@inproceedings{67344,
  author       = {{Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}},
  booktitle    = {{The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings}},
  keywords     = {{becker dice enexa kiowl ngonga sailproject sherif trr318_inf whale}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{TIM: Tiered Iterative Knowledge Graph Matching}}},
  year         = {{2026}},
}

@inproceedings{67341,
  author       = {{Ekinci Birol, Duygu and KOUAGOU, N'Dah Jean and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{IEEE International Conference on Data Mining (ICDM) 2026}},
  keywords     = {{dice duygu fairomics kouagou ngonga sail sherif trr318 whale}},
  title        = {{{ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings}}},
  year         = {{2026}},
}

@inproceedings{62119,
  author       = {{Ihtassine, Reda and Firmansyah, Asep Fajar and Srivastava, Nikit and Ali, Manzoor and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}},
  booktitle    = {{Proceedings of the 12th Knowledge Capture Conference 2025, {K-CAP} 2025, The Thirteenth International Conference on Knowledge Capture, December 10 - 12, 2025, Dayton, Ohio, USA}},
  keywords     = {{Srivastava ali dice enexa firmansyah ihtassine ngonga sailproject sherif whale}},
  publisher    = {{ACM}},
  title        = {{{NL2LS: LLM-based Automatic Linking of Knowledge Graphs}}},
  year         = {{2025}},
}

@inproceedings{59054,
  author       = {{Firmansyah, Asep Fajar and Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{ESWC2025}},
  isbn         = {{978-3-031-94575-5}},
  keywords     = {{firmansyah mousallem ngonga sherif zahera}},
  pages        = {{133----151}},
  publisher    = {{pringer Nature Switzerland}},
  title        = {{{ANTS: Abstractive Entity Summarization in Knowledge Graphs}}},
  doi          = {{10.1007/978-3-031-94575-5_8}},
  year         = {{2025}},
}

@inproceedings{61202,
  abstract     = {{The number of datasets on the web of data increases continuously. However, the knowledge contained therein cannot be fully utilized without finding links between the entities contained in these datasets. Equivalent entities can not be identified solely by checking the equivalence of IRIs because of the different origins and naming schemes of different data providers. Yet, such equivalences can be discovered by computing the similarity of their attributes. In this paper we propose GLIDE, an approach that links entities from two different datasets by embedding a joint model of these datasets enriched by additional relations describing the similarity of literals. The joint model is embedded into a latent vector space while paying attention to juxtaposing similar literals. We evaluate our approach against state-of-the-art algorithms using real-world datasets commonly used in link discovery literature. The results show that GLIDE outperforms all baselines on 5 of 7 datasets with perfect or near-perfect accuracy. Our approach achieves its best performance on datasets that feature several literals with similarities. Our experiments indicate that researchers should not only pay attention to equal literals in knowledge graph embedding but should also be aware of the distance between similar literals.}},
  author       = {{Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}},
  booktitle    = {{The Semantic Web – ISWC 2025}},
  keywords     = {{becker sherif enexa sailproject dice simba ngonga whale}},
  title        = {{{GLIDE: Knowledge Graph Linking using Distance-Aware Embeddings}}},
  year         = {{2025}},
}

@inproceedings{67338,
  author       = {{Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}},
  booktitle    = {{OM 2025: The 20th International Workshop on Ontology Matching collocated with the 24th International Semantic Web Conference(ISWC 2025), November 2nd, 2025, Nara, Japan}},
  keywords     = {{becker dice enexa ngonga sailproject sherif whale}},
  pages        = {{166--171}},
  publisher    = {{CEUR-WS}},
  title        = {{{TIM Results for OAEI 2025}}},
  volume       = {{4144}},
  year         = {{2025}},
}

@inbook{61210,
  abstract     = {{Knowledge graphs (KGs) differ significantly over multiple different versions of the same data source. They also often contain blank nodes that do not have a constant identifier over all versions. Linking such blank nodes from different versions is a challenging task. Previous works propose different approaches to create signatures for all blank nodes based on named nodes in their neighborhood to match blank nodes with similar signatures. However, these works struggle to find a good mapping when the difference between the KGs’ versions grows too large. In this work, we propose Blink, an embedding-based approach for blank node linking. Blink merges two KGs’ versions and embeds the merged graph into a latent vector space based on translational embeddings and subsequently matches the closest pairs of blank nodes from different graphs. We evaluate our approach using real-world datasets against state-of-the-art approaches by computing the blank node matching for isomorphic graphs and graphs that contain triple changes (i.e., added or removed triples). The results indicate that Blink achieves perfect accuracy for isomorphic graphs. For graph versions that contain changes, such as having up to 20% of triples removed in one version, Blink still produces a mapping with an Optimal Mapping Deviation Ratio of under 1%. These results show that Blink leads to a better linking of KGs over different versions and similar graphs adhering to the linked data guidelines.}},
  author       = {{Becker, Alexander and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783031778438}},
  issn         = {{0302-9743}},
  location     = {{Baltimore, USA}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Blink: Blank Node Matching Using Embeddings}}},
  doi          = {{10.1007/978-3-031-77844-5_12}},
  year         = {{2024}},
}

@inproceedings{54084,
  author       = {{Karalis, Nikolaos and Bigerl, Alexander and Heidrich, Liss and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{ESWC}},
  keywords     = {{bigerl dice enexa heidrich karalis ngonga sail sherif}},
  title        = {{{Efficient Evaluation of Conjunctive Regular Path Queries Using Multi-way Joins}}},
  year         = {{2024}},
}

@inproceedings{61219,
  author       = {{Kumar, Ajay and Naumann, Marius and Henne, Kevin and Sherif, Mohamed}},
  booktitle    = {{Joint Proceedings of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems co-located with 20th International Conference on Semantic Systems (SEMANTiCS 2024), Amsterdam, The Netherlands, September 17-19, 2024}},
  editor       = {{Garijo, Daniel and Gentile, Anna Lisa and Kurteva, Anelia and Mannocci, Andrea and Osborne, Francesco and Vahdati, Sahar}},
  keywords     = {{kumar sherif enexa climatebowl ingrid simba dice whale}},
  location     = {{ Amsterdam,The Netherlands}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{PCFWebUI: Data-driven WebUI for holistic decarbonization based on PCF-Tracking}}},
  volume       = {{3759}},
  year         = {{2024}},
}

@inproceedings{55094,
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Manzoor, Ali and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{TRR318 climatebowl colide dice enexa kiam manzoor moussallem ngonga sailproject sherif simba zahera}},
  title        = {{{Generating SPARQL from Natural Language Using Chain-of-Thoughts Prompting}}},
  year         = {{2024}},
}

@inbook{46516,
  abstract     = {{Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by devising NMV-LS, a language model-based verbalization approach for translating complex link specifications into natural language. NMV-LS relies on the results of rule-based link specification verbalization to apply continuous training on T5, a large language model based on the Transformerarchitecture. We evaluated NMV-LS on English and German datasets using well-known machine translation metrics such as BLUE, METEOR, ChrF++ and TER. Our results suggest that our approach achieves a verbalization performance close to that of humans and outperforms state of the art approaches. Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.}},
  author       = {{Ahmed, Abdullah Fathi Ahmed and Firmansyah, Asep Fajar and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Natural Language Processing and Information Systems}},
  isbn         = {{9783031353192}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Explainable Integration of Knowledge Graphs Using Large Language Models}}},
  doi          = {{10.1007/978-3-031-35320-8_9}},
  year         = {{2023}},
}

@inproceedings{54608,
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Vitiugin, Fedor and Sherif, Mohamed and Castillo, Carlos and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{dice kiam ngonga porque sherif zahera}},
  title        = {{{Using Pre-trained Language Models for Abstractive DBPEDIA Summarization: A Comparative Study}}},
  year         = {{2023}},
}

@inbook{54613,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing – Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  keywords     = {{dice ngonga sfb901 sherif}},
  pages        = {{85–104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inproceedings{54616,
  author       = {{Becker, Alexander and Ahmed, Abdullah Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{ahmed becker dice ngonga sail sherif}},
  title        = {{{COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge Graphs}}},
  year         = {{2023}},
}

@misc{45560,
  author       = {{Ali, Manzoor and Saleem, Muhammad and Moussallem, Diego and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  publisher    = {{LibreCat University}},
  title        = {{{RELD: A Knowledge Graph of Relation Extraction Datasets}}},
  doi          = {{10.5281/ZENODO.7429677}},
  year         = {{2023}},
}

@inbook{45884,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{85--104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inproceedings{46514,
  abstract     = {{Purpose: Data integration and applications across knowledge graphs (KGs) rely heavily on the discovery of links between resources within these KGs. Geospatial link discovery algorithms have to deal with millions of point sets containing billions of points. 
Methodology: To speed up the discovery of geospatial links, we propose COBALT. COBALT combines the content measures with R-tree indexing. The content measures are based on the area, diagonal and distance of the minimum bounding boxes of the polygons which speeds up the process but is not perfectly accurate. We thus propose two polygon splitting approaches for improving the accuracy of COBALT. 
Findings: Our experiments on real-world datasets show that COBALT is able to speed up the topological relation discovery over geospatial KGs by up to 1.47 × 104 times over state-of-the-art linking algorithms while maintaining an F-Measure between 0.7 and 0.9 depending on the relation. Furthermore, we were able to achieve an F-Measure of up to 0.99 by applying our polygon splitting approaches before applying the content measures. 
Value: The process of discovering links between geospatial resources can be significantly faster by sacrificing the optimality of the results. This is especially important for real time data-driven applications such as emergency response, location-based services and traffic management. In future work, additional measures, like the location of polygons or the name of the entity represented by the polygon, could be integrated to further improve the accuracy of the results.}},
  author       = {{Becker, Alexander and Ahmed, Abdullah Fathi Ahmed and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{ahmed becker dice ngonga sail sherif}},
  location     = {{Leipzig, Germany}},
  title        = {{{COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge Graphs}}},
  year         = {{2023}},
}

@inproceedings{46518,
  abstract     = {{Purpose: This study addresses the limitations of current short abstracts of DBpedia entities, which often lack a comprehensive overview due to their creating method (i.e., selecting the first two-three sentences from the full DBpedia abstracts).
Methodology: We leverage pre-trained language models to generate abstractive summaries of DBpedia abstracts in six languages (English, French, German, Italian, Spanish, and Dutch). We performed several experiments to assess the quality of generated summaries by language models. In particular, we evaluated the generated summaries using human judgments and automated metrics (Self-ROUGE and BERTScore). Additionally, we studied the correlation between human judgments and automated metrics in evaluating the generated summaries under different aspects: informativeness, coherence, conciseness, and fluency.
Findings: Pre-trained language models generate summaries more concise and informative than existing short abstracts. Specifically, BART-based models effectively overcome the limitations of DBpedia short abstracts, especially for longer ones.
Moreover, we show that BERTScore and ROUGE-1 are reliable metrics for assessing the informativeness and coherence of the generated summaries with respect to the full DBpedia abstracts. We also find a negative correlation between conciseness and human ratings. Furthermore, fluency evaluation remains challenging without human judgment.
Value: This study has significant implications for various applications in machine learning and natural language processing that rely on DBpedia resources. By providing succinct and comprehensive summaries, our approach enhances the quality of DBpedia abstracts and contributes to the semantic web community}},
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Vitiugin, Fedor and Sherif, Mohamed and Castillo, Carlos and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{dice enexa kiam ngonga porque sherif zahera}},
  location     = {{Leipzig, Germany}},
  title        = {{{Using Pre-trained Language Models for Abstractive DBpedia Summarization: A Comparative Study}}},
  year         = {{2023}},
}

@misc{45558,
  abstract     = {{Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (Ingrid) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present IngridKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of IngridKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.}},
  author       = {{Sherif, Mohamed and Morim da Silva, Ana Alexandra and Pestryakova, Svetlana and Ahmed, Abdullah Fathi Ahmed and Niemann, Sven and Ngonga Ngomo, Axel-Cyrille}},
  publisher    = {{LibreCat University}},
  title        = {{{IngridKG: A FAIR Knowledge Graph of Graffiti}}},
  doi          = {{10.5281/ZENODO.7560242}},
  year         = {{2023}},
}

@inbook{54588,
  author       = {{Manzoor, Ali and Saleem, Muhammad and Moussallem, Diego and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web}},
  isbn         = {{9783031334542}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{RELD: A Knowledge Graph of Relation Extraction Datasets}}},
  doi          = {{10.1007/978-3-031-33455-9_20}},
  year         = {{2023}},
}