georgala, Kleanthi, Michael Röder, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Applying Edge-Counting Semantic Similarities to Link Discovery: Scalability and Accuracy.” In <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","apa":"Georgala, K., Röder, M., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2020). Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy. <i>Proceedings of Ontology Matching Workshop 2020</i>.","mla":"Georgala, Kleanthi, et al. “Applying Edge-Counting Semantic Similarities to Link Discovery: Scalability and Accuracy.” <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","short":"K. Georgala, M. Röder, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2020, 2020.","bibtex":"@inproceedings{Georgala_Röder_Sherif_Ngonga Ngomo_2020, title={Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy}, booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Georgala, Kleanthi and Röder, Michael and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2020} }"},"title":"Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy","date_updated":"2023-08-16T09:34:31Z","date_created":"2021-12-17T09:53:49Z","author":[{"first_name":"Kleanthi","last_name":"Georgala","full_name":"Georgala, Kleanthi"},{"first_name":"Michael","full_name":"Röder, Michael","last_name":"Röder"},{"first_name":"Mohamed","id":"67234","full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif"},{"last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","first_name":"Axel-Cyrille"}]},{"abstract":[{"lang":"eng","text":"Link discovery plays a key role in the integration and use of data across RDF knowledge graphs. Active learning approaches are a common family of solutions to address the problem of learning how to compute links from users. So far, only active learning from perfect oracles has been considered in the literature. However, real oracles are often far from perfect (e.g., in crowdsourcing). We hence study the problem of learning how to compute links across knowledge graphs from noisy oracles, i.e., oracles that are not guaranteed to return correct classification results. We present a novel approach for link discovery based on a probabilistic model, with which we estimate the joint odds of the oracles’ guesses. We combine this approach with an iterative learning approach based on refinements. The resulting method, Ligon, is evaluated on 10 benchmark datasets. Our results suggest that Ligon configured with 10 iterations and 10 training examples per iteration achieves more than 95% of the F-measure achieved by state-of-the-art algorithms trained with a perfect oracle. Moreover, Ligon outperforms batch learning approaches devised to be trained with small amounts of training data by more than 40% F-measure on average."}],"status":"public","publication":"Proceedings of Ontology Matching Workshop 2020","type":"conference","keyword":["2020 dice simba sherif ligon ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal kevin"],"language":[{"iso":"eng"}],"_id":"29010","user_id":"67234","year":"2020","citation":{"bibtex":"@inproceedings{Sherif_Dreßler}_Ngonga Ngomo_2020, title={LIGON – Link Discovery with Noisy Oracles}, booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Sherif, Mohamed and Dreßler}, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2020} }","short":"M. Sherif, K. Dreßler}, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2020, 2020.","mla":"Sherif, Mohamed, et al. “LIGON – Link Discovery with Noisy Oracles.” <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","apa":"Sherif, M., Dreßler}, K., &#38; Ngonga Ngomo, A.-C. (2020). LIGON – Link Discovery with Noisy Oracles. <i>Proceedings of Ontology Matching Workshop 2020</i>.","ama":"Sherif M, Dreßler} K, Ngonga Ngomo A-C. LIGON – Link Discovery with Noisy Oracles. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.","ieee":"M. Sherif, K. Dreßler}, and A.-C. Ngonga Ngomo, “LIGON – Link Discovery with Noisy Oracles,” 2020.","chicago":"Sherif, Mohamed, Kevin Dreßler}, and Axel-Cyrille Ngonga Ngomo. “LIGON – Link Discovery with Noisy Oracles.” In <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020."},"title":"LIGON – Link Discovery with Noisy Oracles","date_updated":"2023-08-16T09:34:11Z","author":[{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","full_name":"Sherif, Mohamed"},{"first_name":"Kevin","full_name":"Dreßler}, Kevin","last_name":"Dreßler}"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"date_created":"2021-12-17T09:54:05Z"},{"citation":{"apa":"Bigerl, A., Conrads, F., Behning, C., Sherif, M., Saleem, M., &#38; Ngonga Ngomo, A.-C. (2020). Tentris – A Tensor-Based Triple Store. <i>The Semantic Web -- ISWC 2020</i>.","short":"A. Bigerl, F. Conrads, C. Behning, M. Sherif, M. Saleem, A.-C. Ngonga Ngomo, The Semantic Web -- ISWC 2020 (2020).","mla":"Bigerl, Alexander, et al. “Tentris – A Tensor-Based Triple Store.” <i>The Semantic Web -- ISWC 2020</i>, Springer International Publishing, 2020.","bibtex":"@article{Bigerl_Conrads_Behning_Sherif_Saleem_Ngonga Ngomo_2020, title={Tentris – A Tensor-Based Triple Store}, journal={The Semantic Web -- ISWC 2020}, publisher={Springer International Publishing}, author={Bigerl, Alexander and Conrads, Felix and Behning, Charlotte and Sherif, Mohamed and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}, year={2020} }","ama":"Bigerl A, Conrads F, Behning C, Sherif M, Saleem M, Ngonga Ngomo A-C. Tentris – A Tensor-Based Triple Store. <i>The Semantic Web -- ISWC 2020</i>. Published online 2020.","ieee":"A. Bigerl, F. Conrads, C. Behning, M. Sherif, M. Saleem, and A.-C. Ngonga Ngomo, “Tentris – A Tensor-Based Triple Store,” <i>The Semantic Web -- ISWC 2020</i>, 2020.","chicago":"Bigerl, Alexander, Felix Conrads, Charlotte Behning, Mohamed Sherif, Muhammad Saleem, and Axel-Cyrille Ngonga Ngomo. “Tentris – A Tensor-Based Triple Store.” <i>The Semantic Web -- ISWC 2020</i>, 2020."},"year":"2020","author":[{"first_name":"Alexander","last_name":"Bigerl","full_name":"Bigerl, Alexander","id":"72857"},{"full_name":"Conrads, Felix","last_name":"Conrads","first_name":"Felix"},{"full_name":"Behning, Charlotte","last_name":"Behning","first_name":"Charlotte"},{"first_name":"Mohamed","full_name":"Sherif, Mohamed","id":"67234","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203"},{"full_name":"Saleem, Muhammad","last_name":"Saleem","first_name":"Muhammad"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"date_created":"2021-12-17T10:05:41Z","publisher":"Springer International Publishing","date_updated":"2023-08-16T10:06:33Z","title":"Tentris – A Tensor-Based Triple Store","publication":"The Semantic Web -- ISWC 2020","type":"journal_article","status":"public","user_id":"67234","_id":"29039","language":[{"iso":"eng"}],"keyword":["sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba sys:relevantFor:limbo sys:relevantFor:raki daikiri speaker tentris knowgraphs bigerl fconrads saleem sherif ngonga group_aksw dice"]},{"title":"ProBERT: Product Data Classification with Fine-tuning BERT Model","date_updated":"2023-08-16T10:06:10Z","author":[{"last_name":"Zahera","orcid":"0000-0003-0215-1278","id":"72768","full_name":"Zahera, Hamada Mohamed Abdelsamee","first_name":"Hamada Mohamed Abdelsamee"},{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","full_name":"Sherif, Mohamed"}],"date_created":"2021-12-17T10:05:42Z","year":"2020","citation":{"ama":"Zahera HMA, Sherif M. ProBERT: Product Data Classification with Fine-tuning BERT Model. In: <i>Proceedings of Mining the Web of HTML-Embedded Product Data Workshop (MWPD2020)</i>. ; 2020.","chicago":"Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product Data Classification with Fine-Tuning BERT Model.” In <i>Proceedings of Mining the Web of HTML-Embedded Product Data Workshop (MWPD2020)</i>, 2020.","ieee":"H. M. A. Zahera and M. Sherif, “ProBERT: Product Data Classification with Fine-tuning BERT Model,” 2020.","apa":"Zahera, H. M. A., &#38; Sherif, M. (2020). ProBERT: Product Data Classification with Fine-tuning BERT Model. <i>Proceedings of Mining the Web of HTML-Embedded Product Data Workshop (MWPD2020)</i>.","short":"H.M.A. Zahera, M. Sherif, in: Proceedings of Mining the Web of HTML-Embedded Product Data Workshop (MWPD2020), 2020.","mla":"Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product Data Classification with Fine-Tuning BERT Model.” <i>Proceedings of Mining the Web of HTML-Embedded Product Data Workshop (MWPD2020)</i>, 2020.","bibtex":"@inproceedings{Zahera_Sherif_2020, title={ProBERT: Product Data Classification with Fine-tuning BERT Model}, booktitle={Proceedings of Mining the Web of HTML-embedded Product Data Workshop (MWPD2020)}, author={Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed}, year={2020} }"},"keyword":["2020 dice zahera sherif knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal"],"language":[{"iso":"eng"}],"_id":"29040","user_id":"67234","status":"public","publication":"Proceedings of Mining the Web of HTML-embedded Product Data Workshop (MWPD2020)","type":"conference"},{"date_updated":"2023-08-16T10:27:11Z","author":[{"last_name":"Georgala","full_name":"Georgala, Kleanthi","first_name":"Kleanthi"},{"first_name":"Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","id":"67234","full_name":"Sherif, Mohamed"},{"last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","first_name":"Axel-Cyrille"}],"date_created":"2021-12-17T09:53:07Z","title":"LIGER – Link Discovery with Partial Recall","year":"2020","citation":{"ama":"Georgala K, Sherif M, Ngonga Ngomo A-C. LIGER – Link Discovery with Partial Recall. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.","chicago":"Georgala, Kleanthi, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “LIGER – Link Discovery with Partial Recall.” In <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","ieee":"K. Georgala, M. Sherif, and A.-C. Ngonga Ngomo, “LIGER – Link Discovery with Partial Recall,” 2020.","mla":"Georgala, Kleanthi, et al. “LIGER – Link Discovery with Partial Recall.” <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","bibtex":"@inproceedings{Georgala_Sherif_Ngonga Ngomo_2020, title={LIGER – Link Discovery with Partial Recall}, booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Georgala, Kleanthi and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2020} }","short":"K. Georgala, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2020, 2020.","apa":"Georgala, K., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2020). LIGER – Link Discovery with Partial Recall. <i>Proceedings of Ontology Matching Workshop 2020</i>."},"_id":"29007","user_id":"67234","keyword":["2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal georgala"],"language":[{"iso":"eng"}],"publication":"Proceedings of Ontology Matching Workshop 2020","type":"conference","abstract":[{"lang":"eng","text":"Modern data-driven frameworks often have to process large amounts of data periodically. Hence, they often operate under time or space constraints. This also holds for Linked Data-driven frameworks when processing RDF data, in particular, when they perform link discovery tasks. In this work, we present a novel approach for link discovery under constraints pertaining to the expected recall of a link discovery task. Given a link specification, the approach aims to find a subsumed link specification that achieves a lower run time than the input specification while abiding by a predefined constraint on the expected recall it has to achieve. Our approach, dubbed LIGER, combines downward refinement oper- ators with monotonicity assumptions to detect such specifications. We evaluate our approach on seven datasets. Our results suggest that the different implemen- tations of LIGER can detect subsumed specifications that abide by expected recall constraints efficiently, thus leading to significantly shorter overall run times than our baseline."}],"status":"public"},{"title":"Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction","author":[{"first_name":"Hamada Mohamed Abdelsamee","orcid":"0000-0003-0215-1278","last_name":"Zahera","full_name":"Zahera, Hamada Mohamed Abdelsamee","id":"72768"},{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","full_name":"Sherif, Mohamed"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"date_created":"2021-12-17T10:05:07Z","date_updated":"2023-08-16T09:24:21Z","citation":{"apa":"Zahera, H. M. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. <i>K-CAP 2019: Knowledge Capture Conference</i>, 4.","short":"H.M.A. Zahera, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019, p. 4.","bibtex":"@inproceedings{Zahera_Sherif_Ngonga Ngomo_2019, title={Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction}, booktitle={K-CAP 2019: Knowledge Capture Conference}, author={Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019}, pages={4} }","ieee":"H. M. A. Zahera, M. Sherif, and A.-C. Ngonga Ngomo, “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction,” in <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019, p. 4.","chicago":"Zahera, Hamada Mohamed Abdelsamee, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” In <i>K-CAP 2019: Knowledge Capture Conference</i>, 4, 2019.","ama":"Zahera HMA, Sherif M, Ngonga Ngomo A-C. Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. In: <i>K-CAP 2019: Knowledge Capture Conference</i>. ; 2019:4."},"page":"4","year":"2019","language":[{"iso":"eng"}],"keyword":["sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera sherif solide limboproject opal group\\_aksw dice"],"user_id":"67234","_id":"29037","status":"public","abstract":[{"lang":"eng","text":"Existing technologies employ different machine learning approaches to predict disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. In this work, we consider social media as a supplementary source of knowledge in addition to historical environmental data. Further, we build a joint model that learns from disaster-related tweets and environmental data to improve prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with their semantics representations computed with the traditional word2vec. Our experiments show that our proposed approach outperforms the accuracy of state-of-the-art models in disaster prediction."}],"type":"conference","publication":"K-CAP 2019: Knowledge Capture Conference"},{"title":"LimesWebUI – Link Discovery Made Simple","date_updated":"2023-08-16T09:25:11Z","publisher":"CEUR-WS.org","author":[{"last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","full_name":"Sherif, Mohamed","first_name":"Mohamed"},{"last_name":"Pestryakova","full_name":"Pestryakova, Svetlana","first_name":"Svetlana"},{"first_name":"Kevin","id":"78256","full_name":"Dreßler, Kevin","last_name":"Dreßler"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"date_created":"2021-12-17T09:54:17Z","year":"2019","citation":{"ieee":"M. Sherif, S. Pestryakova, K. Dreßler, and A.-C. Ngonga Ngomo, “LimesWebUI – Link Discovery Made Simple,” 2019.","chicago":"Sherif, Mohamed, Svetlana Pestryakova, Kevin Dreßler, and Axel-Cyrille Ngonga Ngomo. “LimesWebUI – Link Discovery Made Simple.” In <i>18th International Semantic Web Conference (ISWC 2019)</i>. CEUR-WS.org, 2019.","ama":"Sherif M, Pestryakova S, Dreßler K, Ngonga Ngomo A-C. LimesWebUI – Link Discovery Made Simple. In: <i>18th International Semantic Web Conference (ISWC 2019)</i>. CEUR-WS.org; 2019.","apa":"Sherif, M., Pestryakova, S., Dreßler, K., &#38; Ngonga Ngomo, A.-C. (2019). LimesWebUI – Link Discovery Made Simple. <i>18th International Semantic Web Conference (ISWC 2019)</i>.","bibtex":"@inproceedings{Sherif_Pestryakova_Dreßler_Ngonga Ngomo_2019, title={LimesWebUI – Link Discovery Made Simple}, booktitle={18th International Semantic Web Conference (ISWC 2019)}, publisher={CEUR-WS.org}, author={Sherif, Mohamed and Pestryakova, Svetlana and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2019} }","short":"M. Sherif, S. Pestryakova, K. Dreßler, A.-C. Ngonga Ngomo, in: 18th International Semantic Web Conference (ISWC 2019), CEUR-WS.org, 2019.","mla":"Sherif, Mohamed, et al. “LimesWebUI – Link Discovery Made Simple.” <i>18th International Semantic Web Conference (ISWC 2019)</i>, CEUR-WS.org, 2019."},"keyword":["2019 sys:relevantFor:infai group\\_aksw simba sherif kevin ngonga Svetlana slipo limes dice sage limbo opal"],"language":[{"iso":"eng"}],"_id":"29011","user_id":"67234","abstract":[{"lang":"eng","text":"In this paper we present LimesWebUI, our web interface of Limes. Limes, the Link Discovery Framework for Metric Spaces, is a framework for dis- covering links between entities contained in Linked Data sources. LimesWebUI assists the end user during the link discovery process. By representing the link specifications (LS) as interlocking blocks, our interface eases the manual creation of links for users who already know which LS they would like to execute. How- ever, most users do not know which LS suits their linking task best and therefore need help throughout this process. Hence, our interface provides wizards which allow the easy configuration of many link discovery machine learning algorithms, that does not require the user to enter a manual LS. We evaluate the usability of the interface by using the standard system usability scale questionnaire. Our over- all usability score of 76.5 suggests that the online interface is consistent, easy to use, and the various functions of the system are well integrated."}],"status":"public","type":"conference","publication":"18th International Semantic Web Conference (ISWC 2019)"},{"_id":"29003","user_id":"67234","keyword":["zahera elgendy jalota sherif dice"],"language":[{"iso":"eng"}],"type":"conference","publication":"Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019","abstract":[{"text":"In this paper, we describe our approach to classify disaster-related tweets into multilabel information types (ie, labels). We aim to filter first relevant tweets during disasters. Then, we assign tweets relevant information types. Information types can be SearchAndRescue, MovePeople and Volunteer. We employ a fine-tuned BERT model with 10 BERT layers. Further, we submitted our approach to the TREC-IS 2019 challenge, the evaluation results showed that our approach outperforms the F1-score of median score in identifying actionable information.","lang":"eng"}],"status":"public","date_updated":"2023-08-16T09:25:34Z","author":[{"first_name":"Hamada Mohamed Abdelsamee","orcid":"0000-0003-0215-1278","last_name":"Zahera","full_name":"Zahera, Hamada Mohamed Abdelsamee","id":"72768"},{"first_name":"Ibrahim","full_name":"A. Elgendy, Ibrahim","last_name":"A. Elgendy"},{"first_name":"Rricha","id":"69526","full_name":"Jalota, Rricha","last_name":"Jalota"},{"first_name":"Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","id":"67234","full_name":"Sherif, Mohamed"}],"date_created":"2021-12-17T09:48:17Z","title":"Fine-tuned BERT Model for Multi-Label Tweets Classification","year":"2019","citation":{"short":"H.M.A. Zahera, I. A. Elgendy, R. Jalota, M. Sherif, in: Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019, 2019.","bibtex":"@inproceedings{Zahera_A. Elgendy_Jalota_Sherif_2019, title={Fine-tuned BERT Model for Multi-Label Tweets Classification}, booktitle={Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019}, author={Zahera, Hamada Mohamed Abdelsamee and A. Elgendy, Ibrahim and Jalota, Rricha and Sherif, Mohamed}, year={2019} }","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “Fine-Tuned BERT Model for Multi-Label Tweets Classification.” <i>Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>, 2019.","apa":"Zahera, H. M. A., A. Elgendy, I., Jalota, R., &#38; Sherif, M. (2019). Fine-tuned BERT Model for Multi-Label Tweets Classification. <i>Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>.","ama":"Zahera HMA, A. Elgendy I, Jalota R, Sherif M. Fine-tuned BERT Model for Multi-Label Tweets Classification. In: <i>Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>. ; 2019.","chicago":"Zahera, Hamada Mohamed Abdelsamee, Ibrahim A. Elgendy, Rricha Jalota, and Mohamed Sherif. “Fine-Tuned BERT Model for Multi-Label Tweets Classification.” In <i>Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>, 2019.","ieee":"H. M. A. Zahera, I. A. Elgendy, R. Jalota, and M. Sherif, “Fine-tuned BERT Model for Multi-Label Tweets Classification,” 2019."}},{"author":[{"id":"29670","full_name":"Ahmed, Abdullah Fathi Ahmed","last_name":"Ahmed","first_name":"Abdullah Fathi Ahmed"},{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","full_name":"Sherif, Mohamed","id":"67234"},{"first_name":"Axel-Cyrille","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"date_created":"2021-12-17T10:05:09Z","date_updated":"2023-08-16T09:35:21Z","title":"Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery","citation":{"ama":"Ahmed AFA, Sherif M, Ngonga Ngomo A-C. Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery. In: <i>K-CAP 2019: Knowledge Capture Conference</i>. ; 2019.","ieee":"A. F. A. Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery,” 2019.","chicago":"Ahmed, Abdullah Fathi Ahmed, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Do Your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery.” In <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019.","apa":"Ahmed, A. F. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery. <i>K-CAP 2019: Knowledge Capture Conference</i>.","bibtex":"@inproceedings{Ahmed_Sherif_Ngonga Ngomo_2019, title={Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery}, booktitle={K-CAP 2019: Knowledge Capture Conference}, author={Ahmed, Abdullah Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019} }","mla":"Ahmed, Abdullah Fathi Ahmed, et al. “Do Your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery.” <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019.","short":"A.F.A. Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge Capture Conference, 2019."},"year":"2019","user_id":"67234","_id":"29038","language":[{"iso":"eng"}],"keyword":["sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:ngonga ahmed sherif solide limboproject opal group_aksw dice"],"type":"conference","publication":"K-CAP 2019: Knowledge Capture Conference","status":"public","abstract":[{"lang":"eng","text":"An increasing number of heterogeneous datasets abiding by the Linked Data paradigm is published everyday. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Complex LS combine similarity measures with thresholds to determine whether a given predicate holds between two resources. State of the art LD frameworks rely mostly on string-based similarity measures such as Levenshtein and Jaccard. However, string-based similarity measures often fail to catch the similarity of resources with phonetically similar property values when these property values are represented using different string representation (e.g., names and street labels). In this paper, we evaluate the impact of using phonetics-based similarities in the process of LD. Moreover, we evaluate the impact of phonetic-based similarity measures on a state-of-the-art machine learning approach used to generate LS. Our experiments suggest that the combination of string-based and phonetic-based measures can improve the Fmeasures achieved by LD frameworks on most datasets."}]},{"date_updated":"2023-08-16T10:06:20Z","publisher":"Springer","author":[{"first_name":"Abdullah ","last_name":"Fathi Ahmed","full_name":"Fathi Ahmed, Abdullah "},{"id":"67234","full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","first_name":"Mohamed"},{"first_name":"Axel-Cyrille","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"date_created":"2021-12-17T09:54:40Z","title":"LSVS: Link Specification Verbalization and Summarization","year":"2019","citation":{"chicago":"Fathi Ahmed, Abdullah , Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “LSVS: Link Specification Verbalization and Summarization.” In <i>24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)</i>. Springer, 2019.","ieee":"A. Fathi Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “LSVS: Link Specification Verbalization and Summarization,” 2019.","ama":"Fathi Ahmed A, Sherif M, Ngonga Ngomo A-C. LSVS: Link Specification Verbalization and Summarization. In: <i>24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)</i>. Springer; 2019.","mla":"Fathi Ahmed, Abdullah, et al. “LSVS: Link Specification Verbalization and Summarization.” <i>24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)</i>, Springer, 2019.","short":"A. Fathi Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: 24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019), Springer, 2019.","bibtex":"@inproceedings{Fathi Ahmed_Sherif_Ngonga Ngomo_2019, title={LSVS: Link Specification Verbalization and Summarization}, booktitle={24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)}, publisher={Springer}, author={Fathi Ahmed, Abdullah  and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019} }","apa":"Fathi Ahmed, A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). LSVS: Link Specification Verbalization and Summarization. <i>24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)</i>."},"_id":"29012","user_id":"67234","keyword":["2019 sys:relevantFor:infai group\\_aksw simba sherif ngonga ahmed slipo limes dice sage limbo opal"],"language":[{"iso":"eng"}],"publication":"24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)","type":"conference","abstract":[{"lang":"eng","text":"An increasing number and size of datasets abiding by the Linked Data paradigm are published everyday. Discovering links between these datasets is thus central to achieve the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users, particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we address this problem by proposing a generic approach that allows a LS to be verbalized, i.e., converted into understandable natural language. We propose a summarization approach to the verbalized LS based on the selectivity of the underlying LS. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users."}],"status":"public"},{"user_id":"67234","_id":"29013","language":[{"iso":"eng"}],"keyword":["2019 sys:relevantFor:infai group\\_aksw simba sherif ngonga lehmann slipo limes dice deer"],"type":"conference","publication":"International Conference on Extending Database Technology 2019, EDBT19","status":"public","abstract":[{"text":"Point of Interest (POI) data constitute the cornerstone of any application, service or product even remotely related to our physical surroundings. From navigation applications to social networks, tourism, and logistics, we use POI data to search, communicate, decide and plan our actions. POIs are semantically diverse and spatio-temporally evolving entities, having geographical, temporal and thematic relations. Currently, integrating POI data to increase their coverage, timeliness, accuracy and value is a resource-intensive and mostly manual process, with no specialized software available to address the specific challenges of this task. In this paper, we present an integrated toolkit for transforming, linking, fusing and enriching POI data, and extracting additional value from them. In particular, we demonstrate how Linked Data technologies can address the limitations, gaps and challenges of the current landscape in Big POI data integration. We have built a prototype application that enables users to define, manage and execute scalable POI data integration workflows built on top of state-of-the-art software for geospatial Linked Data. The application abstracts and hides away the underlying complexity, automates quality-assured integration, scales efficiently for world-scale integration tasks and lowers the entry barrier for end-users. Validated against real-world POI datasets in several application domains, our system has shown great potential to address the requirements and needs of cross-sector, cross-border and cross-lingual integration of Big POI data.","lang":"eng"}],"date_created":"2021-12-17T09:54:54Z","author":[{"first_name":"Spiros","full_name":"Athanasiou, Spiros","last_name":"Athanasiou"},{"last_name":"Giorgos","full_name":"Giorgos, Giannopoulos","first_name":"Giannopoulos"},{"first_name":"Graux","last_name":"Damien","full_name":"Damien, Graux"},{"last_name":"Nikos","full_name":"Nikos, Karagiannakis","first_name":"Karagiannakis"},{"last_name":"Jens","full_name":"Jens, Lehmann","first_name":"Lehmann"},{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"},{"last_name":"Kostas","full_name":"Kostas, Patroumpas","first_name":"Patroumpas"},{"orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","full_name":"Sherif, Mohamed","id":"67234","first_name":"Mohamed"},{"first_name":"Dimitrios","last_name":"Skoutas","full_name":"Skoutas, Dimitrios"}],"date_updated":"2023-08-16T10:29:26Z","title":"Big POI data integration with Linked Data technologies","citation":{"ama":"Athanasiou S, Giorgos G, Damien G, et al. Big POI data integration with Linked Data technologies. In: <i>International Conference on Extending Database Technology 2019, EDBT19</i>. ; 2019.","ieee":"S. Athanasiou <i>et al.</i>, “Big POI data integration with Linked Data technologies,” 2019.","chicago":"Athanasiou, Spiros, Giannopoulos Giorgos, Graux Damien, Karagiannakis Nikos, Lehmann Jens, Axel-Cyrille Ngonga Ngomo, Patroumpas Kostas, Mohamed Sherif, and Dimitrios Skoutas. “Big POI Data Integration with Linked Data Technologies.” In <i>International Conference on Extending Database Technology 2019, EDBT19</i>, 2019.","bibtex":"@inproceedings{Athanasiou_Giorgos_Damien_Nikos_Jens_Ngonga Ngomo_Kostas_Sherif_Skoutas_2019, title={Big POI data integration with Linked Data technologies}, booktitle={International Conference on Extending Database Technology 2019, EDBT19}, author={Athanasiou, Spiros and Giorgos, Giannopoulos and Damien, Graux and Nikos, Karagiannakis and Jens, Lehmann and Ngonga Ngomo, Axel-Cyrille and Kostas, Patroumpas and Sherif, Mohamed and Skoutas, Dimitrios}, year={2019} }","mla":"Athanasiou, Spiros, et al. “Big POI Data Integration with Linked Data Technologies.” <i>International Conference on Extending Database Technology 2019, EDBT19</i>, 2019.","short":"S. Athanasiou, G. Giorgos, G. Damien, K. Nikos, L. Jens, A.-C. Ngonga Ngomo, P. Kostas, M. Sherif, D. Skoutas, in: International Conference on Extending Database Technology 2019, EDBT19, 2019.","apa":"Athanasiou, S., Giorgos, G., Damien, G., Nikos, K., Jens, L., Ngonga Ngomo, A.-C., Kostas, P., Sherif, M., &#38; Skoutas, D. (2019). Big POI data integration with Linked Data technologies. <i>International Conference on Extending Database Technology 2019, EDBT19</i>."},"year":"2019"},{"citation":{"short":"A. Fathi Ahmed, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2018, 2018.","mla":"Fathi Ahmed, Abdullah, et al. “RADON2: A Buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results).” <i>Proceedings of Ontology Matching Workshop 2018</i>, 2018.","bibtex":"@inproceedings{Fathi Ahmed_Sherif_Ngonga Ngomo_2018, title={RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results)}, booktitle={Proceedings of Ontology Matching Workshop 2018}, author={Fathi Ahmed, Abdullah and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2018} }","apa":"Fathi Ahmed, A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2018). RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results). <i>Proceedings of Ontology Matching Workshop 2018</i>.","ama":"Fathi Ahmed A, Sherif M, Ngonga Ngomo A-C. RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results). In: <i>Proceedings of Ontology Matching Workshop 2018</i>. ; 2018.","ieee":"A. Fathi Ahmed, M. Sherif, and A.-C. Ngonga Ngomo, “RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results),” 2018.","chicago":"Fathi Ahmed, Abdullah, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “RADON2: A Buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results).” In <i>Proceedings of Ontology Matching Workshop 2018</i>, 2018."},"year":"2018","author":[{"first_name":"Abdullah","last_name":"Fathi Ahmed","full_name":"Fathi Ahmed, Abdullah"},{"last_name":"Sherif","full_name":"Sherif, Mohamed","id":"67234","first_name":"Mohamed"},{"last_name":"Ngonga Ngomo","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille"}],"date_created":"2021-12-17T09:59:59Z","date_updated":"2022-04-05T10:28:29Z","title":"RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results)","publication":"Proceedings of Ontology Matching Workshop 2018","type":"conference","status":"public","user_id":"67234","_id":"29029","project":[{"_id":"52","name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"language":[{"iso":"eng"}],"keyword":["2018 simba dice radon abdullah sherif ngonga slipo sage geiser hobbit group\\_aksw sys:relevantFor:infai sys:relevantFor:bis limes linkinglod sake diesel sys:relevantFor:leds leds"]},{"year":"2018","citation":{"ama":"Moussallem D, Sherif M, Esteves D, Zampieri M, Ngonga Ngomo A-C. LIdioms: A Multilingual Linked Idioms Data Set. In: <i>The 11th Edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)</i>. ; 2018.","chicago":"Moussallem, Diego, Mohamed Sherif, Diego Esteves, Marcos Zampieri, and Axel-Cyrille Ngonga Ngomo. “LIdioms: A Multilingual Linked Idioms Data Set.” In <i>The 11th Edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)</i>, 2018.","ieee":"D. Moussallem, M. Sherif, D. Esteves, M. Zampieri, and A.-C. Ngonga Ngomo, “LIdioms: A Multilingual Linked Idioms Data Set,” 2018.","mla":"Moussallem, Diego, et al. “LIdioms: A Multilingual Linked Idioms Data Set.” <i>The 11th Edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)</i>, 2018.","short":"D. Moussallem, M. Sherif, D. Esteves, M. Zampieri, A.-C. Ngonga Ngomo, in: The 11th Edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan), 2018.","bibtex":"@inproceedings{Moussallem_Sherif_Esteves_Zampieri_Ngonga Ngomo_2018, title={LIdioms: A Multilingual Linked Idioms Data Set}, booktitle={The 11th edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)}, author={Moussallem, Diego and Sherif, Mohamed and Esteves, Diego and Zampieri, Marcos and Ngonga Ngomo, Axel-Cyrille}, year={2018} }","apa":"Moussallem, D., Sherif, M., Esteves, D., Zampieri, M., &#38; Ngonga Ngomo, A.-C. (2018). LIdioms: A Multilingual Linked Idioms Data Set. <i>The 11th Edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)</i>."},"title":"LIdioms: A Multilingual Linked Idioms Data Set","date_updated":"2023-08-16T09:33:54Z","author":[{"id":"71635","full_name":"Moussallem, Diego","last_name":"Moussallem","first_name":"Diego"},{"id":"67234","full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","first_name":"Mohamed"},{"first_name":"Diego","full_name":"Esteves, Diego","last_name":"Esteves"},{"first_name":"Marcos","last_name":"Zampieri","full_name":"Zampieri, Marcos"},{"last_name":"Ngonga Ngomo","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille"}],"date_created":"2021-12-17T09:57:23Z","status":"public","type":"conference","publication":"The 11th edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)","keyword":["lidiom sys:relevantFor:infai sys:relevantFor:bis group\\_aksw sherif simba dice moussallem esteves ngonga slipo sage projecthobbit geiser diesel simba"],"language":[{"iso":"eng"}],"_id":"29021","user_id":"67234"},{"title":"Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign","keyword":["2018 DICE SIMBA group_aksw ngonga projecthobbit roeder sherif"],"language":[{"iso":"eng"}],"_id":"46539","date_updated":"2026-03-09T12:49:48Z","department":[{"_id":"574"}],"user_id":"14972","author":[{"full_name":"Jiménez-Ruiz, Ernesto","last_name":"Jiménez-Ruiz","first_name":"Ernesto"},{"first_name":"Tzanina","full_name":"Saveta, Tzanina","last_name":"Saveta"},{"first_name":"Ondrej","last_name":"Zamazal","full_name":"Zamazal, Ondrej"},{"full_name":"Hertling, Sven","last_name":"Hertling","first_name":"Sven"},{"first_name":"Michael","full_name":"Röder, Michael","last_name":"Röder"},{"full_name":"Fundulaki, Irini","last_name":"Fundulaki","first_name":"Irini"},{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"},{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","full_name":"Sherif, Mohamed"},{"first_name":"Amina","last_name":"Annane","full_name":"Annane, Amina"},{"full_name":"Bellahsene, Zohra","last_name":"Bellahsene","first_name":"Zohra"},{"last_name":"Yahia","full_name":"Yahia, Sadok Ben","first_name":"Sadok Ben"},{"first_name":"Gayo","full_name":"Diallo, Gayo","last_name":"Diallo"},{"full_name":"Faria, Daniel","last_name":"Faria","first_name":"Daniel"},{"first_name":"Marouen","last_name":"Kachroudi","full_name":"Kachroudi, Marouen"},{"last_name":"Khiat","full_name":"Khiat, Abderrahmane","first_name":"Abderrahmane"},{"full_name":"Lambrix, Patrick","last_name":"Lambrix","first_name":"Patrick"},{"full_name":"Li, Huanyu","last_name":"Li","first_name":"Huanyu"},{"first_name":"Maximilian","last_name":"Mackeprang","full_name":"Mackeprang, Maximilian"},{"first_name":"Majid","full_name":"Mohammadi, Majid","last_name":"Mohammadi"},{"full_name":"Rybinski, Maciej","last_name":"Rybinski","first_name":"Maciej"},{"first_name":"Booma Sowkarthiga","last_name":"Balasubramani","full_name":"Balasubramani, Booma Sowkarthiga"},{"full_name":"Trojahn, Cassia","last_name":"Trojahn","first_name":"Cassia"}],"date_created":"2023-08-16T10:31:40Z","abstract":[{"lang":"eng","text":"This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this precampaign and discuss the future steps for the OAEI."}],"year":"2018","status":"public","citation":{"ama":"Jiménez-Ruiz E, Saveta T, Zamazal O, et al. Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign. In: <i>Proceedings of the Ontology Matching Workshop 2018</i>. ; 2018.","apa":"Jiménez-Ruiz, E., Saveta, T., Zamazal, O., Hertling, S., Röder, M., Fundulaki, I., Ngonga Ngomo, A.-C., Sherif, M., Annane, A., Bellahsene, Z., Yahia, S. B., Diallo, G., Faria, D., Kachroudi, M., Khiat, A., Lambrix, P., Li, H., Mackeprang, M., Mohammadi, M., … Trojahn, C. (2018). Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign. <i>Proceedings of the Ontology Matching Workshop 2018</i>.","bibtex":"@inproceedings{Jiménez-Ruiz_Saveta_Zamazal_Hertling_Röder_Fundulaki_Ngonga Ngomo_Sherif_Annane_Bellahsene_et al._2018, title={Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign}, booktitle={Proceedings of the Ontology Matching Workshop 2018}, author={Jiménez-Ruiz, Ernesto and Saveta, Tzanina and Zamazal, Ondrej and Hertling, Sven and Röder, Michael and Fundulaki, Irini and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Annane, Amina and Bellahsene, Zohra and et al.}, year={2018} }","short":"E. Jiménez-Ruiz, T. Saveta, O. Zamazal, S. Hertling, M. Röder, I. Fundulaki, A.-C. Ngonga Ngomo, M. Sherif, A. Annane, Z. Bellahsene, S.B. Yahia, G. Diallo, D. Faria, M. Kachroudi, A. Khiat, P. Lambrix, H. Li, M. Mackeprang, M. Mohammadi, M. Rybinski, B.S. Balasubramani, C. Trojahn, in: Proceedings of the Ontology Matching Workshop 2018, 2018.","mla":"Jiménez-Ruiz, Ernesto, et al. “Introducing the HOBBIT Platform into the Ontology Alignment Evaluation Campaign.” <i>Proceedings of the Ontology Matching Workshop 2018</i>, 2018.","ieee":"E. Jiménez-Ruiz <i>et al.</i>, “Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign,” 2018.","chicago":"Jiménez-Ruiz, Ernesto, Tzanina Saveta, Ondrej Zamazal, Sven Hertling, Michael Röder, Irini Fundulaki, Axel-Cyrille Ngonga Ngomo, et al. “Introducing the HOBBIT Platform into the Ontology Alignment Evaluation Campaign.” In <i>Proceedings of the Ontology Matching Workshop 2018</i>, 2018."},"publication":"Proceedings of the Ontology Matching Workshop 2018","type":"conference"},{"_id":"29032","user_id":"67234","keyword":["2017 group\\_aksw slipo sys:relevantFor:infai sys:relevantFor:bis ngonga simba DICE sherif geo-distance limes"],"language":[{"iso":"eng"}],"publication":"Semantic Web Journal","type":"journal_article","abstract":[{"lang":"eng","text":"Large amounts of geo-spatial information have been made available with the growth of the Web of Data. While discovering links between resources on the Web of Data has been shown to be a demanding task, discovering links between geo-spatial resources proves to be even more challenging. This is partly due to the resources being described by the means of vector geometry. Especially, discrepancies in granularity and error measurements across data sets render the selection of appropriate distance measures for geo-spatial resources difficult. In this paper, we survey existing literature for point-set measures that can be used to measure the similarity of vector geometries. We then present and evaluate the ten measures that we derived from literature. We evaluate these measures with respect to their time-efficiency and their robustness against discrepancies in measurement and in granularity. To this end, we use samples of real data sets of different granularity as input for our evaluation framework. The results obtained on three different data sets suggest that most distance approaches can be led to scale. Moreover, while some distance measures are significantly slower than other measures, distance measure based on means, surjections and sums of minimal distances are robust against the different types of discrepancies."}],"status":"public","date_updated":"2023-08-16T09:18:34Z","author":[{"first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","full_name":"Sherif, Mohamed"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"date_created":"2021-12-17T10:00:03Z","title":"A Systematic Survey of Point Set Distance Measures for Link Discovery","year":"2017","citation":{"ama":"Sherif M, Ngonga Ngomo A-C. A Systematic Survey of Point Set Distance Measures for Link Discovery. <i>Semantic Web Journal</i>. Published online 2017.","ieee":"M. Sherif and A.-C. Ngonga Ngomo, “A Systematic Survey of Point Set Distance Measures for Link Discovery,” <i>Semantic Web Journal</i>, 2017.","chicago":"Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “A Systematic Survey of Point Set Distance Measures for Link Discovery.” <i>Semantic Web Journal</i>, 2017.","bibtex":"@article{Sherif_Ngonga Ngomo_2017, title={A Systematic Survey of Point Set Distance Measures for Link Discovery}, journal={Semantic Web Journal}, author={Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2017} }","mla":"Sherif, Mohamed, and Axel-Cyrille Ngonga Ngomo. “A Systematic Survey of Point Set Distance Measures for Link Discovery.” <i>Semantic Web Journal</i>, 2017.","short":"M. Sherif, A.-C. Ngonga Ngomo, Semantic Web Journal (2017).","apa":"Sherif, M., &#38; Ngonga Ngomo, A.-C. (2017). A Systematic Survey of Point Set Distance Measures for Link Discovery. <i>Semantic Web Journal</i>."}},{"user_id":"67234","_id":"29018","language":[{"iso":"eng"}],"keyword":["2017 dice simba sherif radon ngonga slipo sage geiser hobbit group\\_aksw sys:relevantFor:infai sys:relevantFor:bis limes linkinglod sake diesel kevin sys:relevantFor:leds leds"],"publication":"Proceedings of Ontology Matching Workshop 2017","type":"conference","status":"public","author":[{"first_name":"Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif","id":"67234","full_name":"Sherif, Mohamed"},{"last_name":"Dreßler","id":"78256","full_name":"Dreßler, Kevin","first_name":"Kevin"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"}],"date_created":"2021-12-17T09:56:11Z","date_updated":"2023-08-16T09:23:30Z","title":"RADON results for OAEI 2017","citation":{"chicago":"Sherif, Mohamed, Kevin Dreßler, and Axel-Cyrille Ngonga Ngomo. “RADON Results for OAEI 2017.” In <i>Proceedings of Ontology Matching Workshop 2017</i>, 2017.","ieee":"M. Sherif, K. Dreßler, and A.-C. Ngonga Ngomo, “RADON results for OAEI 2017,” 2017.","ama":"Sherif M, Dreßler K, Ngonga Ngomo A-C. RADON results for OAEI 2017. In: <i>Proceedings of Ontology Matching Workshop 2017</i>. ; 2017.","apa":"Sherif, M., Dreßler, K., &#38; Ngonga Ngomo, A.-C. (2017). RADON results for OAEI 2017. <i>Proceedings of Ontology Matching Workshop 2017</i>.","mla":"Sherif, Mohamed, et al. “RADON Results for OAEI 2017.” <i>Proceedings of Ontology Matching Workshop 2017</i>, 2017.","bibtex":"@inproceedings{Sherif_Dreßler_Ngonga Ngomo_2017, title={RADON results for OAEI 2017}, booktitle={Proceedings of Ontology Matching Workshop 2017}, author={Sherif, Mohamed and Dreßler, Kevin and Ngonga Ngomo, Axel-Cyrille}, year={2017} }","short":"M. Sherif, K. Dreßler, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2017, 2017."},"year":"2017"}]