@article{25211,
  author       = {{Vollmers, Daniel and Jalota, Rricha and Moussallem, Diego and Topiwala, Hardik and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}},
  journal      = {{CoRR}},
  title        = {{{Knowledge Graph Question Answering using Graph-Pattern Isomorphism}}},
  volume       = {{abs/2103.06752}},
  year         = {{2021}},
}

@inproceedings{29043,
  abstract     = {{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.}},
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Jalota, Rricha and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{IEEE Open Access}},
  keywords     = {{sys:relevantFor:infai sys:relevantFor:DAIKIRI ngonga zahera sherif daikiriproject dice simba}},
  title        = {{{I-AID: Identifying Actionable Information from Disaster-related Tweets}}},
  year         = {{2021}},
}

@inproceedings{29003,
  abstract     = {{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.}},
  author       = {{Zahera, Hamada Mohamed Abdelsamee and A. Elgendy, Ibrahim and Jalota, Rricha and Sherif, Mohamed}},
  booktitle    = {{Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019}},
  keywords     = {{zahera elgendy jalota sherif dice}},
  title        = {{{Fine-tuned BERT Model for Multi-Label Tweets Classification}}},
  year         = {{2019}},
}

@inbook{57286,
  author       = {{Jalota, Rricha and Srivastava, Nikit and Vollmers, Daniel and Speck, René and Röder, Michael and Usbeck, Ricardo and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Rich Search and Discovery for Research Datasets}},
  keywords     = {{dice jalota ngonga roeder speck srivastava vollmers}},
  publisher    = {{SAGE Publications}},
  title        = {{{Finding Datasets in Publications: The University of Paderborn Approach}}},
  year         = {{2019}},
}