Publications – Smart Data Analytics

You can find a list of completed Phd theses here.

503 entries « ‹ 2 of 11 › »

2021

Tahmasebzadeh, Golsa; Kacupaj, Endri; Müller-Budack, Eric; Hakimov, Sherzod; Lehmann, Jens; Ewerth, Ralph

GeoWINE: Geolocation based Wiki, Image, News and Event Retrieval Inproceedings

In: SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, pp. 2565–2569, ACM, 2021.

Links | BibTeX

Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza; Rimaz, Mohammad Hossein; Ioini, Nabil El; Tkalcic, Marko; Trattner, Christoph; Tillo, Tammam

Recommending Videos in Cold Start With Automatic Visual Tags Inproceedings

In: Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021, Utrecht, The Netherlands, June 21-25, 2021, pp. 54–60, ACM, 2021.

Links | BibTeX

Bagozi, Ada; Bianchini, Devis; Rula, Anisa

A Multi-perspective Model of Smart Products for Designing Web-Based Services on the Production Chain Inproceedings

In: Web Information Systems Engineering - WISE 2021 - 22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26-29, 2021, Proceedings, Part II, pp. 447–462, Springer, 2021.

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Bastos, Anson; Nadgeri, Abhishek; Singh, Kuldeep; Shekarpour, Isaiah Onando Mulang'and Saeedeh; Hoffart, Johannes; Kaul, Manohar

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network Inproceedings

In: WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pp. 1673–1685, ACM / IW3C2, 2021.

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Nayyeri, Mojtaba; Cil, Gökce Müge; Vahdati, Sahar; Osborne, Francesco; Kravchenko, Andrey; Angioni, Simone; Salatino, Angelo A.; Recupero, Diego Reforgiato; Motta, Enrico; Lehmann, Jens

Link Prediction of Weighted textbackslashfontencodingLELAtextbackslashselectfontḩar47riples for Knowledge Graph Completion Within the Scholarly textbackslashDJomain Journal Article

In: IEEE Access, vol. 9, pp. 116002–116014, 2021.

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Nayyeri, Mojtaba; Cil, Gökce Müge; Vahdati, Sahar; Osborne, Francesco; Kravchenko, Andrey; Angioni, Simone; Salatino, Angelo A.; Recupero, Diego Reforgiato; Motta, Enrico; Lehmann, Jens

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain Journal Article

In: IEEE Access, vol. 9, pp. 116002–116014, 2021.

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Bharadhwaj, Vinay Srinivas; Ali, Mehdi; Birkenbihl, Colin; Mubeen, Sarah; Lehmann, Jens; Hofmann-Apitius, Martin; Hoyt, Charles Tapley; Domingo-Fernández, Daniel

CLEP: a hybrid data- and knowledge-driven framework for generating patient representations Journal Article

In: Bioinform., vol. 37, no. 19, pp. 3311–3318, 2021.

Links | BibTeX

Grubenmann, Tobias; Lehmann, Jens

Geolog: Scalable Logic Programming on Spatial Data Inproceedings

In: Proceedings 37th International Conference on Logic Programming (Technical Communications), ICLP Technical Communications 2021, Porto (virtual event), 20-27th September 2021, pp. 191–204, 2021.

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Hogan, Aidan; Blomqvist, Eva; Cochez, Michael; d'Amato, Claudia; Melo, Gerard; Gutiérrez, Claudio; Kirrane, Sabrina; Gayo, José Emilio Labra; Navigli, Roberto; Neumaier, Sebastian; Ngomo, Axel-Cyrille Ngonga; Polleres, Axel; Rashid, Sabbir M.; Rula, Anisa; Schmelzeisen, Lukas; Sequeda, Juan F.; Staab, Steffen; Zimmermann, Antoine

Knowledge Graphs Journal Article

In: ACM Comput. Surv., vol. 54, no. 4, pp. 71:1–71:37, 2021.

Links | BibTeX

Nayyeri, Mojtaba; Cil, Gökce Müge; Vahdati, Sahar; Osborne, Francesco; Rahman, Mahfuzur; Angioni, Simone; Salatino, Angelo A.; Recupero, Diego Reforgiato; Vassilyeva, Nadezhda; Motta, Enrico; Lehmann, Jens

Trans4E: Link prediction on scholarly knowledge graphs Journal Article

In: Neurocomputing, vol. 461, pp. 530–542, 2021.

Links | BibTeX

Ali, Mohamed Hassan Mohamed; Fathalla, Said; Kholief, Mohamed; Hassan, Yasser Fouad

Learning Non-Taxonomic Relations of Ontologies: A Systematic Review Journal Article

In: Int. J. Semantic Web Inf. Syst., vol. 17, no. 1, pp. 97–122, 2021.

Links | BibTeX

Oliveira, Italo Lopes; Fileto, Renato; Speck, René; Garcia, Lu'is Paulo F.; Moussallem, Diego; Lehmann, Jens

Towards holistic Entity Linking: Survey and directions Journal Article

In: Inf. Syst., vol. 95, pp. 101624, 2021.

Links | BibTeX

Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Sharifzadeh, Sahand; Tresp, Volker; Lehmann, Jens

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings Journal Article

In: J. Mach. Learn. Res., vol. 22, pp. 82:1–82:6, 2021.

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Ngomo, Axel-Cyrille Ngonga; Sherif, Mohamed Ahmed; Georgala, Kleanthi; Hassan, Mofeed Mohamed; Dreßler, Kevin; Lyko, Klaus; Obraczka, Daniel; Soru, Tommaso

LIMES: A Framework for Link Discovery on the Semantic Web Journal Article

In: Künstliche Intell., vol. 35, no. 3, pp. 413–423, 2021.

Links | BibTeX

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

Discover Relations in the Industry 4.0 Standards Via Unsupervised Learning on Knowledge Graph Embeddings Journal Article

In: J. Data Intell., vol. 2, no. 3, pp. 326–347, 2021.

Links | BibTeX

Lackner, Arthur; Fathalla, Said; Nayyeri, Mojtaba; Behrend, Andreas; Manthey, Rainer; Auer, Sören; Lehmann, Jens; Vahdati, Sahar

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade Journal Article

In: Scientometrics, vol. 126, no. 9, pp. 8129–8151, 2021.

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Vahdati, Sahar; Fathalla, Said; Lange, Christoph; Behrend, Andreas; Say, Aysegul; Say, Zeynep; Auer, Sören

A comprehensive quality assessment framework for scientific events Journal Article

In: Scientometrics, vol. 126, no. 1, pp. 641–682, 2021.

Links | BibTeX

Adamczak, Jens; Deldjoo, Yashar; Moghaddam, Farshad Bakhshandegan; Knees, Peter; Leyson, Gerard Paul; Monreal, Philipp

Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019 Journal Article

In: ACM Trans. Intell. Syst. Technol., vol. 12, no. 1, pp. 1:1–1:20, 2021.

Links | BibTeX

Chakraborty, Nilesh; Lukovnikov, Denis; Maheshwari, Gaurav; Trivedi, Priyansh; Lehmann, Jens; Fischer, Asja

Introduction to neural network-based question answering over knowledge graphs Journal Article

In: WIREs Data Mining Knowl. Discov., vol. 11, no. 3, 2021.

Links | BibTeX

Ding, Linfang; Xiao, Guohui; Pano, Albulen; Stadler, Claus; Calvanese, Diego

Towards the next generation of the LinkedGeoData project using virtual knowledge graphs Journal Article

In: J. Web Semant., vol. 71, pp. 100662, 2021.

Links | BibTeX

Mami, Mohamed Nadjib

Strategies for a Semantified Uniform Access to Large and Heterogeneous Data Sources PhD Thesis

University of Bonn, Germany, 2021.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Mami21,

title = {Strategies for a Semantified Uniform Access to Large and Heterogeneous

Data Sources},

author = {Mohamed Nadjib Mami},

url = {https://hdl.handle.net/20.500.11811/8925},

year = {2021},

date = {2021-01-01},

school = {University of Bonn, Germany},

abstract = {The remarkable advances achieved in both research and development of Data Management as well as the prevalence of high-speed Internet and technology in the last few decades have caused unprecedented data avalanche. Large volumes of data manifested in a multitude of types and formats are being generated and becoming the new norm. In this context, it is crucial to both leverage existing approaches and propose novel ones to overcome this data size and complexity, and thus facilitate data exploitation. In this thesis, we investigate two major approaches to addressing this challenge: Physical Data Integration and Logical Data Integration. The specific problem tackled is to enable querying large and heterogeneous data sources in an ad hoc manner.

In the Physical Data Integration, data is physically and wholly transformed into a canonical unique format, which can then be directly and uniformly queried. In the Logical Data Integration, data remains in its original format and form and a middleware is posed above the data allowing to map various schemata elements to a high-level unifying formal model. The latter enables the querying of the underlying original data in an ad hoc and uniform way, a framework which we call Semantic Data Lake, SDL. Both approaches have their advantages and disadvantages. For example, in the former, a significant effort and cost are devoted to pre-processing and transforming the data to the unified canonical format. In the latter, the cost is shifted to the query processing phases, e.g., query analysis, relevant source detection and results reconciliation.

In this thesis we investigate both directions and study their strengths and weaknesses. For each direction, we propose a set of approaches and demonstrate their feasibility via a proposed implementation. In both directions, we appeal to Semantic Web technologies, which provide a set of time-proven techniques and standards that are dedicated to Data Integration. In the Physical Integration, we suggest an end-to-end blueprint for the semantification of large and heterogeneous data sources, i.e., physically transforming the data to the Semantic Web data standard RDF (Resource Description Framework). A unified data representation, storage and query interface over the data are suggested. In the Logical Integration, we provide a description of the SDL architecture, which allows querying data sources right on their original form and format without requiring a prior transformation and centralization. For a number of reasons that we detail, we put more emphasis on the virtual approach. We present the effort behind an extensible implementation of the SDL, called Squerall, which leverages state-of-the-art Semantic and Big Data technologies, e.g., RML (RDF Mapping Language) mappings, FnO (Function Ontology) ontology, and Apache Spark. A series of evaluation is conducted to evaluate the implementation along with various metrics and input data scales. In particular, we describe an industrial real-world use case using our SDL implementation. In a preparation phase, we conduct a survey for the Query Translation methods in order to back some of our design choices.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

The remarkable advances achieved in both research and development of Data Management as well as the prevalence of high-speed Internet and technology in the last few decades have caused unprecedented data avalanche. Large volumes of data manifested in a multitude of types and formats are being generated and becoming the new norm. In this context, it is crucial to both leverage existing approaches and propose novel ones to overcome this data size and complexity, and thus facilitate data exploitation. In this thesis, we investigate two major approaches to addressing this challenge: Physical Data Integration and Logical Data Integration. The specific problem tackled is to enable querying large and heterogeneous data sources in an ad hoc manner.
In the Physical Data Integration, data is physically and wholly transformed into a canonical unique format, which can then be directly and uniformly queried. In the Logical Data Integration, data remains in its original format and form and a middleware is posed above the data allowing to map various schemata elements to a high-level unifying formal model. The latter enables the querying of the underlying original data in an ad hoc and uniform way, a framework which we call Semantic Data Lake, SDL. Both approaches have their advantages and disadvantages. For example, in the former, a significant effort and cost are devoted to pre-processing and transforming the data to the unified canonical format. In the latter, the cost is shifted to the query processing phases, e.g., query analysis, relevant source detection and results reconciliation.
In this thesis we investigate both directions and study their strengths and weaknesses. For each direction, we propose a set of approaches and demonstrate their feasibility via a proposed implementation. In both directions, we appeal to Semantic Web technologies, which provide a set of time-proven techniques and standards that are dedicated to Data Integration. In the Physical Integration, we suggest an end-to-end blueprint for the semantification of large and heterogeneous data sources, i.e., physically transforming the data to the Semantic Web data standard RDF (Resource Description Framework). A unified data representation, storage and query interface over the data are suggested. In the Logical Integration, we provide a description of the SDL architecture, which allows querying data sources right on their original form and format without requiring a prior transformation and centralization. For a number of reasons that we detail, we put more emphasis on the virtual approach. We present the effort behind an extensible implementation of the SDL, called Squerall, which leverages state-of-the-art Semantic and Big Data technologies, e.g., RML (RDF Mapping Language) mappings, FnO (Function Ontology) ontology, and Apache Spark. A series of evaluation is conducted to evaluate the implementation along with various metrics and input data scales. In particular, we describe an industrial real-world use case using our SDL implementation. In a preparation phase, we conduct a survey for the Query Translation methods in order to back some of our design choices.

Mulang', Isaiah Onando

Knowledge Context for Entity and Relation Linking PhD Thesis

University of Bonn, Germany, 2021.

Links | BibTeX

Thakker, Harsh Vrajeshkumar

On Supporting Interoperability between RDF and Property Graph Databases PhD Thesis

University of Bonn, Germany, 2021.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Thakker21,

title = {On Supporting Interoperability between RDF and Property Graph Databases},

author = {Harsh Vrajeshkumar Thakker},

url = {https://hdl.handle.net/20.500.11811/9083},

year = {2021},

date = {2021-01-01},

school = {University of Bonn, Germany},

abstract = {Over the last few years, the amount and availability of machine-readable Open, Linked, and Big data on the web has increased. Simultaneously, several data management systems have emerged to deal with the increased amounts of this structured data. RDF and Graph databases are two popular approaches for data management based on modeling, storing, and querying graph-like data. RDF database systems are based on the W3C standard RDF data model and use the W3C standard SPARQL as their defacto query language. Most graph database systems are based on the Property Graph (PG) data model and use the Gremlin language as their query language due to its popularity amongst vendors. Given that both of these approaches have distinct and complementary characteristics – RDF is suited for distributed data integration with built-in world-wide unique identifiers and vocabularies; PGs, on the other hand, support horizontally scalable storage and querying, and are widely used for modern data analytics applications, – it becomes necessary to support interoperability amongst them. The main objective of this dissertation is to study and address this interoperability issue. We identified three research challenges that are concerned with the data interoperability, query interoperability, and benchmarking of these databases. First, we tackle the data interoperability problem. We propose three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database. We show that the proposed mappings satisfy the desired properties of semantics preservation and information preservation. Based on our analysis (both formal and empirical), we argue that any RDF database can be transformed into a PG database using our approach. Second, we propose a novel approach for querying PG databases using SPARQL using Gremlin traversals – GREMLINATOR to tackle the query interoperability problem. In doing so, we first formalize the declarative constructs of Gremlin language using a consolidated graph relational algebra and define mappings to translate SPARQL queries into Gremlin traversals. GREMLINATOR has been officially integrated as a plugin for the Apache TinkerPop graph computing framework (as sparql-gremlin), which enables users to execute SPARQL queries over a wide variety of OLTP graph databases and OLAP graph processing frameworks. Finally, we tackle the third, benchmarking (performance evaluation), problem. We propose a novel framework – LITMUS Benchmark Suite that allows a choke-point driven performance comparison and analysis of various databases (PG and RDF-based) using various third-party real and synthetic datasets and queries. We also studied a variety of intrinsic and extrinsic factors – data and system-specific metrics and Key Performance Indicators (KPIs) that influence a given system’s performance. LITMUS incorporates various memory, processor, data quality, indexing, query typology, and data-based metrics for providing a fine-grained evaluation of the benchmark. In conclusion, by filling the research gaps, addressed by this dissertation, we have laid a solid formal and practical foundation for supporting interoperability between the RDF and Property graph database technology stacks. The artifacts produced during the term of this dissertation have been integrated into various academic and industrial projects.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Over the last few years, the amount and availability of machine-readable Open, Linked, and Big data on the web has increased. Simultaneously, several data management systems have emerged to deal with the increased amounts of this structured data. RDF and Graph databases are two popular approaches for data management based on modeling, storing, and querying graph-like data. RDF database systems are based on the W3C standard RDF data model and use the W3C standard SPARQL as their defacto query language. Most graph database systems are based on the Property Graph (PG) data model and use the Gremlin language as their query language due to its popularity amongst vendors. Given that both of these approaches have distinct and complementary characteristics – RDF is suited for distributed data integration with built-in world-wide unique identifiers and vocabularies; PGs, on the other hand, support horizontally scalable storage and querying, and are widely used for modern data analytics applications, – it becomes necessary to support interoperability amongst them. The main objective of this dissertation is to study and address this interoperability issue. We identified three research challenges that are concerned with the data interoperability, query interoperability, and benchmarking of these databases. First, we tackle the data interoperability problem. We propose three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database. We show that the proposed mappings satisfy the desired properties of semantics preservation and information preservation. Based on our analysis (both formal and empirical), we argue that any RDF database can be transformed into a PG database using our approach. Second, we propose a novel approach for querying PG databases using SPARQL using Gremlin traversals – GREMLINATOR to tackle the query interoperability problem. In doing so, we first formalize the declarative constructs of Gremlin language using a consolidated graph relational algebra and define mappings to translate SPARQL queries into Gremlin traversals. GREMLINATOR has been officially integrated as a plugin for the Apache TinkerPop graph computing framework (as sparql-gremlin), which enables users to execute SPARQL queries over a wide variety of OLTP graph databases and OLAP graph processing frameworks. Finally, we tackle the third, benchmarking (performance evaluation), problem. We propose a novel framework – LITMUS Benchmark Suite that allows a choke-point driven performance comparison and analysis of various databases (PG and RDF-based) using various third-party real and synthetic datasets and queries. We also studied a variety of intrinsic and extrinsic factors – data and system-specific metrics and Key Performance Indicators (KPIs) that influence a given system’s performance. LITMUS incorporates various memory, processor, data quality, indexing, query typology, and data-based metrics for providing a fine-grained evaluation of the benchmark. In conclusion, by filling the research gaps, addressed by this dissertation, we have laid a solid formal and practical foundation for supporting interoperability between the RDF and Property graph database technology stacks. The artifacts produced during the term of this dissertation have been integrated into various academic and industrial projects.

2020

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

An OER Recommender System Supporting Accessibility Requirements Inproceedings

In: ASSETS '20: The 22nd International ACM SIGACCESS Conference on Computers and Accessibility, Virtual Event, Greece, October 26-28, 2020, pp. 57:1–57:4, ACM, 2020.

Links | BibTeX

Jabeen, Hajira; Weinz, Jonas; Lehmann, Jens

AutoChef: Automated Generation of Cooking Recipes Inproceedings

In: IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, United Kingdom, July 19-24, 2020, pp. 1–7, IEEE, 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

In: 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

In: 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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Singh, Isaiah Onando Mulang'and Kuldeep; Prabhu, Chaitali; Nadgeri, Abhishek; Hoffart, Johannes; Lehmann, Jens

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

In: 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

In: 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

In: 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

In: 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

In: 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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Sibarani, Elisa Margareth; Scerri, Simon

SCODIS: Job Advert-Derived Time Series for High-Demand Skillset Discovery and Prediction Inproceedings

In: Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Bratislava, Slovakia, September 14-17, 2020, Proceedings, Part II, pp. 366–381, Springer, 2020.

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Alam, Mirza Mohtashim; Nayyeri, Mojtaba; Xu, Chengjin; Rony, Md. Rashad Al Hasan; Yazdi, Hamed Shariat; Sadeghi, Afshin; Lehmann, Jens

The Effect of Rule Injectionin a Leakage Free Dataset Inproceedings

In: Proceedings of the International Workshop on Knowledge Representation and Representation Learning co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020), Virtual Event, September, 2020, pp. 28–35, CEUR-WS.org, 2020.

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Khvalchik, Maria; Galkin, Mikhail

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

In: 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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Sadeghi, Afshin; Graux, Damien; Yazdi, Hamed Shariat; Lehmann, Jens

MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs Inproceedings

In: ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020), pp. 1427–1434, IOS Press, 2020.

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Xu, Chengjin; Nayyeri, Mojtaba; Vahdati, Sahar; Lehmann, Jens

Multiple Run Ensemble Learning with Low Dimensional Knowledge Graph Embeddings Inproceedings

Links | BibTeX

Zafar, Hamid; Tavakol, Maryam; Lehmann, Jens

Distantly Supervised Question Parsing Inproceedings

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

Quality Evaluation of Open Educational Resources Inproceedings

In: Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14-18, 2020, Proceedings, pp. 410–415, Springer, 2020.

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

Message Passing for Hyper-Relational Knowledge Graphs Inproceedings

In: 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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Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören

Ontology Design for Pharmaceutical Research Outcomes Inproceedings

In: Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings, pp. 119–132, Springer, 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

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

Training Multimodal Systems for Classification with Multiple Objectives Inproceedings

In: 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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Bader, Sebastian R.; Grangel-González, Irlán; Nanjappa, Priyanka; Vidal, Maria-Esther; Maleshkova, Maria

A Knowledge Graph for Industry 4.0 Inproceedings

In: The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings, pp. 465–480, Springer, 2020.

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Bader, Sebastian R.; Maleshkova, Maria

Towards Integrated Data Control for Digital Twins in Industry 4.0 Inproceedings

In: 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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Chau, Minh Triet; Esteves, Diego; Lehmann, Jens

A Neural-based model to Predict the Future Natural Gas Market Price through Open-domain Event Extraction Inproceedings

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Kacupaj, Endri; Zafar, Hamid; Lehmann, Jens; Maleshkova, Maria

VQuAnDa: Verbalization QUestion ANswering DAtaset Inproceedings

In: The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings, pp. 531–547, Springer, 2020.

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Mami, Mohamed Nadjib; Grangel-González, Irlán; Graux, Damien; Elezi, Enkeleda; Lösch, Felix

Semantic Data Integration for the SMT Manufacturing Process Using SANSA Stack Inproceedings

In: The Semantic Web: ESWC 2020 Satellite Events - ESWC 2020 Satellite Events, Heraklion, Crete, Greece, May 31 - June 4, 2020, Revised Selected Papers, pp. 307–311, Springer, 2020.

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Nayyeri, Mojtaba; Vahdati, Sahar; Zhou, Xiaotian; Yazdi, Hamed Shariat; Lehmann, Jens

Embedding-Based Recommendations on Scholarly Knowledge Graphs Inproceedings

In: The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings, pp. 255–270, Springer, 2020.

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Elahi, Mehdi; Hosseini, Reza; Rimaz, Mohammad Hossein; Moghaddam, Farshad Bakhshandegan; Trattner, Christoph

Visually-Aware Video Recommendation in the Cold Start Inproceedings

In: HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020, pp. 225–229, ACM, 2020.

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