TY - CONF AU - Trentinaglia, Roman AU - Merschjohann, Sven AU - Fockel, Markus AU - Eikerling, Hendrik ID - 43395 SN - 0302-9743 T2 - REFSQ 2023: Requirements Engineering: Foundation for Software Quality TI - Eliciting Security Requirements – An Experience Report ER - TY - CHAP AU - Castenow, Jannik AU - Harbig, Jonas AU - Meyer auf der Heide, Friedhelm ID - 44769 SN - 0302-9743 T2 - Lecture Notes in Computer Science TI - Unifying Gathering Protocols for Swarms of Mobile Robots ER - TY - CHAP AB - 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. AU - Ahmed, Abdullah Fathi Ahmed AU - Firmansyah, Asep Fajar AU - Sherif, Mohamed AU - Moussallem, Diego AU - Ngonga Ngomo, Axel-Cyrille ID - 46516 SN - 0302-9743 T2 - Natural Language Processing and Information Systems TI - Explainable Integration of Knowledge Graphs Using Large Language Models ER - TY - CHAP AB - Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world’s largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research’s domain range. AU - Gusmita, Ria Hari AU - Firmansyah, Asep Fajar AU - Moussallem, Diego AU - Ngonga Ngomo, Axel-Cyrille ID - 46572 SN - 0302-9743 T2 - Natural Language Processing and Information Systems TI - IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran ER - TY - CHAP AU - Dieter, Peter ID - 46867 SN - 0302-9743 T2 - Lecture Notes in Computer Science TI - A Regret Policy for the Dynamic Vehicle Routing Problem with Time Windows ER - TY - CHAP AB - Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (ALC). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic ALCHIQ(D). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2. AU - Kouagou, N'Dah Jean AU - Heindorf, Stefan AU - Demir, Caglar AU - Ngonga Ngomo, Axel-Cyrille ID - 47421 SN - 0302-9743 T2 - Machine Learning and Knowledge Discovery in Databases: Research Track TI - Neural Class Expression Synthesis in ALCHIQ(D) ER - TY - JOUR AU - Kornowicz, Jaroslaw AU - Thommes, Kirsten ID - 47953 JF - Artificial Intelligence in HCI SN - 0302-9743 TI - Aggregating Human Domain Knowledge for Feature Ranking ER - TY - CONF AB - Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC. AU - Qudus, Umair AU - Röder, Michael AU - Kirrane, Sabrina AU - Ngomo, Axel-Cyrille Ngonga ED - R. Payne, Terry ED - Presutti, Valentina ED - Qi, Guilin ED - Poveda-Villalón, María ED - Stoilos, Giorgos ED - Hollink, Laura ED - Kaoudi, Zoi ED - Cheng, Gong ED - Li, Juanzi ID - 50479 KW - temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs SN - 0302-9743 T2 - The Semantic Web – ISWC 2023 TI - TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs VL - 14265 ER - TY - CHAP AU - Alt, Christoph AU - Kenter, Tobias AU - Faghih-Naini, Sara AU - Faj, Jennifer AU - Opdenhövel, Jan-Oliver AU - Plessl, Christian AU - Aizinger, Vadym AU - Hönig, Jan AU - Köstler, Harald ID - 46191 SN - 0302-9743 T2 - Lecture Notes in Computer Science TI - Shallow Water DG Simulations on FPGAs: Design and Comparison of a Novel Code Generation Pipeline ER - TY - CONF AU - Hanselle, Jonas Manuel AU - Fürnkranz, Johannes AU - Hüllermeier, Eyke ID - 51373 SN - 0302-9743 T2 - 26th International Conference on Discovery Science TI - Probabilistic Scoring Lists for Interpretable Machine Learning VL - 14050 ER - TY - CHAP AU - Muschalik, Maximilian AU - Fumagalli, Fabian AU - Hammer, Barbara AU - Huellermeier, Eyke ID - 48776 SN - 0302-9743 T2 - Machine Learning and Knowledge Discovery in Databases: Research Track TI - iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams ER - TY - CHAP AU - de Camargo e Souza Câmara, Igor AU - Turhan, Anni-Yasmin ID - 52859 SN - 0302-9743 T2 - Logics in Artificial Intelligence TI - Deciding Subsumption in Defeasible $$\mathcal {ELI}_\bot $$ with Typicality Models ER - TY - CONF AU - Hansmeier, Tim AU - Platzner, Marco ID - 30971 SN - 0302-9743 T2 - Applications of Evolutionary Computation, EvoApplications 2022, Proceedings TI - Integrating Safety Guarantees into the Learning Classifier System XCS VL - 13224 ER - TY - CHAP AU - Bondarenko, Alexander AU - Fröbe, Maik AU - Kiesel, Johannes AU - Syed, Shahbaz AU - Gurcke, Timon AU - Beloucif, Meriem AU - Panchenko, Alexander AU - Biemann, Chris AU - Stein, Benno AU - Wachsmuth, Henning AU - Potthast, Martin AU - Hagen, Matthias ID - 34077 SN - 0302-9743 T2 - Lecture Notes in Computer Science TI - Overview of Touché 2022: Argument Retrieval ER - TY - CHAP AU - Wolters, Dennis AU - Engels, Gregor ED - Taibi, Davide ED - Kuhrmann, Marco ED - Mikkonen, Tommi ED - Klünder, Jil ED - Abrahamsson, Pekka ID - 34292 SN - 0302-9743 T2 - Product-Focused Software Process Improvement TI - Towards Situational Process Management for Professional Education Programmes VL - 13709 ER - TY - CHAP AU - Maack, Marten AU - Meyer auf der Heide, Friedhelm AU - Pukrop, Simon ID - 29872 SN - 0302-9743 T2 - Approximation and Online Algorithms TI - Server Cloud Scheduling ER - TY - CHAP AU - KOUAGOU, N'Dah Jean AU - Heindorf, Stefan AU - Demir, Caglar AU - Ngonga Ngomo, Axel-Cyrille ID - 33740 SN - 0302-9743 T2 - The Semantic Web TI - Learning Concept Lengths Accelerates Concept Learning in ALC ER - TY - CHAP AU - Wohlleben, Meike Claudia AU - Bender, Amelie AU - Peitz, Sebastian AU - Sextro, Walter ID - 29727 SN - 0302-9743 T2 - Machine Learning, Optimization, and Data Science TI - Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction ER - TY - CHAP AU - Zahera, Hamada Mohamed Abdelsamee AU - Heindorf, Stefan AU - Balke, Stefan AU - Haupt, Jonas AU - Voigt, Martin AU - Walter, Carolin AU - Witter, Fabian AU - Ngonga Ngomo, Axel-Cyrille ID - 33738 SN - 0302-9743 T2 - The Semantic Web: ESWC 2022 Satellite Events TI - Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings ER - TY - CHAP AU - Zahera, H.M.A AU - Vollmers, Daniel AU - Sherif, Mohamed Ahmed AU - Ngomo, Axel-Cyrille Ngonga ID - 38506 SN - 0302-9743 T2 - The Semantic Web – ISWC 2022 TI - MultPAX: Keyphrase Extraction Using Language Models and Knowledge Graphs ER -