@article{35551, author = {{Ali, Waqas and Saleem, Muhammad and Yao, Bin and Hogan, Aidan and Ngonga Ngomo, Axel-Cyrille}}, journal = {{VLDB J.}}, number = {{3}}, pages = {{1–26}}, title = {{{A survey of RDF stores & SPARQL engines for querying knowledge graphs}}}, doi = {{10.1007/s00778-021-00711-3}}, volume = {{31}}, year = {{2022}}, } @article{35552, author = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, journal = {{Softw. Impacts}}, pages = {{100377}}, title = {{{Hardware-agnostic computation for large-scale knowledge graph embeddings}}}, doi = {{10.1016/j.simpa.2022.100377}}, volume = {{13}}, year = {{2022}}, } @inproceedings{33957, abstract = {{Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.}}, author = {{Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}}, keywords = {{Assistance system, Knowledge graph, Information retrieval, Neural networks, AR}}, location = {{Stuttgart}}, title = {{{AI-Based Assistance System for Manufacturing}}}, doi = {{10.1109/ETFA52439.2022.9921520}}, year = {{2022}}, } @inbook{33740, author = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web}}, isbn = {{9783031069802}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Learning Concept Lengths Accelerates Concept Learning in ALC}}}, doi = {{10.1007/978-3-031-06981-9_14}}, year = {{2022}}, } @inproceedings{29290, abstract = {{Classifying nodes in knowledge graphs is an important task, e.g., predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner - an evolutionary approach to learn ALCQ(D), which is the attributive language with complement (ALC) paired with qualified cardinality restrictions (Q) and data properties (D). We contribute a novel initialization method for the initial population: starting from positive examples (nodes in the knowledge graph), we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.}}, author = {{Heindorf, Stefan and Blübaum, Lukas and Düsterhus, Nick and Werner, Till and Golani, Varun Nandkumar and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{WWW}}, pages = {{818--828}}, publisher = {{ACM}}, title = {{{EvoLearner: Learning Description Logics with Evolutionary Algorithms}}}, doi = {{10.1145/3485447.3511925}}, year = {{2022}}, } @article{29851, author = {{Pestryakova, Svetlana and Vollmers, Daniel and Sherif, Mohamed and Heindorf, Stefan and Saleem, Muhammad and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, journal = {{Scientific Data}}, title = {{{CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications}}}, doi = {{10.1038/s41597-022-01298-2}}, year = {{2022}}, } @inproceedings{34674, abstract = {{Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation.}}, author = {{Sieger, Leonie Nora and Hermann, Julia and Schomäcker, Astrid and Heindorf, Stefan and Meske, Christian and Hey, Celine-Chiara and Doğangün, Ayşegül}}, booktitle = {{International Conference on Human-Agent Interaction}}, keywords = {{human-agent interaction, smart homes, supervised learning, participation}}, location = {{Christchurch, New Zealand}}, publisher = {{ACM}}, title = {{{User Involvement in Training Smart Home Agents}}}, doi = {{10.1145/3527188.3561914}}, year = {{2022}}, } @inbook{54585, author = {{Manzoor, Ali and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web – ISWC 2022}}, isbn = {{9783031194320}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{REBench: Microbenchmarking Framework for Relation Extraction Systems}}}, doi = {{10.1007/978-3-031-19433-7_37}}, year = {{2022}}, } @inbook{33738, author = {{Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Balke, Stefan and Haupt, Jonas and Voigt, Martin and Walter, Carolin and Witter, Fabian and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web: ESWC 2022 Satellite Events}}, isbn = {{9783031116087}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings}}}, doi = {{10.1007/978-3-031-11609-4_9}}, year = {{2022}}, } @inproceedings{33739, abstract = {{At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark.}}, author = {{Bondarenko, Alexander and Wolska, Magdalena and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille and Stein, Benno and Braslavski, Pavel and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, pages = {{3296–3308}}, publisher = {{International Committee on Computational Linguistics}}, title = {{{CausalQA: A Benchmark for Causal Question Answering}}}, year = {{2022}}, } @article{34640, author = {{Schloots, Franziska Margarete}}, issn = {{2192-5445}}, journal = {{Rabbit Eye - Zeitschrift für Filmforschung}}, keywords = {{Wearable, selft-tracking, Selbstvermessung, Animation, Tamagotchi, Anschaulichkeit}}, pages = {{65--77}}, title = {{{Die Tamagotchisierung des Selbst. Zur Anschaulichkeit von animierten Körperdaten}}}, volume = {{12}}, year = {{2022}}, } @article{34614, abstract = {{Mit steigenden Optimierungsanforderungen an das Individuum wächst auch das indivi- duelle Bedürfnis nach Kontrolle. Dieses kann u. a. durch self tracking-Technologien erfüllt werden. Anhand von drei Fallbeispielen – der Personenwaage, dem Wearable und dem habit tracker – zeigt dieser Aufsatz, wie sich medienbasierte Selbsttechnologien im historischen Verlauf intensiviert und stärker in den Alltag integriert haben. Ein besonderer Fokus liegt dabei auf der Ambivalenz dieser Medien: Ermöglichen sie auf der einen Seite zwar eine Selbstkontrolle und stellen so potenziell sta- bilisierende Ressourcen für das Individuum dar, schaffen sie auf der anderen Seite auch neue Anforderungen, die es zu erfüllen gilt.}}, author = {{Schloots, Franziska Margarete}}, journal = {{ffk Journal}}, keywords = {{self-tracking, Selbsttechnologien, Wearable, Bullet Journal, Personenwaage, Selbstvermessung}}, number = {{7}}, pages = {{74--91}}, title = {{{‚Understand what’s happening within‘. Selbstkontrolle mit Personenwaage, Wearable und habit tracker}}}, doi = {{10.25969/MEDIAREP/18238}}, volume = {{6}}, year = {{2022}}, } @article{24721, author = {{Fathi Ahmed, Abdullah and Ahmed Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, journal = {{Data Knowl. Eng.}}, pages = {{101874}}, title = {{{Multilingual Verbalization and Summarization for Explainable Link Discovery}}}, doi = {{10.1016/j.datak.2021.101874}}, volume = {{133}}, year = {{2021}}, } @inproceedings{24722, author = {{Röder, Michael and Frerk, Philip and Conrads, Felix and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual Event, June 6-10, 2021, Proceedings}}, editor = {{Verborgh, Ruben and Hose, Katja and Paulheim, Heiko and Champin, Pierre-Antoine and Maleshkova, Maria and Corcho, Oscar and Ristoski, Petar and Alam, Mehwish}}, pages = {{93--108}}, publisher = {{Springer}}, title = {{{Applying Grammar-Based Compression to RDF}}}, doi = {{10.1007/978-3-030-77385-4\_6}}, volume = {{12731}}, year = {{2021}}, } @inproceedings{24723, author = {{Ali, Manzoor and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web: {ESWC} 2021 Satellite Events - Virtual Event, June 6-10, 2021, Revised Selected Papers}}, editor = {{Verborgh, Ruben and Dimou, Anastasia and Hogan, Aidan and d'Amato, Claudia and Tiddi, Ilaria and Br{\"{o}}ring, Arne and Maier, Simon and Ongenae, Femke and Tommasini, Riccardo and Alam, Mehwish}}, pages = {{136--140}}, publisher = {{Springer}}, title = {{{Unsupervised Relation Extraction Using Sentence Encoding}}}, doi = {{10.1007/978-3-030-80418-3\_25}}, volume = {{12739}}, year = {{2021}}, } @inproceedings{24724, author = {{Ali, Manzoor and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web: {ESWC} 2021 Satellite Events - Virtual Event, June 6-10, 2021, Revised Selected Papers}}, editor = {{Verborgh, Ruben and Dimou, Anastasia and Hogan, Aidan and d'Amato, Claudia and Tiddi, Ilaria and Br{\"{o}}ring, Arne and Maier, Simon and Ongenae, Femke and Tommasini, Riccardo and Alam, Mehwish}}, pages = {{136--140}}, publisher = {{Springer}}, title = {{{Unsupervised Relation Extraction Using Sentence Encoding}}}, doi = {{10.1007/978-3-030-80418-3\_25}}, volume = {{12739}}, year = {{2021}}, } @inproceedings{24725, author = {{Shahzad, Moemmur and Amin, Ayesha and Esteves, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the Thirty-Fourth International Florida Artificial Intelligence Research Society Conference, North Miami Beach, Florida, USA, May 17-19, 2021}}, editor = {{Bell, Eric and Keshtkar, Fazel}}, title = {{{InferNER: an attentive model leveraging the sentence-level information for Named Entity Recognition in Microblogs}}}, doi = {{10.32473/flairs.v34i1.128538}}, year = {{2021}}, } @inproceedings{24726, author = {{Röder, Michael and Thuy Sy Nguyen, Pham and Conrads, Felix and Alexandra Morim da Silva, Ana and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{15th {IEEE} International Conference on Semantic Computing, {ICSC} 2021, Laguna Hills, CA, USA, January 27-29, 2021}}, pages = {{62--69}}, publisher = {{{IEEE}}}, title = {{{Lemming - Example-based Mimicking of Knowledge Graphs}}}, doi = {{10.1109/ICSC50631.2021.00015}}, year = {{2021}}, } @inproceedings{24727, author = {{Amer Desouki, Abdelmoneim and Conrads, Felix and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{15th {IEEE} International Conference on Semantic Computing, {ICSC} 2021, Laguna Hills, CA, USA, January 27-29, 2021}}, pages = {{76--79}}, publisher = {{{IEEE}}}, title = {{{SYNTHG: Mimicking RDF Graphs Using Tensor Factorization}}}, doi = {{10.1109/ICSC50631.2021.00017}}, year = {{2021}}, } @inproceedings{24728, author = {{Demir, Caglar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{15th {IEEE} International Conference on Semantic Computing, {ICSC} 2021, Laguna Hills, CA, USA, January 27-29, 2021}}, pages = {{179--182}}, publisher = {{{IEEE}}}, title = {{{A shallow neural model for relation prediction}}}, doi = {{10.1109/ICSC50631.2021.00038}}, year = {{2021}}, }