Publications – Smart Data Analytics

You can find a list of completed Phd theses here.

423 entries « ‹ 2 of 9 › »

2020

Mohamed, Heba; Fathalla, Said; Lehmann, Jens; Jabeen, Hajira

A Distributed Approach for Parsing Large-scale OWL Datasets Inproceedings

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2020, Volume 2: KEOD, Budapest, Hungary, November 2-4, 2020, pp. 227–234, SCITEPRESS, 2020.

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Say, Aysegul; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören

Semantic Representation of Physics Research Data Inproceedings

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Nayyeri, Mojtaba; Alam, Mirza Mohtashim; Lehmann, Jens; Vahdati, Sahar

3D Learning and Reasoning in Link Prediction Over Knowledge Graphs Journal Article

IEEE Access, 8 , pp. 196459–196471, 2020.

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Armitage, Jason; Kacupaj, Endri; Tahmasebzadeh, Golsa; Swati, ; Maleshkova, Maria; Ewerth, Ralph; Lehmann, Jens

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities Inproceedings

CIKM'20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, pp. 2967–2974, ACM, 2020.

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Iglesias, Enrique; Jozashoori, Samaneh; Chaves-Fraga, David; Collarana, Diego; Vidal, Maria-Esther

SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs Inproceedings

CIKM'20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, pp. 3039–3046, ACM, 2020.

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Mulang, Isaiah Onando; Singh, Kuldeep; Prabhu, Chaitali; Nadgeri, Abhishek; Hoffart, Johannes; Lehmann, Jens

Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models Inproceedings

CIKM'20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, pp. 2157–2160, ACM, 2020.

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Nedelchev, Rostislav; Lehmann, Jens; Usbeck, Ricardo

Language Model Transformers as Evaluators for Open-domain Dialogues Inproceedings

Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pp. 6797–6808, International Committee on Computational Linguistics, 2020.

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Xu, Chengjin; Nayyeri, Mojtaba; Alkhoury, Fouad; Yazdi, Hamed Shariat; Lehmann, Jens

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation Inproceedings

Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pp. 1583–1593, International Committee on Computational Linguistics, 2020.

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Xu, Chengjin; Nayyeri, Mojtaba; Chen, Yung-Yu; Lehmann, Jens

Knowledge Graph Embeddings in Geometric Algebras Inproceedings

Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pp. 530–544, International Committee on Computational Linguistics, 2020.

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Rivas, Ariam; Grangel-González, Irlán; Collarana, Diego; Lehmann, Jens; Vidal, Maria-Esther

Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings Inproceedings

Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Bratislava, Slovakia, September 14-17, 2020, Proceedings, Part II, pp. 179–194, Springer, 2020.

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Armitage, Jason; Thakur, Shramana; Tripathi, Rishi; Maleshkova, Maria; Lehmann, Jens

Training Multimodal Systems for Classification with Multiple Objectives Inproceedings

Proceedings of the 1st International Workshop on Cross-lingual Event-centric Open Analytics co-located with the 17th Extended Semantic Web Conference (ESWC 2020), Heraklion, Crete, Greece, June 3, 2020 (online event due to COVID-19 outbreak), pp. 57–71, CEUR-WS.org, 2020.

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Tavakoli, MohammadReza; Elias, Mirette; Kismihók, Gábor; Auer, Sören

Quality Prediction of Open Educational Resources A Metadata-based Approach Inproceedings

20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020, Tartu, Estonia, July 6-9, 2020, pp. 29–31, IEEE, 2020.

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Stadler, Claus; Bin, Simon; Wenige, Lisa; Bühmann, Lorenz; Lehmann, Jens

Schema-agnostic SPARQL-driven faceted search benchmark generation Journal Article

J. Web Semant., 65 , pp. 100614, 2020.

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Zafar, Hamid; Dubey, Mohnish; Lehmann, Jens; Demidova, Elena

IQA: Interactive query construction in semantic question answering systems Journal Article

J. Web Semant., 64 , pp. 100586, 2020.

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Khvalchik, Maria; Galkin, Mikhail

Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks Inproceedings

Proceedings of the Workshop on Hybrid Intelligence for Natural Language Processing Tasks (HI4NLP 2020) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 2020, pp. 29–33, CEUR-WS.org, 2020.

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Galkin, Mikhail; Trivedi, Priyansh; Maheshwari, Gaurav; Usbeck, Ricardo; Lehmann, Jens

Message Passing for Hyper-Relational Knowledge Graphs Inproceedings

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 7346–7359, Association for Computational Linguistics, 2020.

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Alam, Mirza Mohtashim; Jabeen, Hajira; Ali, Mehdi; Mohiuddin, Karishma; Lehmann, Jens

Sffinity Dependent Negative Sampling for Knowledge Graph Embeddings Inproceedings

Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020) co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020), Heraklion, Greece, June 02, 2020 - moved online, CEUR-WS.org, 2020.

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Bader, Sebastian R; Maleshkova, Maria

Towards Integrated Data Control for Digital Twins in Industry 4.0 Inproceedings

Proceedings of the International Workshop on Semantic Digital Twins co-located with the 17th Extended Semantic Web Conference, SeDiT@ESWC 2020, Heraklion, Greece, June 3, 2020, CEUR-WS.org, 2020.

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Rehm, Georg; Galanis, Dimitris; Labropoulou, Penny; Piperidis, Stelios; Welß, Martin; Usbeck, Ricardo; Köhler, Joachim; Deligiannis, Miltos; Gkirtzou, Katerina; Fischer, Johannes; Chiarcos, Christian; Feldhus, Nils; Schneider, Julián Moreno; Kintzel, Florian; Montiel-Ponsoda, Elena; 'i, V{'i}ctor Rodr; McCrae, John Philip; Laqua, David; Theile, Irina Patricia; Dittmar, Christian; Bontcheva, Kalina; Roberts, Ian; Vasiljevs, Andrejs; Lagzdins, Andis

Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability Inproceedings

Proceedings of the 1st International Workshop on Language Technology Platforms, IWLTP@LREC 2020, Marseille, France, May 2020, pp. 96–107, European Language Resources Association, 2020.

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Banerjee, Debayan; Chaudhuri, Debanjan; Dubey, Mohnish; Lehmann, Jens

PNEL: Pointer Network Based End-To-End Entity Linking over Knowledge Graphs Inproceedings

The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 21–38, Springer, 2020.

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Chakraborty, Nilesh; Chao, Yao; Luo, Chengwen; Li, Jianqiang; Ziying, Pan; Chen, Jie; Pan, Yi

On Understanding the Impact of RTT in the Mobile Network for Detecting the Rogue UAVs Journal Article

IEEE Trans. Cogn. Commun. Netw., 6 (4), pp. 1218–1229, 2020.

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Alam, Mirza Mohtashim; Jabeen, Hajira; Ali, Mehdi; Mohiuddin, Karishma; Lehmann, Jens

Affinity Dependent Negative Sampling for Knowledge Graph Embeddings Inproceedings

Links | BibTeX

2019

Vahdati, Sahar

Collaborative Integration, Publishing and Analysis of Distributed Scholarly Metadata PhD Thesis

University of Bonn, 2019.

Abstract | Links | BibTeX

@phdthesis{VahdatiThesis,
title = {Collaborative Integration, Publishing and Analysis of Distributed Scholarly Metadata},
author = {Sahar Vahdati},
url = {https://d-nb.info/1181855969/34},
year = {2019},
date = {2019-01-01},
school = {University of Bonn},
abstract = {Research is becoming increasingly digital, interdisciplinary, and data-driven and affects different en- vironments in addition to academia, such as industry, and government. Research output representation, publication, mining, analysis, and visualization are taken to a new level, driven by the increased use of Web standards and digital scholarly communication initiatives. The number of scientific publications produced by new players and the increasing digital availability of scholarly artifacts, and associated metadata are other drivers of the substantial growth in scholarly communication. The heterogeneity of scholarly artifacts and their metadata spread over different Web data sources poses a major challenge for researchers with regard to search, retrieval and exploration. For example, it has become difficult to keep track of relevant scientific results, to stay up-to-date with new scientific events and running projects, as well as to find potential future collaborators. Thus, assisting researchers with a broader integration, management, and analysis of scholarly metadata can lead to new opportunities in research and to new ways of conducting research. The data integration problem has been extensively addressed by communities in the Database, Artificial Intelligence and Semantic Web fields. However, a share of the interoperability issues are domain specific and new challenges with regard to schema, structure, or domain, arise in the context of scholarly metadata integration. Thus, a method is needed to support scientific communities to integrate and manage heterogeneous scholarly metadata in order to derive insightful analysis (e.g., quality assessment of scholarly artifacts). This thesis tackles the problem of scholarly metadata integration and develops a life cycle methodology to facilitate the integrated use of different methods, analysis techniques, and tools for improving scholarly communication. Some key steps of the metadata life cycle are implemented using a collaborative platform, which allows to keep the research communities in the loop. In particular, the use of collaborative methods is beneficial for the acquisition, integration, curation and utilization of scholarly metadata. We conducted empirical evaluations to assess the effectiveness and efficiency of the proposed approach. Our metadata transformation from legacy resources achieves reasonable performance and results in better metadata maintainability. The interlinking of metadata enhances the coherence of scholarly information spaces both qualitatively and quantitatively. Our metadata analysis techniques provide a precise quality assessment of scholarly artifacts, taking into account the perspectives of multiple stakeholders, while maintaining compatibility with existing ranking systems. These empirical evaluations and the concrete applications with a particular focus on collaborative aspects demonstrate the benefits of integrating distributed scholarly metadata.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

Research is becoming increasingly digital, interdisciplinary, and data-driven and affects different en- vironments in addition to academia, such as industry, and government. Research output representation, publication, mining, analysis, and visualization are taken to a new level, driven by the increased use of Web standards and digital scholarly communication initiatives. The number of scientific publications produced by new players and the increasing digital availability of scholarly artifacts, and associated metadata are other drivers of the substantial growth in scholarly communication. The heterogeneity of scholarly artifacts and their metadata spread over different Web data sources poses a major challenge for researchers with regard to search, retrieval and exploration. For example, it has become difficult to keep track of relevant scientific results, to stay up-to-date with new scientific events and running projects, as well as to find potential future collaborators. Thus, assisting researchers with a broader integration, management, and analysis of scholarly metadata can lead to new opportunities in research and to new ways of conducting research. The data integration problem has been extensively addressed by communities in the Database, Artificial Intelligence and Semantic Web fields. However, a share of the interoperability issues are domain specific and new challenges with regard to schema, structure, or domain, arise in the context of scholarly metadata integration. Thus, a method is needed to support scientific communities to integrate and manage heterogeneous scholarly metadata in order to derive insightful analysis (e.g., quality assessment of scholarly artifacts). This thesis tackles the problem of scholarly metadata integration and develops a life cycle methodology to facilitate the integrated use of different methods, analysis techniques, and tools for improving scholarly communication. Some key steps of the metadata life cycle are implemented using a collaborative platform, which allows to keep the research communities in the loop. In particular, the use of collaborative methods is beneficial for the acquisition, integration, curation and utilization of scholarly metadata. We conducted empirical evaluations to assess the effectiveness and efficiency of the proposed approach. Our metadata transformation from legacy resources achieves reasonable performance and results in better metadata maintainability. The interlinking of metadata enhances the coherence of scholarly information spaces both qualitatively and quantitatively. Our metadata analysis techniques provide a precise quality assessment of scholarly artifacts, taking into account the perspectives of multiple stakeholders, while maintaining compatibility with existing ranking systems. These empirical evaluations and the concrete applications with a particular focus on collaborative aspects demonstrate the benefits of integrating distributed scholarly metadata.

Esteves, Diego

Automating the Fact-Checking Task: Challenges and Directions PhD Thesis

University of Bonn, 2019.

Abstract | Links | BibTeX

@phdthesis{EstevesThesis,
title = {Automating the Fact-Checking Task: Challenges and Directions},
author = {Diego Esteves},
url = {http://hdl.handle.net/20.500.11811/8030},
year = {2019},
date = {2019-01-01},
school = {University of Bonn},
abstract = {In recent years, misinformation has caused widespread alarm and has become a global concern, given the negative impact placed on society, democratic institutions and even computing systems whose primary objective is to serve as a reliable information channel, e.g., KnowledgeBases (KBs). The proliferation of fake news has a wide range of characteristics and different motivations. For instance, it can be produced unintentionally (e.g., the creation process of KBswhich is mostly based on automated information extraction methods, thus naturally error-prone)or intentionally (e.g., the spread of misinformation through social media to persuade). Thus, they differ considerably in complexity, structure and number of arguments and propositions. To further exacerbate this problem, an ever-increasing amount of fake news on the Web has created another challenge to drawing correct information. This huge sea of data makes it very difficult for human fact-checkers and journalists to assess all the information manually. Therefore, addressing this problem is of utmost importance to minimize real-world circumstances which may provoke a negative impact on society, in general. Presently, Fact-Checking has emerged as a branch of natural language processing devoted to achieving this feat. Under this umbrella, Automated Fact-Checking frameworks have been proposed to perform claim verification. However, given the nature of the problem, different tasks need to be performed, from natural language understanding to source trustworthiness analysis and credibility scoring. In this thesis, we tackle the problem of fake news and underlying challenges related to the process of estimating the veracity of a given claim, discussing challenges and proposing novel models to improve the current state of the art on different sub-tasks. Thus, besides the principal task (i.e., performing automated fact-checking) we also investigate the recognition of entities on noisy data and the computation of web site credibility. Ultimately, due to the challenging nature of the automated fact-checking task - which requires a complex analysis over several perspectives - we also contribute towards the reproducibility of scientific experiments. First, we tackle the named entity recognition problem. We propose a novel multi-level approach named HORUS which - given an input token - generates heuristics based on computer vision and text mining techniques. These heuristics are then used to detect and classify named entities on noisy data (e.g., The Web). Second, we propose WebCred, a novel model to compute the credibility score of a given website, regardless of dependency on search engine results, which is a limiting factor when dealing with real scenarios. WebCreddoes not require any third-party service and is 100% open-source. Third, we conduct several empirical evaluations and extend DeFacto, a fact-checking framework initially designed to verify English claims in RDF format. DeFacto supports both structured claims (e.g., triple-like) as well as complex claims (i.e., natural language sentences). Last, but not least, we consistently contributed towards better reproducibility research tools, methods, and methodologies. We proposed ontologies (MEX, ML-Schema) and tools (LOG4MEX, MEX-Interfaces, Web4MEX, WASOTA) which turned into state of the art for better reproducibility of machine learning experiments, becoming part of a global W3C community},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

In recent years, misinformation has caused widespread alarm and has become a global concern, given the negative impact placed on society, democratic institutions and even computing systems whose primary objective is to serve as a reliable information channel, e.g., KnowledgeBases (KBs). The proliferation of fake news has a wide range of characteristics and different motivations. For instance, it can be produced unintentionally (e.g., the creation process of KBswhich is mostly based on automated information extraction methods, thus naturally error-prone)or intentionally (e.g., the spread of misinformation through social media to persuade). Thus, they differ considerably in complexity, structure and number of arguments and propositions. To further exacerbate this problem, an ever-increasing amount of fake news on the Web has created another challenge to drawing correct information. This huge sea of data makes it very difficult for human fact-checkers and journalists to assess all the information manually. Therefore, addressing this problem is of utmost importance to minimize real-world circumstances which may provoke a negative impact on society, in general. Presently, Fact-Checking has emerged as a branch of natural language processing devoted to achieving this feat. Under this umbrella, Automated Fact-Checking frameworks have been proposed to perform claim verification. However, given the nature of the problem, different tasks need to be performed, from natural language understanding to source trustworthiness analysis and credibility scoring. In this thesis, we tackle the problem of fake news and underlying challenges related to the process of estimating the veracity of a given claim, discussing challenges and proposing novel models to improve the current state of the art on different sub-tasks. Thus, besides the principal task (i.e., performing automated fact-checking) we also investigate the recognition of entities on noisy data and the computation of web site credibility. Ultimately, due to the challenging nature of the automated fact-checking task - which requires a complex analysis over several perspectives - we also contribute towards the reproducibility of scientific experiments. First, we tackle the named entity recognition problem. We propose a novel multi-level approach named HORUS which - given an input token - generates heuristics based on computer vision and text mining techniques. These heuristics are then used to detect and classify named entities on noisy data (e.g., The Web). Second, we propose WebCred, a novel model to compute the credibility score of a given website, regardless of dependency on search engine results, which is a limiting factor when dealing with real scenarios. WebCreddoes not require any third-party service and is 100% open-source. Third, we conduct several empirical evaluations and extend DeFacto, a fact-checking framework initially designed to verify English claims in RDF format. DeFacto supports both structured claims (e.g., triple-like) as well as complex claims (i.e., natural language sentences). Last, but not least, we consistently contributed towards better reproducibility research tools, methods, and methodologies. We proposed ontologies (MEX, ML-Schema) and tools (LOG4MEX, MEX-Interfaces, Web4MEX, WASOTA) which turned into state of the art for better reproducibility of machine learning experiments, becoming part of a global W3C community

Angles, Renzo; Thakkar, Harsh; Tomaszuk, Dominik

RDF and Property Graphs Interoperability: Status and Issues Inproceedings

Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asunción, Paraguay, June 3-7, 2019, CEUR-WS.org, 2019.

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Jalota, Rricha; Trivedi, Priyansh; Maheshwari, Gaurav; Ngomo, Axel-Cyrille Ngonga; Usbeck, Ricardo

An Approach for Ex-Post-Facto Analysis of Knowledge Graph-Driven Chatbots - The DBpedia Chatbot Inproceedings

Chatbot Research and Design - Third International Workshop, CONVERSATIONS 2019, Amsterdam, The Netherlands, November 19-20, 2019, Revised Selected Papers, pp. 19–33, Springer, 2019.

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Tasnim, Mayesha; Collarana, Diego; Graux, Damien; Galkin, Mikhail; Vidal, Maria-Esther

COMET: A Contextualized Molecule-Based Matching Technique Inproceedings

Database and Expert Systems Applications - 30th International Conference, DEXA 2019, Linz, Austria, August 26-29, 2019, Proceedings, Part I, pp. 175–185, Springer, 2019.

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Athanasiou, Spiros; Giannopoulos, Giorgos; Graux, Damien; Karagiannakis, Nikos; Lehmann, Jens; Ngomo, Axel-Cyrille Ngonga; Patroumpas, Kostas; Sherif, Mohamed Ahmed; Skoutas, Dimitrios

Big POI data integration with Linked Data technologies Inproceedings

Advances in Database Technology - 22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, March 26-29, 2019, pp. 477–488, OpenProceedings.org, 2019.

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Rahimbayli, Sattar; Lehmann, Jens; Deserno, Thomas M

Pay for Performance in Electronic Data Capture for Clinical Trials and Medical Registries Inproceedings

ICT for Health Science Research - Proceedings of the EFMI 2019 Special Topic Conference - 7-10 April 2019, Hanover, Germany, pp. 221–225, IOS Press, 2019.

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Chawla, Piyush; Esteves, Diego; Pujar, Karthik; Lehmann, Jens

SimpleLSTM: A Deep-Learning Approach to Simple-Claims Classification Inproceedings

Progress in Artificial Intelligence, 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3-6, 2019, Proceedings, Part II, pp. 244–255, Springer, 2019.

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Fathalla, Said; Lange, Christoph; Auer, Sören

A Human-Friendly Query Generation Frontend for a Scientific Events Knowledge Graph Inproceedings

Digital Libraries for Open Knowledge - 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Oslo, Norway, September 9-12, 2019, Proceedings, pp. 200–214, Springer, 2019.

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Vahdati, Sahar; Fathalla, Said; Auer, Sören; Lange, Christoph; Vidal, Maria-Esther

Semantic Representation of Scientific Publications Inproceedings

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Bader, Sebastian R; Maleshkova, Maria

Towards Enforceable Usage Policies for Industry 4.0 Inproceedings

Joint Proceedings of the 1st International Workshop on Knowledge Graph Building and 1st International Workshop on Large Scale RDF Analytics co-located with 16th Extended Semantic Web Conference (ESWC 2019), Portorov z, Slovenia, June 3, 2019, pp. 75–84, CEUR-WS.org, 2019.

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Cremaschi, Marco; Rula, Anisa; Siano, Alessandra; Paoli, Flavio De

MantisTable: A Tool for Creating Semantic Annotations on Tabular Data Inproceedings

The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Portorov z, Slovenia, June 2-6, 2019, Revised Selected Papers, pp. 18–23, Springer, 2019.

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Dadwal, Rajjat; Graux, Damien; Sejdiu, Gezim; Jabeen, Hajira; Lehmann, Jens

Clustering Pipelines of Large RDF POI Data Inproceedings

The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Portorov z, Slovenia, June 2-6, 2019, Revised Selected Papers, pp. 24–27, Springer, 2019.

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Fathalla, Said; Lange, Christoph; Auer, Sören

EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities Inproceedings

The Semantic Web - 16th International Conference, ESWC 2019, Portorov z, Slovenia, June 2-6, 2019, Proceedings, pp. 427–442, Springer, 2019.

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Ibrahim, Shimaa; Fathalla, Said; Yazdi, Hamed Shariat; Lehmann, Jens; Jabeen, Hajira

OECM: A Cross-Lingual Approach for Ontology Enrichment Inproceedings

The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Portorov z, Slovenia, June 2-6, 2019, Revised Selected Papers, pp. 100–104, Springer, 2019.

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Bader, Sebastian R; Grangel-González, Irlán; Tasnim, Mayesha; Lohmann, Steffen

Structuring the Industry 4.0 Landscape Inproceedings

24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, Zaragoza, Spain, September 10-13, 2019, pp. 224–231, IEEE, 2019.

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Jabeen, Hajira; Tahara, Nargis; Lehmann, Jens

EvoChef: Show Me What to Cook! Artificial Evolution of Culinary Arts Inproceedings

Computational Intelligence in Music, Sound, Art and Design - 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24-26, 2019, Proceedings, pp. 156–172, Springer, 2019.

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Bader, Sebastian R; Maleshkova, Maria

The Semantic Asset Administration Shell Inproceedings

Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings, pp. 159–174, Springer, 2019.

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Bajwa, Awais Manzoor; Collarana, Diego; Vidal, Maria-Esther

Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings Inproceedings

Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings, pp. 249–255, Springer, 2019.

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Gottschalk, Simon; Tempelmeier, Nicolas; Kniesel, Günter; Iosifidis, Vasileios; Fetahu, Besnik; Demidova, Elena

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows Inproceedings

Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings, pp. 359–366, Springer, 2019.

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Gusmita, Ria Hari; Jalota, Rricha; Vollmers, Daniel; Reineke, Jan; Ngomo, Axel-Cyrille Ngonga; Usbeck, Ricardo

QUANT - Question Answering Benchmark Curator Inproceedings

Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings, pp. 343–358, Springer, 2019.

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Ibrahim, Shimaa; Fathalla, Said; Yazdi, Hamed Shariat; Lehmann, Jens; Jabeen, Hajira

From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment Inproceedings

Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings, pp. 215–230, Springer, 2019.

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Kaur, Paramjot; Blücher, Vincent; Jalota, Rricha; Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga; Usbeck, Ricardo

Offline Question Answering over Linked Data using Limited Resources Inproceedings

Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019), Karlsruhe, Germany, September 9th - to - 12th, 2019, CEUR-WS.org, 2019.

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Sejdiu, Gezim; Graux, Damien; Khan, Imran; Lytra, Ioanna; Jabeen, Hajira; Lehmann, Jens

Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation Inproceedings

Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019, Proceedings, pp. 295–309, Springer, 2019.

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Stadler, Claus; Wenige, Lisa; Martin, Michael; Tramp, Sebastian; Junghanns, Kurt

RDF-based Deployment Pipelining for Efficient Dataset Release Management Inproceedings

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Sui, Danning; Sejdiu, Gezim; Graux, Damien; Lehmann, Jens

The Hubs and Authorities Transaction Network Analysis using the SANSA framework Inproceedings

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Hemid, Ahmad; Halilaj, Lavdim; Khiat, Abderrahmane; Lohmann, Steffen

RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data Inproceedings

Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019, Volume 2: KEOD, Vienna, Austria, September 17-19, 2019, pp. 508–516, ScitePress, 2019.

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Nejad, Najmeh Mousavi; Graux, Damien; Collarana, Diego

Towards Measuring Risk Factors in Privacy Policies Inproceedings

Proceedings of the Workshop on Artificial Intelligence and the Administrative State co-located with 17th International Conference on AI and Law (ICAIL 2019), Montreal, QC, Canada, June 17, 2019, pp. 18–20, CEUR-WS.org, 2019.

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423 entries « ‹ 2 of 9 › »