ESLM: Improving Entity Summarization by Leveraging Language Models - Firmansyah, Asep Fajar; Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga (The Semantic Web)

Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches - Mindlin, Dimitry; Beer, Fabian; Sieger, Leonie Nora; Heindorf, Stefan; Cimiano, Philipp; Esposito, Elena; Ngonga-Ngomo, Axel-Cyrille
In the last decade, there has been increasing interest in allowing users to understand how the predictions of machine-learned models come about, thus increasing transparency and empowering users to understand and potentially contest those decisions.Dialogue-based approaches, in contrast to traditional one-shot eXplainable Artificial Intelligence (XAI) methods, facilitate interactive, in-depth exploration through multi-turn dialogues, simulating expert conversations. This paper reviews the current state of dialogue-based XAI, presenting a systematic review of 1,339 publications, narrowed down to 14 based on inclusion criteria. We explore theoretical foundations of the systems, propose key dimensions along which different solutions to dialogue-based XAI differ, and identify key use cases, target audiences, system components, and the types of supported queries and responses. Furthermore, we investigate the current paradigms by which systems are evaluated and highlight their key limitations. Key findings include identifying the main use cases, objectives, and audiences targeted by dialogue-based XAI methods, and summarize the main types of questions and information needs. Beyond discussing avenues for future work, we present a meta-architecture for these systems from existing literature and outlined prevalent theoretical frameworks.

Universal Knowledge Graph Embeddings - KOUAGOU, N'Dah Jean; Demir, Caglar; Zahera, Hamada Mohamed Abdelsamee; Wilke, Adrian; Heindorf, Stefan; Li, Jiayi; Ngonga Ngomo, Axel-Cyrille (Companion Proceedings of the ACM on Web Conference 2024)

IngridKG: A FAIR Knowledge Graph of Graffiti - Sherif, Mohamed Ahmed; da Silva, Ana Alexandra Morim; Pestryakova, Svetlana; Ahmed, Abdullah Fathi; Niemann, Sven; Ngomo, Axel-Cyrille Ngonga (Scientific Data)
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 Ingrid KG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update Ingrid KG 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 Ingrid KG 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.
Keywords: Library and Information Sciences; Statistics; Probability and Uncertainty; Computer Science Applications; Education; Information Systems; Statistics and Probability

Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics

Neuro-Symbolic Class Expression Learning - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (International Joint Conference on Artificial Intelligence)

IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran - Gusmita, Ria Hari; Firmansyah, Asep Fajar; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Natural Language Processing and Information Systems)
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.

Mit dem Leben Schritt halten - Eine Analyse des Wearable-Dispositivs - Schloots, Franziska Margarete
„Lerne deinen Körper besser kennen“, „Das Beste für deine Gesundheit“ und „Ihre Transformation beginnt jetzt“ - mit Versprechen wie diesen vermitteln die Produkttexte von Wearables wie Fitnesstracker und Smartwatches ein ganz bestimmtes Bild ihrer vorgesehenen Nutzer*innen und deren Nutzung. Verbunden mit den kleinen, am Handgelenk getragenen Geräten sind Fragen nach Erkenntnisgewinn und Kontrollverlust, Selbstoptimierung und Quantifizierungslogiken, Eigenverantwortung und Fremdsteuerung. Die vorliegende Arbeit widmet sich diesem komplexen Spannungsfeld und verfolgt dabei einen multiperspektivischen Ansatz: im Rahmen einer Dispositivanalyse werden die einzelnen Elemente des Wearable-Dispositivs als eigenständige, empirisch zu untersuchende Analysegegenstände betrachtet, um so das Zusammenwirken und die komplexe Beziehung von Diskursen, Gegenständen, Nutzung, Subjekten und Gesellschaft zu erforschen. Ein besonderes Erkenntnisinteresse liegt dabei auf dem Wissen, was sich über Wearables etabliert hat und sich in den Alltagspraktiken der Nutzer*innen widerspiegelt sowie bei der Frage nach den möglichen Funktionen und Auswirkungen des Wearable-Dispositivs.
Keywords: Selbstvermessung; Dispositivanalyse; Gesundheitsdiskurs; Quantifizierungsgesellschaft; Wearables; Selbstoptimierung

Class Expression Learning with Multiple Representations - Ngonga Ngomo, Axel-Cyrille; Demir, Caglar; Kouagou, N'Dah Jean; Heindorf, Stefan; Karalis, Nikoloas; Bigerl, Alexander (Compendium of Neurosymbolic Artificial Intelligence)

LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals - Demir, Caglar; Wiebesiek, Michel; Lu, Renzhong; Ngonga Ngomo, Axel-Cyrille; Heindorf, Stefan (ECML PKDD)

Neural Class Expression Synthesis in ALCHIQ(D) - Kouagou, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (Machine Learning and Knowledge Discovery in Databases: Research Track)
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.

Explaining ELH Concept Descriptions through Counterfactual Reasoning - Sieger, Leonie Nora; Heindorf, Stefan; Blübaum, Lukas; Ngonga Ngomo, Axel-Cyrille (arXiv:2301.05109)
Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.

MultPAX: Keyphrase Extraction Using Language Models and Knowledge Graphs - Zahera, Hamada M.; Vollmers, Daniel; Sherif, Mohamed Ahmed; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings)

Can Machine Translation be a Reasonable Alternative for Multilingual Question Answering Systems over Knowledge Graphs? - Perevalov, Aleksandr; Both, Andreas; Diefenbach, Dennis; Ngonga Ngomo, Axel-Cyrille (WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022)

Proceedings of the QuWeDa 2022: 6th Workshop on Storing, Querying and Benchmarking Knowledge Graphs co-located with 21st International Semantic Web Conference (ISWC 2022), Hangzhou, China, 23-27 October 2022

Hashing the Hypertrie: Space- and Time-Efficient Indexing for SPARQL in Tensors - Bigerl, Alexander; Conrads, Lixi; Behning, Charlotte; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings)

REBench: Microbenchmarking Framework for Relation Extraction Systems - Ali, Manzoor; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings)

Rapid Explainability for Skill Description Learning - Demir, Caglar; Himmelhuber, Anna; Liu, Yushan; Bigerl, Alexander; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Proceedings of the ISWC 2022 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 21st International Semantic Web Conference {(ISWC} 2022), Virtual Conference, Hangzhou, China, October 23-27, 2022)

LSQ Framework: The LSQ Framework for SPARQL Query Log Processing - Stadler, Claus; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille (Proceedings of the QuWeDa 2022: 6th Workshop on Storing, Querying and Benchmarking Knowledge Graphs co-located with 21st International Semantic Web Conference {(ISWC} 2022), Hangzhou, China, 23-27 October 2022)

Kronecker Decomposition for Knowledge Graph Embeddings - Demir, Caglar; Lienen, Julian; Ngonga Ngomo, Axel-Cyrille (HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona, Spain, 28 June 2022- 1 July 2022)

Enhancing the Accessibility of Knowledge Graph Question Answering Systems through Multilingualization - Perevalov, Aleksandr; Ngonga Ngomo, Axel-Cyrille; Both, Andreas (16th IEEE International Conference on Semantic Computing, ICSC 2022, Laguna Hills, CA, USA, January 26-28, 2022)

Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings - Zahera, Hamada M.; Heindorf, Stefan; Balke, Stefan; Haupt, Jonas; Voigt, Martin; Walter, Carolin; Witter, Fabian; Ngonga Ngomo, Axel-Cyrille (The Semantic Web: ESWC 2022 Satellite Events - Hersonissos, Crete, Greece, May 29 - June 2, 2022, Proceedings)

CausalQA: A Benchmark for Causal Question Answering - Bondarenko, Alexander; Wolska, Magdalena; Heindorf, Stefan; Blübaum, Lukas; Ngonga Ngomo, Axel-Cyrille; Stein, Benno; Braslavski, Pavel; Hagen, Matthias; Potthast, Martin (Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022)

Knowledge Graphs - Hogan, Aidan; Blomqvist, Eva; Cochez, Michael; d’Amato, Claudia; de Melo, Gerard; Gutierrez, Claudio; Kirrane, Sabrina; Gayo, José Emilio Labra; Navigli, Roberto; Neumaier, Sebastian; Ngonga Ngomo, Axel-Cyrille; Polleres, Axel; et al. (ACM Comput. Surv.)

A survey of RDF stores & SPARQL engines for querying knowledge graphs - Ali, Waqas; Saleem, Muhammad; Yao, Bin; Hogan, Aidan; Ngonga Ngomo, Axel-Cyrille (VLDB J.)

Hardware-agnostic computation for large-scale knowledge graph embeddings - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (Softw. Impacts)

Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings - Zahera, Hamada Mohamed Abdelsamee; Heindorf, Stefan; Balke, Stefan; Haupt, Jonas; Voigt, Martin; Walter, Carolin; Witter, Fabian; Ngonga Ngomo, Axel-Cyrille (The Semantic Web: ESWC 2022 Satellite Events)

CausalQA: A Benchmark for Causal Question Answering - Bondarenko, Alexander; Wolska, Magdalena; Heindorf, Stefan; Blübaum, Lukas; Ngonga Ngomo, Axel-Cyrille; Stein, Benno; Braslavski, Pavel; Hagen, Matthias; Potthast, Martin (Proceedings of the 29th International Conference on Computational Linguistics)
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.

AI-Based Assistance System for Manufacturing - Deppe, Sahar; Brandt, Lukas; Brünninghaus, Marc; Papenkordt, Jörg; Heindorf, Stefan; Tschirner-Vinke, Gudrun
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.
Keywords: Assistance system; Knowledge graph; Information retrieval; Neural networks; AR

Learning Concept Lengths Accelerates Concept Learning in ALC - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web)

User Involvement in Training Smart Home Agents - Sieger, Leonie Nora; Hermann, Julia; Schomäcker, Astrid; Heindorf, Stefan; Meske, Christian; Hey, Celine-Chiara; Doğangün, Ayşegül (International Conference on Human-Agent Interaction)
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.
Keywords: human-agent interaction; smart homes; supervised learning; participation

EvoLearner: Learning Description Logics with Evolutionary Algorithms - Heindorf, Stefan; Blübaum, Lukas; Düsterhus, Nick; Werner, Till; Golani, Varun Nandkumar; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (WWW)
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.

CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications - Pestryakova, Svetlana; Vollmers, Daniel; Sherif, Mohamed; Heindorf, Stefan; Saleem, Muhammad; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Scientific Data)

Multilingual Verbalization and Summarization for Explainable Link Discovery - Fathi Ahmed, Abdullah; Ahmed Sherif, Mohamed; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Data Knowl. Eng.)

Applying Grammar-Based Compression to RDF - Röder, Michael; Frerk, Philip; Conrads, Felix; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings)

Unsupervised Relation Extraction Using Sentence Encoding - Ali, Manzoor; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille (The Semantic Web: ESWC 2021 Satellite Events - Virtual Event, June 6-10, 2021, Revised Selected Papers)

Unsupervised Relation Extraction Using Sentence Encoding - Ali, Manzoor; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille (The Semantic Web: ESWC 2021 Satellite Events - Virtual Event, June 6-10, 2021, Revised Selected Papers)

InferNER: an attentive model leveraging the sentence-level information for Named Entity Recognition in Microblogs - Shahzad, Moemmur; Amin, Ayesha; Esteves, Diego; Ngonga Ngomo, Axel-Cyrille (Proceedings of the Thirty-Fourth International Florida Artificial Intelligence Research Society Conference, North Miami Beach, Florida, USA, May 17-19, 2021)

Lemming - Example-based Mimicking of Knowledge Graphs - Röder, Michael; Thuy Sy Nguyen, Pham; Conrads, Felix; Alexandra Morim da Silva, Ana; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

SYNTHG: Mimicking RDF Graphs Using Tensor Factorization - Amer Desouki, Abdelmoneim; Conrads, Felix; Röder, Michael; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

A shallow neural model for relation prediction - Demir, Caglar; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

Template-based Question Answering using Recursive Neural Networks - G. Athreya, Ram; Kona Bansal, Srividya; Ngonga Ngomo, Axel-Cyrille; Usbeck, Ricardo (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

LAUREN - Knowledge Graph Summarization for Question Answering - Jalota, Rricha; Vollmers, Daniel; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

Relicensing Combined Datasets - Wilke, Adrian; Bannoura, Arwa; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

ORCA - a Benchmark for Data Web Crawlers - Röder, Michael; de Souza, Geraldo; Kuchelev, Denis; Amer Desouki, Abdelmoneim; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

Using Compositional Embeddings for Fact Checking - Alexandra Morim da Silva, Ana; Röder, Michael; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - ISWC 2021 - 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24-28, 2021, Proceedings)

Convolutional Complex Knowledge Graph Embeddings - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings)

Twitter Network Mimicking for Data Storage Benchmarking - Speck, René; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, January 27-29, 2021)

A shallow neural model for relation prediction - Demir, Caglar; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (CoRR)

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs - Ali, Waqas; Saleem, Muhammad; Yao, Bin; Hogan, Aidan; Ngonga Ngomo, Axel-Cyrille (CoRR)