Search

Your search keyword '"Soylu, Ahmet"' showing total 648 results
Start Over Author "Soylu, Ahmet"
648 results on '"Soylu, Ahmet"'

3. Scheduling of Distributed Applications on the Computing Continuum: A Survey

Author

Mehran, Narges, Kimovski, Dragi, Hellwagner, Hermann, Roman, Dumitru, Soylu, Ahmet, and Prodan, Radu

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The demand for distributed applications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. These applications predominantly utilize Cloud data centers for high-performance computing and Fog and Edge devices for low-latency communication for small-size machine learning model training and inference. The challenge of executing applications with different requirements on heterogeneous devices requires effective methods for solving NP-hard resource allocation and application scheduling problems. The state-of-the-art techniques primarily investigate conflicting objectives, such as the completion time, energy consumption, and economic cost of application execution on the Cloud, Fog, and Edge computing infrastructure. Therefore, in this work, we review these research works considering their objectives, methods, and evaluation tools. Based on the review, we provide a discussion on the scheduling methods in the Computing Continuum., Comment: 7 pages, 3 figures, 3 tables

Published
2024
Full Text
View/download PDF

4. Comparison of Microservice Call Rate Predictions for Replication in the Cloud

Author

Mehran, Narges, Haghighi, Arman, Aminharati, Pedram, Nikolov, Nikolay, Soylu, Ahmet, Roman, Dumitru, and Prodan, Radu

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Today, many users deploy their microservice-based applications with various interconnections on a cluster of Cloud machines, subject to stochastic changes due to dynamic user requirements. To address this problem, we compare three machine learning (ML) models for predicting the microservice call rates based on the microservice times and aiming at estimating the scalability requirements. We apply the linear regression (LR), multilayer perception (MLP), and gradient boosting regression (GBR) models on the Alibaba microservice traces. The prediction results reveal that the LR model reaches a lower training time than the GBR and MLP models. However, the GBR reduces the mean absolute error and the mean absolute percentage error compared to LR and MLP models. Moreover, the prediction results show that the required number of replicas for each microservice by the gradient boosting model is close to the actual test data without any prediction., Comment: 7 pages, 5 figures, 4 tables

Published
2023

5. Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case

Author

Tan, Zhipeng, Zhou, Baifan, Zheng, Zhuoxun, Savkovic, Ognjen, Huang, Ziqi, Gonzalez, Irlan-Grangel, Soylu, Ahmet, and Kharlamov, Evgeny

Subjects
Computer Science - Artificial Intelligence
Abstract

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., Comment: Paper accepted at ISWC2023 In-Use track

Published
2023

6. Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Author

Zhou, Baifan, Nikolov, Nikolay, Zheng, Zhuoxun, Luo, Xianghui, Savkovic, Ognjen, Roman, Dumitru, Soylu, Ahmet, and Kharlamov, Evgeny

Subjects
Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

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., Comment: Paper accepted at ISWC2023 In-Use track

Published
2023

8. ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER

Author

Layegh, Amirhossein, Payberah, Amir H., Soylu, Ahmet, Roman, Dumitru, and Matskin, Mihhail

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design., Comment: 9 pages, 5 figures, COMPSAC2023

Published
2023
Full Text
View/download PDF

10. Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data

Author

Zheng, Zhuoxun, Zhou, Baifan, Yang, Hui, Tan, Zhipeng, Waaler, Arild, Kharlamov, Evgeny, Soylu, Ahmet, Hartmanis, Juris, Founding Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Meroño Peñuela, Albert, editor, Dimou, Anastasia, editor, Troncy, Raphaël, editor, Hartig, Olaf, editor, Acosta, Maribel, editor, Alam, Mehwish, editor, Paulheim, Heiko, editor, and Lisena, Pasquale, editor

Published
2024
Full Text
View/download PDF

11. A Taxonomy for Cloud Storage Cost

Author

Khan, Akif Quddus, Nikolov, Nikolay, Matskin, Mihhail, Prodan, Radu, Bussler, Christoph, Roman, Dumitru, Soylu, Ahmet, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chbeir, Richard, editor, Benslimane, Djamal, editor, Zervakis, Michalis, editor, Manolopoulos, Yannis, editor, Ngyuen, Ngoc Thanh, editor, and Tekli, Joe, editor

Published
2024
Full Text
View/download PDF

12. Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

Author

Zheng, Zhuoxun, Zhou, Baifan, Zhou, Dongzhuoran, Cheng, Gong, Jiménez-Ruiz, Ernesto, Soylu, Ahmet, and Kharlamo, Evgeny

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence
Abstract

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.

Published
2022
Full Text
View/download PDF

13. Towards Cloud Storage Tier Optimization with Rule-Based Classification

Author

Khan, Akif Quddus, Nikolov, Nikolay, Matskin, Mihhail, Prodan, Radu, Bussler, Christoph, Roman, Dumitru, Soylu, Ahmet, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Papadopoulos, George A., editor, Rademacher, Florian, editor, and Soldani, Jacopo, editor

Published
2023
Full Text
View/download PDF

14. A Reference Data Model to Specify Event Logs for Big Data Pipeline Discovery

Author

Benvenuti, Dario, Marrella, Andrea, Rossi, Jacopo, Nikolov, Nikolay, Roman, Dumitru, Soylu, Ahmet, Perales, Fernando, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Di Francescomarino, Chiara, editor, Burattin, Andrea, editor, Janiesch, Christian, editor, and Sadiq, Shazia, editor

Published
2023
Full Text
View/download PDF

20. Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring

Author

Zheng, Zhuoxun, Zhou, Baifan, Zhou, Dongzhuoran, Zheng, Xianda, Cheng, Gong, Soylu, Ahmet, Kharlamov, Evgeny, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sattler, Ulrike, editor, Hogan, Aidan, editor, Keet, Maria, editor, Presutti, Valentina, editor, Almeida, João Paulo A., editor, Takeda, Hideaki, editor, Monnin, Pierre, editor, Pirrò, Giuseppe, editor, and d’Amato, Claudia, editor

Published
2022
Full Text
View/download PDF

21. Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case

Author

Zhou, Dongzhuoran, Zhou, Baifan, Zheng, Zhuoxun, Soylu, Ahmet, Cheng, Gong, Jimenez-Ruiz, Ernesto, Kostylev, Egor V., Kharlamov, Evgeny, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sattler, Ulrike, editor, Hogan, Aidan, editor, Keet, Maria, editor, Presutti, Valentina, editor, Almeida, João Paulo A., editor, Takeda, Hideaki, editor, Monnin, Pierre, editor, Pirrò, Giuseppe, editor, and d’Amato, Claudia, editor

Published
2022
Full Text
View/download PDF

22. Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case

Author

Zhou, Dongzhuoran, Zhou, Baifan, Zheng, Zhuoxun, Kostylev, Egor V., Cheng, Gong, Jiménez-Ruiz, Ernesto, Soylu, Ahmet, Kharlamov, Evgeny, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Groth, Paul, editor, Rula, Anisa, editor, Schneider, Jodi, editor, Tiddi, Ilaria, editor, Simperl, Elena, editor, Alexopoulos, Panos, editor, Hoekstra, Rinke, editor, Alam, Mehwish, editor, Dimou, Anastasia, editor, and Tamper, Minna, editor

Published
2022
Full Text
View/download PDF

23. The Data Value Quest: A Holistic Semantic Approach at Bosch

Author

Zhou, Baifan, Zheng, Zhuoxun, Zhou, Dongzhuoran, Cheng, Gong, Jiménez-Ruiz, Ernesto, Tran, Trung-Kien, Stepanova, Daria, Gad-Elrab, Mohamed H., Nikolov, Nikolay, Soylu, Ahmet, Kharlamov, Evgeny, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Groth, Paul, editor, Rula, Anisa, editor, Schneider, Jodi, editor, Tiddi, Ilaria, editor, Simperl, Elena, editor, Alexopoulos, Panos, editor, Hoekstra, Rinke, editor, Alam, Mehwish, editor, Dimou, Anastasia, editor, and Tamper, Minna, editor

Published
2022
Full Text
View/download PDF

24. An Analysis of Data Production Based on the Consistency of Decision Matrices

Author

Sahin, Bekir, Yazidi, Anis, Roman, Dumitru, Uddin, Md Zia, Soylu, Ahmet, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kahraman, Cengiz, editor, Cebi, Selcuk, editor, Cevik Onar, Sezi, editor, Oztaysi, Basar, editor, Tolga, A. Cagri, editor, and Sari, Irem Ucal, editor

Published
2022
Full Text
View/download PDF

25. On Converting Crisp Failure Possibility into Probability for Reliability of Complex Systems

Author

Sahin, Bekir, Yazidi, Anis, Roman, Dumitru, Uddin, Md Zia, Soylu, Ahmet, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kahraman, Cengiz, editor, Cebi, Selcuk, editor, Cevik Onar, Sezi, editor, Oztaysi, Basar, editor, Tolga, A. Cagri, editor, and Sari, Irem Ucal, editor

Published
2022
Full Text
View/download PDF

28. Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis.

Author

Gurcan, Fatih and Soylu, Ahmet

Subjects
*TUMOR diagnosis, *RANDOM forest algorithms, *DIAGNOSTIC imaging, *PREDICTION models, *BIBLIOGRAPHIC databases, *STATISTICAL sampling, *EARLY detection of cancer, *ONCOLOGY, *DESCRIPTIVE statistics, *COMPUTER-aided diagnosis, *TUMORS, *MACHINE learning, *COMPARATIVE studies, *ONCOLOGISTS, *ALGORITHMS
Abstract

Simple Summary: This research focuses on improving cancer diagnosis and prognosis by addressing a common problem in data analysis known as class imbalance, where some patient groups are underrepresented. The authors aim to evaluate different resampling methods that can balance the data and enhance the performance of various classification algorithms used to predict cancer outcomes. By testing a wide range of techniques across multiple cancer datasets, this study identifies the best-performing classifier, Random Forest, along with the most effective resampling method, SMOTEENN. These findings provide valuable insights for researchers and healthcare professionals, enabling them to make more accurate predictions and ultimately improve patient care. This research could pave the way for the development of more reliable machine learning applications in the medical field. Background/Objectives: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. Methods: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison. Results: The results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes. Conclusions: This research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

29. Effects of Sandbag-Free Follow-up After Manual Compression in Patients Who Underwent Transfemoral Access for Percutaneous Intervention.

Author

Soylu, Ahmet, Şahin, Ahmet Taha, Kan, Hasan, Sarı, Hasan, and Tatar, Sefa

Abstract

Copyright of Archives of the Turkish Society of Cardiology / Türk Kardiyoloji Derneği Arşivi is the property of KARE Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
Full Text
View/download PDF

34. TheyBuyForYou: Enabling Procurement Data Value Chains

Author

Simperl, Elena, Corcho, Oscar, Grobelnik, Marko, Roman, Dumitru, Soylu, Ahmet, Ruíz, María Jesús Fernández, Gatti, Stefano, Taggart, Chris, Klima, Urška Skok, Uliana, Annie Ferrari, Makgill, Ian, Turk, Philip, Lech, Till Christopher, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fazio, Maria, editor, and Zimmermann, Wolf, editor

Published
2020
Full Text
View/download PDF

35. Towards Optimal Ship Navigation Using Image Processing

Author

Sahin, Bekir, Uddin, Zia, Soylu, Ahmet, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Yin, Zhaozheng, editor, Kim, Edward, editor, Bender, Jan, editor, Subr, Kartic, editor, Kwon, Bum Chul, editor, Zhao, Jian, editor, Kalkofen, Denis, editor, and Baciu, George, editor

Published
2020
Full Text
View/download PDF

36. Enhancing Public Procurement in the European Union Through Constructing and Exploiting an Integrated Knowledge Graph

Author

Soylu, Ahmet, Corcho, Oscar, Elvesæter, Brian, Badenes-Olmedo, Carlos, Martínez, Francisco Yedro, Kovacic, Matej, Posinkovic, Matej, Makgill, Ian, Taggart, Chris, Simperl, Elena, Lech, Till C., Roman, Dumitru, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pan, Jeff Z., editor, Tamma, Valentina, editor, d’Amato, Claudia, editor, Janowicz, Krzysztof, editor, Fu, Bo, editor, Polleres, Axel, editor, Seneviratne, Oshani, editor, and Kagal, Lalana, editor

Published
2020
Full Text
View/download PDF

42. The Data Value Quest: A Holistic Semantic Approach at Bosch

Author

Zhou, Baifan, primary, Zheng, Zhuoxun, additional, Zhou, Dongzhuoran, additional, Cheng, Gong, additional, Jiménez-Ruiz, Ernesto, additional, Tran, Trung-Kien, additional, Stepanova, Daria, additional, Gad-Elrab, Mohamed H., additional, Nikolov, Nikolay, additional, Soylu, Ahmet, additional, and Kharlamov, Evgeny, additional

Published
2022
Full Text
View/download PDF

43. Wiki-based Prompts for Enhancing Relation Extraction using Language Models

Author

Layegh, Amirhossein, Payberah, Amir H., Soylu, Ahmet, Roman, Dumitru, Matskin, Mihhail, Layegh, Amirhossein, Payberah, Amir H., Soylu, Ahmet, Roman, Dumitru, and Matskin, Mihhail

Abstract

Prompt-tuning and instruction-tuning of language models have exhibited significant results in few-shot Natural Language Processing (NLP) tasks, such as Relation Extraction (RE), which involves identifying relationships between entities within a sentence. However, the effectiveness of these methods relies heavily on the design of the prompts. A compelling question is whether incorporating external knowledge can enhance the language model's understanding of NLP tasks. In this paper, we introduce wiki-based prompt construction that leverages Wikidata as a source of information to craft more informative prompts for both prompt-tuning and instruction-tuning of language models in RE. Our experiments show that using wiki-based prompts enhances cutting-edge language models in RE, emphasizing their potential for improving RE tasks. Our code and datasets are available at GitHub 1, Part of ISBN 9798400702433QC 20240719

Published
2024
Full Text
View/download PDF

44. Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning

Author

Sajid, Saliha, von Zernichow, Bjørn Marius, Soylu, Ahmet, Roman, Dumitru, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garoufallou, Emmanouel, editor, Fallucchi, Francesca, editor, and William De Luca, Ernesto, editor

Published
2019
Full Text
View/download PDF

45. Towards a Knowledge Graph Based Platform for Public Procurement

Author

Simperl, Elena, Corcho, Oscar, Grobelnik, Marko, Roman, Dumitru, Soylu, Ahmet, Ruíz, María Jesús Fernández, Gatti, Stefano, Taggart, Chris, Klima, Urška Skok, Uliana, Annie Ferrari, Makgill, Ian, Lech, Till Christopher, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garoufallou, Emmanouel, editor, Sartori, Fabio, editor, Siatri, Rania, editor, and Zervas, Marios, editor

Published
2019
Full Text
View/download PDF

46. Navigating OWL 2 Ontologies Through Graph Projection

Author

Soylu, Ahmet, Kharlamov, Evgeny, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garoufallou, Emmanouel, editor, Sartori, Fabio, editor, Siatri, Rania, editor, and Zervas, Marios, editor

Published
2019
Full Text
View/download PDF

47. Towards an Ontology for Public Procurement Based on the Open Contracting Data Standard

Author

Soylu, Ahmet, Elvesæter, Brian, Turk, Philip, Roman, Dumitru, Corcho, Oscar, Simperl, Elena, Konstantinidis, George, Lech, Till Christopher, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pappas, Ilias O., editor, Mikalef, Patrick, editor, Dwivedi, Yogesh K., editor, Jaccheri, Letizia, editor, Krogstie, John, editor, and Mäntymäki, Matti, editor

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results