@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}}, } @article{29005, abstract = {{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++.}}, author = {{Fathi Ahmed, Abdullah and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, issn = {{0169-023X}}, journal = {{Data & Knowledge Engineering}}, keywords = {{2021 sys:relevantFor:infai simba sherif ngonga ahmed limes dice raki moussallem libo opal knowgraphs}}, pages = {{101874}}, title = {{{Multilingual Verbalization and Summarization for Explainable Link Discovery}}}, doi = {{https://doi.org/10.1016/j.datak.2021.101874}}, year = {{2021}}, } @inproceedings{29044, author = {{Chakraborty, Jaydeep and Sherif, Mohamed and Zahera, Hamada Mohamed Abdelsamee and Bansal, Srividya}}, booktitle = {{Proceedings of the IEEE International Conference on Machine Learning and Applications}}, keywords = {{dice sherif hamada}}, title = {{{OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling}}}, year = {{2021}}, } @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{29040, author = {{Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed}}, booktitle = {{Proceedings of Mining the Web of HTML-embedded Product Data Workshop (MWPD2020)}}, keywords = {{2020 dice zahera sherif knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal}}, title = {{{ProBERT: Product Data Classification with Fine-tuning BERT Model}}}, 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}}, } @inproceedings{29037, abstract = {{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.}}, author = {{Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{K-CAP 2019: Knowledge Capture Conference}}, keywords = {{sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera sherif solide limboproject opal group\_aksw dice}}, pages = {{4}}, title = {{{Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction}}}, 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}}, } @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}}, } @inproceedings{29038, abstract = {{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.}}, author = {{Ahmed, Abdullah Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{K-CAP 2019: Knowledge Capture Conference}}, keywords = {{sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:ngonga ahmed sherif solide limboproject opal group_aksw dice}}, title = {{{Do your Resources Sound Similar? On the Impact of Using Phonetic Similarity in Link Discovery}}}, year = {{2019}}, } @inproceedings{29012, abstract = {{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.}}, author = {{Fathi Ahmed, Abdullah and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)}}, keywords = {{2019 sys:relevantFor:infai group\_aksw simba sherif ngonga ahmed slipo limes dice sage limbo opal}}, publisher = {{Springer}}, title = {{{LSVS: Link Specification Verbalization and Summarization}}}, year = {{2019}}, } @inproceedings{29013, abstract = {{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.}}, 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}}, booktitle = {{International Conference on Extending Database Technology 2019, EDBT19}}, keywords = {{2019 sys:relevantFor:infai group\_aksw simba sherif ngonga lehmann slipo limes dice deer}}, title = {{{Big POI data integration with Linked Data technologies}}}, year = {{2019}}, } @inproceedings{29029, author = {{Fathi Ahmed, Abdullah and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of Ontology Matching Workshop 2018}}, keywords = {{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}}, title = {{{RADON2: A buffered-Intersection Matrix Computing Approach To Accelerate Link Discovery Over Geo-Spatial RDF Knowledge Bases (OAEI2018 Results)}}}, year = {{2018}}, } @inproceedings{29021, author = {{Moussallem, Diego and Sherif, Mohamed and Esteves, Diego and Zampieri, Marcos and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The 11th edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan)}}, keywords = {{lidiom sys:relevantFor:infai sys:relevantFor:bis group\_aksw sherif simba dice moussallem esteves ngonga slipo sage projecthobbit geiser diesel simba}}, title = {{{LIdioms: A Multilingual Linked Idioms Data Set}}}, year = {{2018}}, } @inproceedings{46539, abstract = {{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.}}, 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 Yahia, Sadok Ben and Diallo, Gayo and Faria, Daniel and Kachroudi, Marouen and Khiat, Abderrahmane and Lambrix, Patrick and Li, Huanyu and Mackeprang, Maximilian and Mohammadi, Majid and Rybinski, Maciej and Balasubramani, Booma Sowkarthiga and Trojahn, Cassia}}, booktitle = {{Proceedings of the Ontology Matching Workshop 2018}}, keywords = {{2018 DICE SIMBA group_aksw ngonga projecthobbit roeder sherif}}, title = {{{Introducing the HOBBIT platform into the Ontology Alignment Evaluation Campaign}}}, year = {{2018}}, } @article{29032, abstract = {{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.}}, author = {{Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, journal = {{Semantic Web Journal}}, keywords = {{2017 group\_aksw slipo sys:relevantFor:infai sys:relevantFor:bis ngonga simba DICE sherif geo-distance limes}}, title = {{{A Systematic Survey of Point Set Distance Measures for Link Discovery}}}, 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}}, }