Evgeny Kharlamov

• Zhou, Dongzhuoran; Yang, Hui; Xiong, Bo; Ma, Yue & Kharlamov, Evgeny (2025). Alleviating over-smoothing via aggregation over compact manifolds (extended version). International Journal of Data Science and Analytics (JDSA). ISSN 2364-415X. 20(8), p. 7055–7069. doi: 10.1007/s41060-025-00875-z. Full text in Research Archive Show summary

  Graph neural networks (GNNs) have achieved significant success in various applications. Most GNNs learn the node features with information aggregation of its neighbors and feature transformation in each layer. However, the node features become indistinguishable after many layers, leading to performance deterioration: a significant limitation known as over-smoothing. Past work adopted various techniques for addressing this issue, such as normalization and skip connection of layer-wise output. After the study, we found that the information aggregations in existing work are all contracted aggregations, with the intrinsic property that features will inevitably converge to the same single point after many layers. To this end, we propose the aggregation over compacted manifold (ACM) method that replaces the existing information aggregation with aggregation over compact manifolds, a special type of manifold, which avoids contracted aggregations. In this work, we theoretically analyze contracted aggregation and its properties. We also provide an extensive empirical evaluation that shows ACM can effectively alleviate over-smoothing and outperforms the state of the art. The code can be found in https://github.com/DongzhuoranZhou/ACM.git.

• Zhu, Yuqicheng; Hernández, Daniel; He, Yuan; Ding, Zifeng; Xiong, Bo & Kharlamov, Evgeny [Show all 7 contributors for this article] (2025). Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings. Association for Computational Linguistics (ACL). Annual Meeting Conference Proceedings. ISSN 0736-587X. 2025, p. 4145–4167. doi: 10.18653/v1/2025.findings-acl.215. Full text in Research Archive Show summary

  Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty estimates by generating a set of answers that is guaranteed to include the true answer with a predefined confidence level. However, existing methods provide probabilistic guarantees averaged over a reference set of queries and answers (marginal coverage guarantee). In high-stakes applications such as medical diagnosis, a stronger guarantee is often required: the predicted sets must provide consistent coverage per query (conditional coverage guarantee). We propose CondKGCP, a novel method that approximates predicate-conditional coverage guarantees while maintaining compact prediction sets. CondKGCP merges predicates with similar vector representations and augments calibration with rank information. We prove the theoretical guarantees and demonstrate empirical effectiveness of CondKGCP by comprehensive evaluations.

• Antonis, Klironomos,; Zhou, Baifan; Zhipeng, Tan,; Zheng, Zhuoxun; H., Gad-Elrab, Mohamed & Heiko, Paulheim, [Show all 7 contributors for this article] (2025). ExeKGLib: A Platform for Machine Learning Analytics Based on Knowledge Graphs. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 16141, p. 57–76. doi: 10.1007/978-3-032-09530-5_4. Full text in Research Archive Show summary

  Nowadays machine learning (ML) practitioners have access to numerous ML libraries available online. Such libraries can be used to create ML pipelines that consist of a series of steps where each step may invoke up to several ML libraries that are used for various data-driven analytical tasks. Development of high-quality ML pipelines is non-trivial; it requires training, ML expertise, and careful development of each step. At the same time, domain experts in science and engineering may not possess such ML expertise and training while they are in pressing need of ML-based analytics. In this paper, we present our ExeKGLib, a Python library enhanced with a graphical interface layer that allows users with minimal ML knowledge to build ML pipelines. This is achieved by relying on knowledge graphs that encode ML knowledge in simple terms accessible to non-ML experts. ExeKGLib also allows improving the transparency and reusability of the built ML workflows and ensures that they are executable. We show the usability and usefulness of ExeKGLib by presenting real use cases.

• Antonis, Klironomos,; Zhou, Baifan; Zheng, Zhuoxun; Gad-Elrab, Mohamed,; Heiko, Paulheim, & Kharlamov, Evgeny (2025). ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs Using Numeric Literals. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 15718, p. 41–58. doi: 10.1007/978-3-031-94575-5_3. Full text in Research Archive Show summary

  Most knowledge graph embedding (KGE) methods tailored for link prediction focus on the entities and relations in the graph, giving little attention to other literal values, which might encode important information. Therefore, some literal-aware KGE models attempt to either integrate numerical values into the embeddings of the entities or convert these numerics into entities during preprocessing, leading to information loss. Other methods concerned with creating relation-specific numerical features assume completeness of numerical data, which does not apply to real-world graphs. In this work, we propose ReaLitE, a novel relation-centric KGE model that dynamically aggregates and merges entities’ numerical attributes with the embeddings of the connecting relations. ReaLitE is designed to complement existing conventional KGE methods while supporting multiple variations for numerical aggregations, including a learnable method. We comprehensively evaluated the proposed relation-centric embedding using several benchmarks for link prediction and node classification tasks. The results showed the superiority of ReaLitE (Pronounced as “reality”, code: https://github.com/boschresearch/ReaLitE) over the state of the art in both tasks.

• Zhou, Dongzhuoran; Kharlamov, Evgeny & Kostylev, Egor V. (2025). GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs. International Conference on Learning Representations. doi: https:/proceedings.iclr.cc/paper_files/paper/2025/file/e779d5a98058b5c9f3df9059a3137647-Paper-Conference.pdf. Full text in Research Archive Show summary

  Learning on graphs is one of the most active research topics in machine learning (ML). Among the key challenges in this field, effectively learning long-range dependencies in graphs has been particularly difficult. It has been observed that, in practice, the performance of many ML approaches, including various types of graph neural networks (GNNs), degrades significantly when the learning task involves long-range dependencies—that is, when the answer is determined by the presence of a certain path of significant length in the graph. This issue has been attributed to several phenomena, including over-smoothing, over-squashing, and vanishing gradient. A number of solutions have been proposed to mitigate these causes. However, evaluation of these solutions is currently challenging because existing benchmarks do not effectively test systems for their ability to learn tasks based on long-range dependencies in a transparent manner. In this paper, we introduce GLoRa, a synthetic benchmark that allows testing of systems for this ability in a systematic way. We then evaluate state-of-the-art systems using GLoRa and conclude that none of them can confidently claim to learn long-range dependencies well. We also observe that this weak performance cannot be attributed to any of the three causes, highlighting the need for further investigation.

• Wahid, Abdul; Yahya, Muhammad; Zaman, Farooq; Zhou, Baifan; Breslin, John G. & Intizar, Muhammad Ali [Show all 7 contributors for this article] (2024). Integrating I4.0 Knowledge Graphs with Large Language Models Beyond SPARQL Endpoints. CEUR Workshop Proceedings. 3830. doi: https:/ceur-ws.org/Vol-3830/paper3sim.pdf. Full text in Research Archive Show summary

  Industry 4.0 (I4.0) knowledge graphs are a common way to represent industrial information models. Conventional SPARQL querying systems require the users to be familiar with the data schema and SPARQL syntax. However, this is often very difficult for many users in industrial production, who have mostly an engineering background, instead of a semantic web. Recent developments in large language models (LLMs) make it possible for non-semantic experts to use natural language to query knowledge graphs (KG). In this work, we present a framework and preliminary results of integrating Industry 4.0 KGs with LLMs to improve how data is represented, reasoned, and processed in manufacturing contexts, facilitating user interaction with KGs and contributing to operational efficiency. Our technique enhances Language Models (LLMs) by utilising the semantic complexity and interdependence of Knowledge Graphs (KGs). This allows us to incorporate domain-specific knowledge. We used the FAISS library and LLaMA2 to optimise the storage and retrieval of vectors, which improved the system’s performance and scalability. This integration allows for advanced fault detection, proactive maintenance, and process optimisation, resulting in decreased periods of inactivity and improved productivity. We introduce the framework’s architecture, implementation strategy, and possible advantages while also discussing the difficulties associated with data integration and scalability. The results of our study show that the integration of KG-LLM surpasses traditional approaches in terms of operational efficiency, as evidenced by enhanced fault detection, proactive maintenance, and process optimisation, thereby opening up possibilities for the advancement of more intelligent and resilient production systems.

• Zheng, Zhuoxun; Zhou, Baifan; Yang, Hui; Tan, Zhipeng; Waaler, Arild Torolv Søetorp & Kharlamov, Evgeny [Show all 7 contributors for this article] (2024). Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. Full text in Research Archive
• Chen, Qiaosheng; Luo, Weiqing; Huang, Zixian; Lin, Tengteng; Wang, Xiaxia & Soylu, Ahmet [Show all 10 contributors for this article] (2024). ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries. In Yang, Grace Hui; Wang, Hongning; Han, Sam; Hauff, Claudia; Zuccon, Guido & Zhang, Yi (Ed.), SIGIR '24 : Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery (ACM). ISSN 9798400704314. p. 303–312. Full text in Research Archive
• Giannakopoulos, Foivos Psarommatis; Zhou, Baifan & Kharlamov, Evgeny (2024). Implementation of Zero Defect Manufacturing using quality prediction: a spot welding case study from Bosch. Procedia Computer Science. 232, p. 1299–1308. doi: 10.1016/j.procs.2024.01.128. Full text in Research Archive Show summary

  Product quality is a vital aspect in the operation of manufacturing systems, manufacturers need to implement at least one quality assurance method to assure the desired quality. The most recent approach for quality assurance is named Zero Defect Manufacturing (ZDM). The scope of the current paper is the implementation of ZDM approach in the automotive industry and specifically for the spot-welding process. Using a machine learning method that is utilizing linear regression and LSTM and consuming data from the production such as sensors and other engineering data to predict the quality of future spot welds. Using this quality prediction there is a root cause analysis to identify why the spot weld will fail and the appropriate prevention actions are proposed. The next step is the training and validation of the machine learning model and the calculation of the accuracy of the model. Once the accuracy of the model is validated a series of simulations, using a dynamic scheduling tool, are performed in order to calculate a series of KPIs to evaluate the impact of the proposed method to the production.

• Zheng, Zhuoxun; Zhou, Baifan; Yang, Hui; Tan, Zhipeng; Sun, Zequn & Li, Chunnong [Show all 9 contributors for this article] (2024). Knowledge graph embedding closed under composition. Data mining and knowledge discovery. ISSN 1384-5810. 38. doi: 10.1007/s10618-024-01050-x. Full text in Research Archive Show summary

  Knowledge Graph Embedding (KGE) has attracted increasing attention. Relation patterns, such as symmetry and inversion, have received considerable focus. Among them, composition patterns are particularly important, as they involve nearly all relations in KGs. However, prior KGE approaches often consider relations to be compositional only if they are well-represented in the training data. Consequently, it can lead to performance degradation, especially for under-represented composition patterns. To this end, we propose HolmE, a general form of KGE with its relation embedding space closed under composition, namely that the composition of any two given relation embeddings remains within the embedding space. This property ensures that every relation embedding can compose, or be composed by other relation embeddings. It enhances HolmE’s capability to model under-represented (also called long-tail) composition patterns with limited learning instances. To our best knowledge, our work is pioneering in discussing KGE with this property of being closed under composition. We provide detailed theoretical proof and extensive experiments to demonstrate the notable advantages of HolmE in modelling composition patterns, particularly for long-tail patterns. Our results also highlight HolmE’s effectiveness in extrapolating to unseen relations through composition and its state-of-the-art performance on benchmark datasets.

• Wang, Xiaxia; Cheng, Gong; Pan, Jeff Z.; Kharlamov, Evgeny & Qu, Yuzhong (2023). BANDAR: Benchmarking Snippet Generation Algorithms for (RDF) Dataset Search. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(2), p. 1227–1241. doi: 10.1109/TKDE.2021.3095309. Full text in Research Archive
• Klironomos, Antonis; Zhou, Baifan; Zhipeng, Tan; Zhuoxun, Zheng; Mohamed, H. Gad-Elrab & Paulheim, Heiko [Show all 7 contributors for this article] (2023). ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13998. Full text in Research Archive
• Rincon-Yanez, Diego; Gad-Elrab, Mohamed H.; Stepanova, Daria; Tran, Kien Trung; Xuan, Cuong Chu & Zhou, Baifan [Show all 7 contributors for this article] (2023). Addressing the Scalability Bottleneck of Semantic Technologies at Bosch. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13998. Full text in Research Archive
• Zhang, Fanjin; Liu, Xiao; Tang, Jie; Dong, Yuxiao; Yao, Peiran & Zhang, Jie [Show all 12 contributors for this article] (2023). OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(9), p. 9225–9239. doi: 10.1109/TKDE.2022.3222168. Full text in Research Archive
• Tan, Zhipeng; Zhou, Baifan; Zheng, Zhuoxun; Savkovic, Ognjen; Huang, Ziqiang & Gonzalez, Irlan-Grangel [Show all 8 contributors for this article] (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14266, p. 453–471. doi: 10.1007/978-3-031-47243-5_25. Full text in Research Archive Show summary

  Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.

• Zhou, Baifan; Nikolov, Nikolay Vladimirov; Zheng, Zhuoxun; Luo, Xianghui; Savkovic, Ognjen & Roman, Dumitru [Show all 8 contributors for this article] (2023). Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14266, p. 380–399. doi: 10.1007/978-3-031-47243-5_21. Full text in Research Archive Show summary

  Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.

• Tan, Zhipeng; Zheng, Zhuoxun; Klironomos, Antonis; Gad-Elrab, Mohamed H.; Xiao, Guohui & Soylu, Ahmet [Show all 8 contributors for this article] (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring. CEUR Workshop Proceedings. 3632. Full text in Research Archive Show summary

  Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem, that is quality monitoring for welding in manufacturing industry. It is an important process accounting for production of millions of cars annually. The work is in line with our research of data-driven solutions that intends to replace the traditional costly quality monitoring. The paper tackles two challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods with literals on real industry data, with consideration of literals. This paper accompanies the full paper in in-use track and provides additional discussion on problem formulation, literal handling strategies, and included information in industrial KG construction.

• Zheng, Zhuoxun; Savkovic, Ognjen; Nikolov, Nikolay Vladimirov; Luu, Huu Phuc; Soylu, Ahmet & Kharlamov, Evgeny [Show all 7 contributors for this article] (2023). Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration. CEUR Workshop Proceedings. 3632. doi: https:/ceur-ws.org/Vol-3632/ISWC2023_paper_480.pdf. Full text in Research Archive Show summary

  Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. To this end, we propose SemCloud, a semantics-enhanced cloud system, for tackling the challenges of data volume and more users. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results. This poster paper accompanies our full paper and focuses on Datalog rules with external machine learning functions for automated resource configuration, and provides additional discussion on formalism and implementation techniques.

• Zheng, Zhuoxun; Zhou, Baifan; Tan, Zhipeng; Savkovic, Ognjen; Rincon-Yanez, Diego & Nikolov, Nikolay Vladimirov [Show all 9 contributors for this article] (2023). Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch. CEUR Workshop Proceedings. 3632. doi: https:/ceur-ws.org/Vol-3632/. Full text in Research Archive Show summary

  Background and Challenges. Industry 4.0 focuses on smart factories that rely on IoT tech- nology for automation. This produces massive amounts of production data, increasing the demand for data-driven solutions and cloud technology. Yet, users of these solutions and cloud technology are often not cloud experts, such as domain experts and data scientists (Fig. 1.1). In a standard setting of a data science project, the team requires extensive assistance from cloud experts, whenever they want to deploy solutions or make small changes to their solutions deployed on the cloud. To facilitate the adoption of cloud systems careful planing to balance cost and benefits is required. Scaling data science solutions presents challenges of handling high data volume and enabling a broader users which are non-cloud experts to use cloud systems.

• Chen, Bo; Zhang, Jing; Zhang, Xiaokang; Dong, Yuxiao; Song, Jian & Zhang, Peng [Show all 9 contributors for this article] (2023). GCCAD: Graph Contrastive Coding for Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 35(8), p. 8037–8051. doi: 10.1109/TKDE.2022.3200459. Full text in Research Archive
• Zhou, Baifan; Pychynski, Tim; Reischl, Markus; Kharlamov, Evgeny & Mikut, Ralf (2022). Machine Learning with Domain Knowledge for Predictive Quality Monitoring in Resistance Spot Welding. Journal of Intelligent Manufacturing. ISSN 0956-5515. 33, p. 1139–1163. doi: 10.1007/s10845-021-01892-y. Full text in Research Archive Show summary

  Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance SpotWelding (RSW). Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Savkovic, Ognjen & Kostylev, Egor [Show all 7 contributors for this article] (2022). ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction. In Hasan, Mohammad Al & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 9781450392365. p. 5074–5078. doi: 10.1145/3511808.3557214. Full text in Research Archive Show summary

  Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently user-friendly. The main challenge arises from the trade-off: the domain KG schema should be knowledge-oriented, to reflect the general domain knowledge; while a KG schema should be dataoriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.

• Zheng, Zhuoxun; Zhou, Baifan; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards a Visualisation Ontology for Data Analysis in Industrial Applications. CEUR Workshop Proceedings. 3355. Full text in Research Archive Show summary

  Machine learning (ML) approaches have proven their great potential in dealing with heterogeneous and voluminous data and thus are widespread in industry. To facilitate the presentation of the ML results and the subsequent discussion on that, visualisation is essential, as it effectively conveys the information behind the data. However, a standardisation of the knowledge and practice about visualisation is still lacking in the industry, which sometimes leads to misunderstandings in conveying information and thus making the discussions on the ML results error-prone. A visualisation ontology which models the nature and pipeline of visualization tasks are well suited to provide such standardisation. Currently there are a few studies that discuss partially the modelling of visualisation, however they are less adequate in depicting the practical procedure of visualisation tasks, which is highly demanded in the industrial applications. To this end, we present our ongoing work of development of the visualisation ontology in industrial applications at Bosch. We also discuss applications and evaluation of our ontology.

• Lin, Tengteng; Chen, Qiaosheng; Cheng, Gong; Soylu, Ahmet; Ell, Basil & Zhao, Ruoqi [Show all 10 contributors for this article] (2022). ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval. In Amigó, Enrique; Castells, Pablo; Gonzalo, Julio; Carterette, Ben; Culpepper, J. Shane & Kazai, Gabriella (Ed.), SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. Association for Computing Machinery (ACM). ISSN 9781450387323. p. 2981–2991. doi: 10.1145/3477495.3531729. Full text in Research Archive Show summary

  Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics. In Hasan, Mohammad Al & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 9781450392365. p. 5064–5068. doi: 10.1145/3511808.3557195. Full text in Research Archive Show summary

  Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Soylu, Ahmet [Show all 7 contributors for this article] (2022). Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384(13384), p. 123–128. doi: 10.1007/978-3-031-11609-4_23. Full text in Research Archive Show summary

  Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.

• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Kostylev, Egor; Cheng, Gong & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2022). Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384(13384), p. 299–302. doi: 10.1007/978-3-031-11609-4_45. Full text in Research Archive Show summary

  In the context of Industry 4.0 and Internet of Things (IoT), modern manufacturing and production lines are equipped with software systems and sensors that constantly collect and send a high volume of data.

• Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Trung-Kien, Tran [Show all 11 contributors for this article] (2022). The Data Value Quest: A Holistic Semantic Approach at Bosch. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 287–290. doi: 10.1007/978-3-031-11609-4_42. Full text in Research Archive Show summary

  Modern industry witnesses a fast growth in volume and complexity of heterogeneous manufacturing (big) data [1, 2] thanks to the technological advances of Industry 4.0 [1, 3], including development in perception, communication, processing, and actuation.

• Zhou, Baifan; Svetashova, Yulia; Gusmao, Andre Silva; Soylu, Ahmet; Cheng, Gong & Mikut, Ralf [Show all 8 contributors for this article] (2021). SemML: Facilitating development of ML models for condition monitoring with semantics. Journal of Web Semantics. ISSN 1570-8268. 71, p. 1–21. doi: 10.1016/j.websem.2021.100664. Full text in Research Archive Show summary

  Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modelling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. In particular, SemML has several novel components and relies on ontologies and ontology templates for ML task negotiation and for data and ML feature annotation. SemML also allows to instantiate parametrised ML pipelines by semantic annotation of industrial data. With SemML, users do not need to dive into data and ML scripts when new datasets of a studied application scenario arrive. They only need to annotate data and then ML models will be constructed through the combination of semantic reasoning and ML modules. We demonstrate the benefits of SemML on a Bosch use-case of electric resistance welding with very promising results.

• Chen, Huajun; Deng, Shumin; Zhang, Wen; Xu, Zezhong; Li, Juan & Kharlamov, Evgeny (2021). Neural symbolic reasoning with knowledge graphs: Knowledge extraction, relational reasoning, and inconsistency checking. Fundamental Research. ISSN 2096-9457. 1(5), p. 565–573. doi: 10.1016/j.fmre.2021.08.013. Full text in Research Archive
• Roman, Dumitru; Nikolov, Nikolay; Elvesæter, Brian; Soylu, Ahmet; Prodan, Radu & Kimovski, Dragi [Show all 17 contributors for this article] (2021). DataCloud: Enabling the Big Data Pipelines on the Computing Continuum. In Cherfi, Samira; Perini, Anna & Nurcan, Selmin (Ed.), Research Challenges in Information Science: 15th International Conference, RCIS 2021, Limassol, Cyprus, May 11–14, 2021, Proceedings. Springer. ISSN 9783030750176. p. 715–717. doi: 10.1007/978-3-030-75018-3. Full text in Research Archive Show summary

  With the recent developments of Internet of Things (IoT) and cloud-based technologies, massive amounts of data are generated by heterogeneous sources and stored through dedicated cloud solutions. Often organizations generate much more data than they are able to interpret, and current Cloud Computing technologies cannot fully meet the requirements of the Big Data processing applications and their data transfer overheads. Many data are stored for compliance purposes only but not used and turned into value, thus becoming Dark Data, which are not only an untapped value, but also pose a risk for organizations.

• Forssell, Henrik; Kharlamov, Evgeny & Thorstensen, Evgenij (2020). On equivalence and cores for incomplete databases in open and closed worlds. Leibniz International Proceedings in Informatics. 155, p. 1–21. doi: 10.4230/LIPIcs.ICDT.2020.10. Full text in Research Archive Show summary

  Data exchange heavily relies on the notion of incomplete database instances. Several semantics for such instances have been proposed and include open (OWA), closed (CWA), and open-closed (OCWA) world. For all these semantics important questions are: whether one incomplete instance semantically implies another; when two are semantically equivalent; and whether a smaller or smallest semantically equivalent instance exists. For OWA and CWA these questions are fully answered. For several variants of OCWA, however, they remain open. In this work we adress these questions for Closed Powerset semantics and the OCWA semantics of [24]. We define a new OCWA semantics, called OCWA*, in terms of homomorphic covers that subsumes both semantics, and characterize semantic implication and equivalence in terms of such covers. This characterization yields a guess-and-check algorithm to decide equivalence, and shows that the problem is NP-complete. For the minimization problem we show that for several common notions of minimality there is in general no unique minimal equivalent instance for Closed Powerset semantics, and consequently not for the more expressive OCWA* either. However, for Closed Powerset semantics we show that one can find, for any incomplete database, a unique finite set of its subinstances which are subinstances (up to renaming of nulls) of all instances semantically equivalent to the original incomplete one. We study properties of this set, and extend the analysis to OCWA*

• Liu, Qingxia; Chen, Yue; Cheng, Gong; Kharlamov, Evgeny; Li, Junyou & Qu, Yuzhong (2020). Entity Summarization with User Feedback. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. p. 376–392. doi: 10.1007/978-3-030-49461-2_22. Full text in Research Archive
• Shi, Yuxuan; Cheng, Gong & Kharlamov, Evgeny (2020). Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings, WWW '20: Proceedings of The Web Conference 2020. Association for Computing Machinery (ACM). ISSN 9781450370233. p. 235–245. doi: 10.1145/3366423.3380110. Full text in Research Archive
• Zhou, Baifan; Svetashova, Yulia; Byeon, Seongsu; Pychynski, Tim; Mikut, Ralf & Kharlamov, Evgeny (2020). Predicting Quality of Automated Welding with Machine Learning and Semantics: A Bosch Case Study. In d'Aquin, Mathieu & Dietze, Stefan (Ed.), CIKM 20 : The 29th ACM international conference on information and knowledge management : Virtual Event Ireland, October 19-23, 2020.. ACM Publications. ISSN 9781450368599. p. 2933–2940. doi: 10.1145/3340531.3412737. Full text in Research Archive
• Li, Junyou; Cheng, Gong; Liu, Qingxia; Zhang, Wen; Kharlamov, Evgeny & Gunaratna, Kalpa [Show all 7 contributors for this article] (2020). Neural Entity Summarization with Joint Encoding and Weak Supervision. In Bessiere, Christian (Eds.), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020. International Joint Conferences on Artificial Intelligence. ISSN 9780999241165. p. 1644–1650. doi: 10.24963/ijcai.2020/228. Full text in Research Archive
• Kalayci, Elem Guzel; Grangel-González, Irlán; Loesch, Felix; Xiao, Guohui; Mehdi, Anees & Kharlamov, Evgeny [Show all 7 contributors for this article] (2020). Serving Bosch production data as virtual KGs. CEUR Workshop Proceedings. 2721(589), p. 355–358. Full text in Research Archive
• Cheng, Gong; Gunaratna, Kalpa & Kharlamov, Evgeny (2020). Entity Summarization in Knowledge Graphs: Algorithms, Evaluation, and Applications, WWW '20: Proceedings of The Web Conference 2020. Association for Computing Machinery (ACM). ISSN 9781450370233. p. 301–302. doi: 10.1145/3366424.3383108. Full text in Research Archive
• Svetashova, Yulia; Zhou, Baifan; Pychynski, Tim; Schmidt, Stefanie; Sure-Vetter, York & Mikut, Ralf [Show all 7 contributors for this article] (2020). Ontology-Enhanced Machine Learning: A Bosch Use Case of Welding Quality Monitoring. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. p. 531–550. doi: 10.1007/978-3-030-62466-8_33. Full text in Research Archive
• Tran, Trung-Kien; Gad-Elrab, Mohamed H.; Stepanova, Daria; Kharlamov, Evgeny & Stroetgen, Jannik (2020). Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs, WWW '20: Proceedings of The Web Conference 2020. Association for Computing Machinery (ACM). ISSN 9781450370233. p. 2613–2619. doi: 10.1145/3366423.3380014. Full text in Research Archive
• Kalayci, Elem Güzel; Grangel-González,, Irlán; Lösch, Felix; Xiao, Guohui; ul-Mehdi, Anees & Kharlamov, Evgeny [Show all 7 contributors for this article] (2020). Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12507, p. 464–481. doi: 10.1007/978-3-030-62466-8_29. Full text in Research Archive
• Zhou, Baifan; Svetashova, Yulia; Pychynski, Tim & Kharlamov, Evgeny (2020). Semantic ML for manufacturing monitoring at Bosch. CEUR Workshop Proceedings. 2721, p. 397–398. Full text in Research Archive
• Li, Shuxing; Huang, Zixian; Cheng, Gong; Kharlamov, Evgeny & Gunaratna, Kalpa (2020). Enriching Documents with Compact, Representative, Relevant Knowledge Graphs. In Bessiere, Christian (Eds.), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020. International Joint Conferences on Artificial Intelligence. ISSN 9780999241165. doi: 10.24963/ijcai.2020/242. Full text in Research Archive
• Svetashova, Yulia; Zhou, Baifan; Schmid, Stefan; Pychynski, Tim & Kharlamov, Evgeny (2020). SemML: Reusable ML for Condition Monitoring in Discrete Manufacturing. CEUR Workshop Proceedings. 2721, p. 214–218. Full text in Research Archive
• Zhou, Baifan; Svetashova, Yulia; Pychynski, Tim; Baimuratov, Ildar; Soylu, Ahmet & Kharlamov, Evgeny (2020). SemFE: Facilitating ML Pipeline Development with Semantics. In d'Aquin, Mathieu & Dietze, Stefan (Ed.), CIKM 20 : The 29th ACM international conference on information and knowledge management : Virtual Event Ireland, October 19-23, 2020.. ACM Publications. ISSN 9781450368599. p. 3489–3492. doi: 10.1145/3340531.3417436. Full text in Research Archive Show summary

  Machine learning (ML) based data analysis has attracted an increasing attention in the manufacturing industry, however, many challenges hamper their wide spread adoption. The main challenges are the high costs of labour-intensive data preparation from diverse sources and processes, the asymmetrical backgrounds of the experts involved in manufacturing analyses that impede efficient communication between them, and the lack of generalisability of ML models tailored to specific applications. Our semantically enhanced ML pipeline, SemFE, with feature engineering addresses these challenges, serving as a bridge to bring the endeavours of experts together, and making data science accessible to non-ML-experts. SemFE relies on ontologies for discrete manufacturing monitoring that encapsulate domain and ML knowledge; it has five novel semantic modules for automation of ML-pipeline development and user-friendly GUIs. The demo attendees will be able to use our system to build manufacturing monitoring ML pipelines, and to design their own pipelines with minimal prior knowledge of machine learning.

• Mailis, Theofilos; Kharlamov, Evgeny; Kotidis, Yannis; Horrocks, Ian; Nikolopoulos, Vaggelis & Ioannidis, Yannis (2019). An efficient index for RDF query containment. Proceedings of the ACM SIGMOD International Conference on Management of Data. ISSN 0730-8078. p. 1499–1516. doi: 10.1145/3299869.3319864. Full text in Research Archive
• Kharlamov, Evgeny; Mehdi, Gulnar; Savković, Ognjen; Xiao, Guohui; Lamparter, Steffen & Horrocks, Ian [Show all 7 contributors for this article] (2019). Towards Simplification of Analytical Workflows with Semantics at Siemens (Extended Abstract). IEEE International Conference on Big Data (Big Data). ISSN 2639-1589. p. 1951–1954. doi: 10.1109/BigData.2018.8622652. Full text in Research Archive
• Kharlamov, Evgeny; Zheleznyakov, Dmitriy; Nutt, Werner & Calvanese, Diego (2019). Evolution of OWL 2 QL knowledge bases: From inexpressibility to practical approaches. CEUR Workshop Proceedings. 2576. Full text in Research Archive
• Kharlamov, Evgeny; Martin-Recuerda, Francisco; Perry, Brandon; Cameron, David B.; Fjellheim, Roar Arne & Waaler, Arild Torolv Søetorp (2019). Towards Semantically Enhanced Digital Twins. IEEE International Conference on Big Data (Big Data). ISSN 2639-1589. p. 4189–4193. doi: 10.1109/BigData.2018.8622503. Full text in Research Archive
• Savkovic, Ognjen; Kharlamov, Evgeny & Lamparter, Steffen (2019). Validation of SHACL Constraints over KGs with OWL 2 QL Ontologies via Rewriting. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11503 LNCS, p. 314–329. doi: 10.1007/978-3-030-21348-0_21. Full text in Research Archive Show summary

  Constraints have traditionally been used to ensure data quality. Recently, several constraint languages such as SHACL, as well as mechanisms for constraint validation, have been proposed for Knowledge Graphs (KGs). KGs are often enhanced with ontologies that define relevant background knowledge in a formal language such as OWL 2 QL. However, existing systems for constraint validation either ignore these ontologies, or compile ontologies and constraints into rules that should be executed by some rule engine. In the latter case, one has to rely on different systems when validating constrains over KGs and over ontology-enhanced KGs. In this work, we address this problem by defining rewriting techniques that allow to compile an OWL 2 QL ontology and a set of SHACL constraints into another set of SHACL constraints. We show that in the general case the rewriting may not exists, but it always exists for the positive fragment of SHACL. Our rewriting techniques allow to validate constraints over KGs with and without ontologies using the same SHACL validation engines.

• Zheleznyakov, Dmitriy; Kharlamov, Evgeny; Nutt, Werner & Calvanese, Diego (2019). On expansion and contraction of DL-Lite knowledge bases. Journal of Web Semantics. ISSN 1570-8268. 57, p. 1–19. doi: 10.1016/j.websem.2018.12.002. Full text in Research Archive
• Wang, Xiaxia; Chen, Jinchi; Li, Shuxin; Cheng, Gong; Pan, Jeff Z. & Kharlamov, Evgeny [Show all 7 contributors for this article] (2019). A Framework for Evaluating Snippet Generation for Dataset Search. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11778 LNCS, p. 680–697. doi: 10.1007/978-3-030-30793-6_39. Full text in Research Archive
• Wang, Xiaxia; Cheng, Gong & Kharlamov, Evgeny (2019). Towards multi-facet snippets for dataset search. CEUR Workshop Proceedings. 2465. Full text in Research Archive
• Klungre, Vidar Norstein; Soylu, Ahmet; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny & Giese, Martin (2019). Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing. New generation computing. ISSN 0288-3635. 37(4), p. 361–392. doi: 10.1007/s00354-019-00071-1. Full text in Research Archive Show summary

  Ontology-based visual query formulation is a viable alternative to textual query editors in the Semantic Web domain for extracting data from structured data sources in terms of the skills and knowledge required. A visual query system is at any moment responsible for providing the user with query extension suggestions; however, suggestions leading to empty results are often not useful. To this end, in this article, we first present an approach for projecting OWL 2 ontologies into navigation graphs to be used for query formulation and then a solution where an efficient finite index is used to calculate non-ranked approximated extension suggestions for ontology-based visual query systems using navigation graphs. The results of our experiments suggest that one can efficiently project an ontology into a navigation graph, query it for running an interactive user interface, and suggest query extensions that do not lead to dead-ends.

• Kharlamov, Evgeny; Kotidis, Yannis; Mailis, Theofilos; Neuenstadt, Christian; Nikolaou, Charalampos & Özcep, Özgür [Show all 12 contributors for this article] (2019). An ontology-mediated analytics-aware approach to support monitoring and diagnostics of static and streaming data. Journal of Web Semantics. ISSN 1570-8268. 56, p. 30–55. doi: 10.1016/j.websem.2019.01.001. Full text in Research Archive
• Soylu, Ahmet & Kharlamov, Evgeny (2019). Navigating OWL 2 Ontologies Through Graph Projection. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 846, p. 113–119. doi: 10.1007/978-3-030-14401-2_10. Full text in Research Archive Show summary

  Ontologies are powerful, yet often complex, assets for rep- resenting, exchanging, and reasoning over data. Particularly, OWL 2 ontologies have been key for constructing semantic knowledge graphs. Ability to navigate ontologies is essential for supporting various knowledge engineering tasks such as querying and domain exploration. To this end, in this short paper, we describe an approach for projecting the non-hierarchical topology of an OWL 2 ontology into a graph. The approach has been implemented in two tools, one for visual query formulation and one for faceted search, and evaluated under different use cases.

• Mailis, Theofilos; Kotidis, Yannis; Nikolopoulos, Vaggelis; Kharlamov, Evgeny; Horrocks, Ian & Ioannidis, Yannis (2019). MV-Index: An efficient index for graph-query containment. CEUR Workshop Proceedings. 2456(50), p. 193–196. Full text in Research Archive
• Savković, Ognjen; Kharlamov, Evgeny; Ringsquandl, Martin; Xiao, Guohui; Mehdi, Gulnar & Kalayc, Elem Güzel [Show all 8 contributors for this article] (2018). Semantic diagnostics of smart factories. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11341 LNCS, p. 277–294. doi: 10.1007/978-3-030-04284-4_19. Full text in Research Archive
• Kharlamov, Evgeny; Skjæveland, Martin G; Hovland, Dag; Mailis, Theofilos; Jimenez-Ruiz, Ernesto & Xiao, Guohui [Show all 9 contributors for this article] (2018). Finding Data Should be Easier than Finding Oil. In Abe, Naoki; Liu, Huan; Hu, Xiaohua; Ahmed, Nesreen; Qiao, Mu; Song, Yang; Kossmann, Donald; Liu, Bing; Lee, Kisung; Tang, Jiliang; He, Jingrui & Saltz, Jeffrey (Ed.), 2018 IEEE International Conference on Big Data (Big Data), Seattle, 10-13 Dec. 2018. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538650356. doi: 10.1109/BigData.2018.8622035. Full text in Research Archive Show summary

  The competitiveness of modern enterprises heavily depends on their ability to make the right business decisions by relying on efficient and timely analysis of the right business critical data. In large and data intensive companies such as Equinor, a Norwegian multinational oil and gas company with more than 20,000 employees, gathering such data is not a trivial task due to the growing size and complexity of corporate information sources. As a result, the data gathering task is often the most time-consuming part of the decision making process, in particular when it comes to the work processes of Equinor’s Exploration geologists that should find in a timely manner New exploitable accumulations of oil or gas in given areas by analysing data about these areas. In this work we present our experience in addressing this data challenge tast at Equinor. We have developed and deployed at Equinor a semantic data access system that relies on the Ontology Based Data Access (OBDA) approach. Our system is based on our solid theoretical contributions and has been extensively evaluated at Equinor

• Klungre, Vidar Norstein; Soylu, Ahmet; Giese, Martin; Waaler, Arild & Kharlamov, Evgeny (2018). On Enhancing Visual Query Building over KGs Using Query Logs. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11341, p. 77–85. doi: 10.1007/978-3-030-04284-4_6. Full text in Research Archive Show summary

  Knowledge Graphs have recently gained a lot of attention and have been successfully applied in both academia and industry. Since KGs may be very large: they may contain millions of entities and triples relating them to each other, to classes, and assigning them data values, it is important to provide endusers with effective tools to explore information encapsulated in KGs. In this work we present a visual query system that allows users to explore KGs by intuitively constructing tree-shaped conjunctive queries. It is known that systems of this kind suffer from the problem of information overflow: when constructing a query the users have to iteratively choose from a potentially very long list of options, sich as, entities, classes, and data values, where each such choice corresponds to an extension of the query new filters. In order to address this problem we propose an approach to substantially reduce such lists with the help of ranking and by eliminating the so-called deadends, options that yield queries with no answers over a given KG.

• Soylu, Ahmet & Kharlamov, Evgeny (2018). Making Complex Ontologies End User Accessible via Ontology Projections. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11341, p. 295–303. doi: 10.1007/978-3-030-04284-4_20. Full text in Research Archive Show summary

  Ontologies are a powerful mechanism to structure domains of interest. They have successfully been applied in medical domain, industry and other important areas. Despite the simplicity of ontological vocabularies that consist of classes and properties, ontologies can relate elements of the vocabulary with the help of axioms in a very non-trivial way. Thus, the relationship between classes and properties can become hardly accessible by end users thus affecting the practical value of ontologies. Indeed, it is essential for end users to be able to navigate or browse through an ontology, to get a big picture of what classes there are and what they have in common in terms of other related classes and properties. This helps end users in effectively performing various knowledge engineering tasks such as querying and domain exploration. To this end, in this short paper, we describe an approach to project OWL 2 ontologies into graphs and show how to leverage this approach in practical systems for visual query formulation and faceted search that we tested in various scenarios.

• Pinkel, Christoph; Binnig, Carsten; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; Nikolov, Andriy & Schwarte, Andreas [Show all 8 contributors for this article] (2017). IncMap: A Journey towards Ontology-based Data Integration. GI-Edition Lecture Notes in Informatics (LNI). ISSN 1617-5468. p. 145–164. Full text in Research Archive

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• Zheng, Zhuoxun; Ognjen, Savković,; Zhou, Baifan; Antonis, Klironomos,; Kharlamov, Evgeny & Soylu, Ahmet (2025). Graph Constraint Language for Industrial Knowledge Graphs and Machine Learning. doi: 10.1007/978-3-032-02215-8_13. Full text in Research Archive Show summary

  Knowledge graphs (KGs) are seeing widespread application across diverse domains, including querying, search, machine learning (ML), and stream data processing. In all these areas, ensuring data consistency and quality is of critical importance. For instance,...

• Waaler, Arild Torolv Søetorp; Kharlamov, Evgeny; Zhou, Baifan & Zhou, Dongzhuoran (2022). Proceedings of the First International Workshop on Semantic Industrial Information Modelling (SemIIM 2022) co-located with the 19th Extended Semantic Web Conference ESWC 2022, Greece, Crete, 30 May 2022. Full text in Research Archive
• Zhou, Baifan; Tan, Zhipeng; Zheng, Zhuoxun; Zhou, Dongzhuoran; He, Yunjie & Zhu, Yuqicheng [Show all 11 contributors for this article] (2022). Neuro-Symbolic AI at Bosch: Data Foundation, Insights, and Deployment. Full text in Research Archive
• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Tan, Zhipeng; Kostylev, Egor & Kharlamov, Evgeny (2022). Towards Executable Knowledge Graph Translation. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Khan, Akif Quddus; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards A Statistic Ontology for Data Analysis in Smart Manufacturing. Full text in Research Archive
• Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Tan, Zhipeng; Savkovic, Ognjen & Yang, Hui [Show all 8 contributors for this article] (2022). Knowledge Graph-Based Semantic System for Visual Analytics in Automatic Manufacturing. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch. In Hasan, Mohammad Al & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 9781450392365. p. 5102–5103. doi: 10.1145/3511808.3557512. Full text in Research Archive Show summary

  With the development of Industry 4.0 technology, modern industries such as Bosch’s welding monitoring witnessed the rapid widespread of machine learning (ML) based data analytical applications, which in the case of welding monitoring has led to more efficient and accurate welding monitoring quality. However, industrial ML is affected by the low transparency of ML towards non-ML experts needs. The lack of understanding by domain experts of ML methods hampers the application of ML methods in industry and the reuse of developed ML pipelines, as ML methods are often developed in an ad hoc manner for specific problems. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG), which formally encode ML knowledge and solutions in KGs, which serve as common language between ML experts and nonML experts, thus facilitate their communication and increase the transparency of ML methods. We evaluated our system extensively with an industrial use case at Bosch, showing promising results.

• Qu, Yuanwei; Zhou, Baifan; Kharlamov, Evgeny & Giese, Martin (2022). Industrial Geological Information Capture with GeoStructure Ontology. Full text in Research Archive Show summary

  Understanding geological structures plays an essential role in a wide range of industries. Various tools and software have been developed to support the observation and characterisation of the geological structures and they produce a large volume of heterogeneous data. Thanks to the development of semantic technologies, plenty of works have investigated industrial geological data. In contrast, geological sketches, figure interpretations, and illustrations as the initial data collection and analysis steps have received little attention. Most structure information is still reported as PDF files with forms, tables, and raster images with interpretations. We attempt to fill this gap by providing an ontology- based industrial geological information capture method to allow users to input and store structure information formally. Following users’ sketches, the corresponding knowledge graphs will be generated. This work aims to make the first-hand geological structure information findable, accessible, interoperable, reusable (FAIR), and to support qualitative information reasoning, from a user-driven perspective, and to meet the needs of digitised industry.

• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Cheng, Gong & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2022). Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Soylu, Ahmet [Show all 7 contributors for this article] (2022). Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring. Full text in Research Archive
• Yahya, Muhammad; Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Breslin, John G. & Ali, Muhammad Intizar [Show all 7 contributors for this article] (2022). Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction. Full text in Research Archive
• Zhou, Baifan; Tan, Zhipeng; Zheng, Zhuoxun; Zhou, Dongzhuoran; Savkovic, Ognjen & Kharlamov, Evgeny (2022). Towards A Visualisation Ontology for Reusable Visual Analytics. Full text in Research Archive
• Zhou, Dongzhuoran; Zhou, Baifan; Chen, Jieying; Cheng, Gong; Kostylev, Egor & Kharlamov, Evgeny (2021). Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding. Full text in Research Archive Show summary

  Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results.

• Zhou, Baifan; Zhou, Dongzhuoran; Chen, Jieying; Svetashova, Yulia; Cheng, Gong & Kharlamov, Evgeny (2021). Scaling Usability of ML Analytics with Knowledge Graphs: Exemplified with A BoschWelding Case. Full text in Research Archive Show summary

  Automated welding is heavily used in the automotive industry to produce car bodies by connecting metal parts with welding spots. Modern welding solutions and manufacturing environments produce a high volume of heterogeneous data. Analytics of these data with machine learning (ML) can help to ensure high quality of welding operations. However, due to heterogeneity of data and application scenarios, it is challenging to scale usability of such ML analytics is challenging, namely saving time in developing new solutions and reusing already developed solutions.We address this challenge by relying on knowledge graphs (KG) that not only conveniently allow to integrate welding data, but also to serve as a basis for layering ML-based analytical applications, thus enabling quality monitoring of welding operations. In this work we focus on the construction of a KG for welding that is tailored towards further use for ML applications. Furthermore, we demonstrate how selected ML analytical tasks are supported by this KG.

• Kharlamov, Evgeny (2019). Knowledge Graphs and Semantic Technologies for Industry 4.0. Full text in Research Archive Show summary

  Industry 4.0 is a global trend that aims at digitalising and automating production processes from product design to manufacturing and delivery. This requires to integrate and analyse large amounts of heterogeneous data such as experts knowledge, standards and good practices, corporate documentation and databases, engineering knowledge, and sensor data. Semantic technologies can help in this by offering modeling, querying, diagnostic, and analytical techniques and languages. While knowledge graphs can serve as a flexible format for data integration. In this talk we will review some Industry 4.0 challenges, approaches, and exemplify how they can be addressed with knowledge graphs and semantics.

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