Publications

You can find a list of completed Phd theses here.

538 entries « ‹ 4 of 11 › »

2020

Kiela, Douwe; Firooz, Hamed; Mohan, Aravind; Goswami, Vedanuj; Singh, Amanpreet; Fitzpatrick, Casey A.; Bull, Peter; Lipstein, Greg; Nelli, Tony; Zhu, Ron; Muennighoff, Niklas; Velioglu, Riza; Rose, Jewgeni; Lippe, Phillip; Holla, Nithin; Chandra, Shantanu; Rajamanickam, Santhosh; Antoniou, Georgios; Shutova, Ekaterina; Yannakoudakis, Helen; Sandulescu, Vlad; Ozertem, Umut; Pantel, Patrick; Specia, Lucia; Parikh, Devi

The Hateful Memes Challenge: Competition Report Proceedings Article

In: NeurIPS 2020 Competition and Demonstration Track, 6-12 December 2020, Virtual Event / Vancouver, BC, Canada, pp. 344–360, PMLR, 2020.

Reimann, Lars; Kniesel-Wünsche, Günter

Achieving guidance in applied machine learning through software engineering techniques Proceedings Article

In: Programming'20: 4th International Conference on the Art, Science, and Engineering of Programming, Porto, Portugal, March 23-26, 2020, pp. 7–12, ACM, 2020.

Fathalla, Said; Auer, Sören; Lange, Christoph

Towards the semantic formalization of science Proceedings Article

In: SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30 - April 3, 2020, pp. 2057–2059, ACM, 2020.

Nejad, Najmeh Mousavi; Jabat, Pablo; Nedelchev, Rostislav; Scerri, Simon; Graux, Damien

Establishing a Strong Baseline for Privacy Policy Classification Proceedings Article

In: ICT Systems Security and Privacy Protection - 35th IFIP TC 11 International Conference, SEC 2020, Maribor, Slovenia, September 21-23, 2020, Proceedings, pp. 370–383, Springer, 2020.

Jabeen, Hajira; Haziiev, Eskender; Sejdiu, Gezim; Lehmann, Jens

DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data Proceedings Article

In: IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 400–407, IEEE, 2020.

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

VoColReg: A Registry for Supporting Distributed Ontology Development using Version Control Systems Proceedings Article

In: IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 393–399, IEEE, 2020.

Thakkar, Harsh; Angles, Renzo; Rodriguez, Marko; Mallette, Stephen; Lehmann, Jens

Let's build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin Proceedings Article

In: IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 408–415, IEEE, 2020.

Arispe, Martin; Tasnim, Mayesha; Graux, Damien; Orlandi, Fabrizio; Collarana, Diego

Verbalizing the Evolution of Knowledge Graphs with Formal Concept Analysis Proceedings Article

In: Joint Proceedings of Workshops AI4LEGAL2020, NLIWOD, PROFILES 2020, QuWeDa 2020 and SEMIFORM2020 Colocated with the 19th International Semantic Web Conference (ISWC 2020), Virtual Conference, November, 2020, pp. 72–79, CEUR-WS.org, 2020.

Bader, Sebastian R.; Pullmann, Jaroslav; Mader, Christian; Tramp, Sebastian; Quix, Christoph; Müller, Andreas W.; Akyürek, Haydar; Böckmann, Matthias; Imbusch, Benedikt T.; Lipp, Johannes; Geisler, Sandra; Lange, Christoph

The International Data Spaces Information Model - An Ontology for Sovereign Exchange of Digital Content Proceedings Article

In: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part II, pp. 176–192, Springer, 2020.

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

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

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

Nayyeri, Mojtaba; Xu, Chengjin; Vahdati, Sahar; Vassilyeva, Nadezhda; Sallinger, Emanuel; Yazdi, Hamed Shariat; Lehmann, Jens

Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them Proceedings Article

In: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 438–455, Springer, 2020.

Rose, Jewgeni; Lehmann, Jens

CASQAD - A New Dataset for Context-Aware Spatial Question Answering Proceedings Article

In: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part II, pp. 3–17, Springer, 2020.

Wiens, Vitalis; Lohmann, Steffen

Demonstration of a Customizable Knowledge Graph Visualization Framework Proceedings Article

In: Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 19th International Semantic Web Conference (ISWC 2020), Globally online, November 1-6, 2020 (UTC), pp. 104–109, CEUR-WS.org, 2020.

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

Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding Proceedings Article

In: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 654–671, Springer, 2020.

D'Souza, Isaiah Onando Mulang'and Jennifer; Auer, Sören

Fine-tuning BERT with Focus Words for Explanation Regeneration Proceedings Article

In: Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, * SEM@COLING 2020, Barcelona, Spain (Online), December 12-13, 2020, pp. 125–130, Association for Computational Linguistics, 2020.

Ibrahim, Shimaa; Fathalla, Said; Lehmann, Jens; Jabeen, Hajira

Multilingual Ontology Merging Using Cross-lingual Matching Proceedings Article

In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI/IAT 2020, Melbourne, Australia, December 14-17, 2020, pp. 113–120, IEEE, 2020.

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

OWLStats: Distributed Computation of OWL Dataset Statistics Proceedings Article

In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI/IAT 2020, Melbourne, Australia, December 14-17, 2020, pp. 381–386, IEEE, 2020.

Singh, Isaiah Onando Mulang'and Kuldeep; Vyas, Akhilesh; Shekarpour, Saeedeh; Vidal, Maria-Esther; Auer, Sören

Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking Proceedings Article

In: Web Information Systems Engineering - WISE 2020 - 21st International Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings, Part I, pp. 328–342, Springer, 2020.

Grubenmann, Tobias; Cheng, Reynold C. K.; Lakshmanan, Laks V. S.

TSA: A Truthful Mechanism for Social Advertising Proceedings Article

In: WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, pp. 214–222, ACM, 2020.

Angles, Renzo; Thakkar, Harsh; Tomaszuk, Dominik

Mapping RDF Databases to Property Graph Databases Journal Article

In: IEEE Access, vol. 8, pp. 86091–86110, 2020.

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

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

In: IEEE Access, vol. 8, pp. 196459–196471, 2020.

Tomaszuk, Dominik; Angles, Renzo; Thakkar, Harsh

PGO: Describing Property Graphs in RDF Journal Article

In: IEEE Access, vol. 8, pp. 118355–118369, 2020.

Musyaffa, Fathoni A.; Vidal, Maria-Esther; Orlandi, Fabrizio; Lehmann, Jens; Jabeen, Hajira

IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA Journal Article

In: Expert Syst. Appl., vol. 147, pp. 113135, 2020.

Cremaschi, Marco; Paoli, Flavio De; Rula, Anisa; Spahiu, Blerina

A fully automated approach to a complete Semantic Table Interpretation Journal Article

In: Future Gener. Comput. Syst., vol. 112, pp. 478–500, 2020.

Bader, Sebastian R.; Maleshkova, Maria

SOLIOT - Decentralized Data Control and Interactions for IoT Journal Article

In: Future Internet, vol. 12, no. 6, pp. 105, 2020.

Rula, Anisa; Zaveri, Amrapali; Simperl, Elena; Demidova, Elena

Editorial: Special Issue on Quality Assessment of Knowledge Graphs Dedicated to the Memory of Amrapali Zaveri Journal Article

In: ACM J. Data Inf. Qual., vol. 12, no. 2, pp. 7:1–7:4, 2020.

Fathalla, Said; Vahdati, Sahar; Lange, Christoph; Auer, Sören

Scholarly event characteristics in four fields of science: a metrics-based analysis Journal Article

In: Scientometrics, vol. 123, no. 2, pp. 677–705, 2020.

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

In: IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 4, pp. 1218–1229, 2020.

Singh, Kuldeep; Lytra, Ioanna; Radhakrishna, Arun Sethupat; Shekarpour, Saeedeh; Vidal, Maria-Esther; Lehmann, Jens

No one is perfect: Analysing the performance of question answering components over the DBpedia knowledge graph Journal Article

In: J. Web Semant., vol. 65, pp. 100594, 2020.

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

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

In: J. Web Semant., vol. 65, pp. 100614, 2020.

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

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

In: J. Web Semant., vol. 64, pp. 100586, 2020.

Höffner, Konrad

Question Answering on RDF Data Cubes PhD Thesis

Leipzig University, Germany, 2020.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Hoffner21,

title = {Question Answering on RDF Data Cubes},

author = {Konrad Höffner},

url = {https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-742429},

year  = {2020},

date = {2020-01-01},

school = {Leipzig University, Germany},

abstract = {The Semantic Web, a Web of Data, is an extension of the World Wide Web (WWW), a Web of Documents. A large amount of such data is freely available as Linked Open Data (LOD) for many areas of knowledge, forming the LOD Cloud. While this data conforms to the Resource Description Framework (RDF) and can thus be processed by machines, users need to master a formal query language and learn a specific vocabulary. Semantic Question Answering (SQA) systems remove those access barriers by letting the user ask natural language questions that the systems translate into formal queries. Thus, the research area of SQA plays an important role for the acceptance and benefit of the Semantic Web. The original contributions of this thesis to SQA are: First, we survey the current state of the art of SQA. We complement existing surveys by systematically identifying SQA publications in the chosen timeframe. 72 publications describing 62 different systems are systematically and manually selected using predefined inclusion and exclusion criteria out of 1960 candidates from the end of 2010 to July 2015. The survey identifies common challenges, structured solutions, and recommendations on research opportunities for future systems. From that point on, we focus on multidimensional numerical data, which is immensely valuable as it influences decisions in health care, policy and finance, among others. With the growth of the open data movement, more and more of it is becoming freely available. A large amount of such data is included in the LOD cloud using the RDF Data Cube (RDC) vocabulary. However, consuming multidimensional numerical data requires experts and specialized tools. Traditional SQA systems cannot process RDCs because their meta-structure is opaque to applications that expect facts to be encoded in single triples, This motivates our second contribution, the design and implementation of the first SQA algorithm on RDF Data Cubes. We kick-start this new research subfield by creating a user question corpus and a benchmark over multiple data sets. The evaluation of our system on the benchmark, which is included in the public Question Answering over Linked Data (QALD) challenge of 2016, shows the feasibility of the approach, but also highlights challenges, which we discuss in detail as a starting point for future work in the field. The benchmark is based on our final contribution, the addition of 955 financial government spending data sets to the LOD cloud by transforming data sets of the OpenSpending project to RDF Data Cubes. Open spending data has the power to reduce corruption by increasing accountability and strengthens democracy because voters can make better informed decisions. An informed and trusting public also strengthens the government itself because it is more likely to commit to large projects. OpenSpending.org is an open platform that provides public finance data from governments around the world. The transformation result, called Linked Spending, consists of more than five million planned and carried out financial transactions in 955 data sets from all over the world as Linked Open Data and is freely available and openly licensed.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

The Semantic Web, a Web of Data, is an extension of the World Wide Web (WWW), a Web of Documents. A large amount of such data is freely available as Linked Open Data (LOD) for many areas of knowledge, forming the LOD Cloud. While this data conforms to the Resource Description Framework (RDF) and can thus be processed by machines, users need to master a formal query language and learn a specific vocabulary. Semantic Question Answering (SQA) systems remove those access barriers by letting the user ask natural language questions that the systems translate into formal queries. Thus, the research area of SQA plays an important role for the acceptance and benefit of the Semantic Web. The original contributions of this thesis to SQA are: First, we survey the current state of the art of SQA. We complement existing surveys by systematically identifying SQA publications in the chosen timeframe. 72 publications describing 62 different systems are systematically and manually selected using predefined inclusion and exclusion criteria out of 1960 candidates from the end of 2010 to July 2015. The survey identifies common challenges, structured solutions, and recommendations on research opportunities for future systems. From that point on, we focus on multidimensional numerical data, which is immensely valuable as it influences decisions in health care, policy and finance, among others. With the growth of the open data movement, more and more of it is becoming freely available. A large amount of such data is included in the LOD cloud using the RDF Data Cube (RDC) vocabulary. However, consuming multidimensional numerical data requires experts and specialized tools. Traditional SQA systems cannot process RDCs because their meta-structure is opaque to applications that expect facts to be encoded in single triples, This motivates our second contribution, the design and implementation of the first SQA algorithm on RDF Data Cubes. We kick-start this new research subfield by creating a user question corpus and a benchmark over multiple data sets. The evaluation of our system on the benchmark, which is included in the public Question Answering over Linked Data (QALD) challenge of 2016, shows the feasibility of the approach, but also highlights challenges, which we discuss in detail as a starting point for future work in the field. The benchmark is based on our final contribution, the addition of 955 financial government spending data sets to the LOD cloud by transforming data sets of the OpenSpending project to RDF Data Cubes. Open spending data has the power to reduce corruption by increasing accountability and strengthens democracy because voters can make better informed decisions. An informed and trusting public also strengthens the government itself because it is more likely to commit to large projects. OpenSpending.org is an open platform that provides public finance data from governments around the world. The transformation result, called Linked Spending, consists of more than five million planned and carried out financial transactions in 955 data sets from all over the world as Linked Open Data and is freely available and openly licensed.

Sejdiu, Gezim

Efficient Distributed In-Memory Processing of RDF Datasets PhD Thesis

University of Bonn, Germany, 2020.

Abstract | Links | BibTeX

@phdthesis{DBLP:phd/dnb/Sejdiu20,

title = {Efficient Distributed In-Memory Processing of RDF Datasets},

author = {Gezim Sejdiu},

url = {https://hdl.handle.net/20.500.11811/8735},

year = {2020},

date = {2020-01-01},

school = {University of Bonn, Germany},

abstract = {Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework.

Jabeen, Hajira

Big Data Outlook, Tools, and Architectures Book Section

In: Knowledge Graphs and Big Data Processing, vol. 12072, pp. 35–55, Springer, 2020.

Jabeen, Hajira; Graux, Damien; Sejdiu, Gezim

Scalable Knowledge Graph Processing Using SANSA Book Section

In: Knowledge Graphs and Big Data Processing, vol. 12072, pp. 105–121, Springer, 2020.

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

Context-Based Entity Matching for Big Data Book Section

In: Knowledge Graphs and Big Data Processing, vol. 12072, pp. 122–146, Springer, 2020.

Zafartavanaelmi, Hamid

Semantic Question Answering Over Knowledge Graphs: Pitfalls and Pearls PhD Thesis

University of Bonn, 2020.

Abstract | Links | BibTeX

2019

Frey, Johannes; Hofer, Marvin; Obraczka, Daniel; Lehmann, Jens; Hellmann, Sebastian

DBpedia FlexiFusion the Best of Wikipedia textbackslashtextgreater Wikidata textbackslashtextgreater Your Data Proceedings Article

In: The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part II, pp. 96–112, Springer, 2019.

Kubitza, Dennis Oliver; Böckmann, Matthias; Graux, Damien

Towards Semantically Structuring GittextbackslashfontencodingLELAtextbackslashselectfontḩar40ub Proceedings Article

In: Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 26-30, 2019, pp. 141–144, CEUR-WS.org, 2019.

González-Beltrán, Alejandra N.; Osborne, Francesco; Peroni, Silvio; Vahdati, Sahar

Editorial: Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation) Journal Article

In: Data Sci., vol. 2, no. 1-2, pp. 177–179, 2019.

Angles, Renzo; Thakkar, Harsh; Tomaszuk, Dominik

RDF and Property Graphs Interoperability: Status and Issues Proceedings Article

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

Barella, Victor H.; Garcia, Lu'is Paulo F.; de Carvalho, André C. P. L. F.

The Influence of Sampling on Imbalanced Data Classification Proceedings Article

In: 8th Brazilian Conference on Intelligent Systems, BRACIS 2019, Salvador, Brazil, October 15-18, 2019, pp. 210–215, IEEE, 2019.

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 Proceedings Article

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

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

COMET: A Contextualized Molecule-Based Matching Technique Proceedings Article

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

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 Proceedings Article

In: 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.

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

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

In: 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.

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

SimpleLSTM: A Deep-Learning Approach to Simple-Claims Classification Proceedings Article

In: 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.

Fathalla, Said; Lange, Christoph; Auer, Sören

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

In: 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.

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

Semantic Representation of Scientific Publications Proceedings Article

In: 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. 375–379, Springer, 2019.

Bader, Sebastian R.; Maleshkova, Maria

Towards Enforceable Usage Policies for Industry 4.0 Proceedings Article

In: 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.

538 entries « ‹ 4 of 11 › »