publications

Bibtex file with the publications listed below.

2020

1. Dataset search: a survey Chapman, Adriane, Simperl, Elena, Koesten, Laura, Konstantinidis, George, Ibáñez, Luis-Daniel, Kacprzak, Emilia, and Groth, Paul The VLDB Journal 2020 [arXiv] [Link] [DOI:10.1007/s00778-019-00564-x]
2. Introduction – FAIR data, systems and analysis Groth, Paul, and Dumontier, Michel Data Science 2020 [Link] [DOI:10.3233/DS-200029]
3. A Longitudinal Analysis of University Rankings Selten, Friso, Neylon, Cameron, Huang, Chun-Kai, and Groth, Paul Quantitative Science Studies 2020 [Link] [DOI:10.1162/qss_a_00052]
4. Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning Yang, Ke, Huang, Biao, Stoyanovich, Julia, and Schelter, Sebastian In Workshop on Human-In-the-Loop Data Analytics (HILDA’20) 2020 [Link] [DOI:10.1145/3398730.3399194]
5. Towards Entity Spaces Erp, Marieke, and Groth, Paul In Proceedings of The 12th Language Resources and Evaluation Conference 2020 [Abs] [Link]

   Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis. For example, they allow us to find documents relevant to a specific entity irrespective of the underlying syntactic expression within a document. However, the entities that are commonly represented in knowledge bases are often a simplification of what is truly being referred to in text. For example, in a knowledge base, we may have an entity for Germany as a country but not for the more fuzzy concept of Germany that covers notions of German Population, German Drivers, and the German Government. Inspired by recent advances in contextual word embeddings, we introduce the concept of entity spaces - specific representations of a set of associated entities with near-identity. Thus, these entity spaces provide a handle to an amorphous grouping of entities. We developed a proof-of-concept for English showing how, through the introduction of entity spaces in the form of disambiguation pages, the recall of entity linking can be improved.

6. PANDAcap: A Framework for Streamlining Collection of Full-System Traces Stamatogiannakis, Manolis, Bos, Herbert, and Groth, Paul In EuroSec 2020 [Link] [DOI:10.1145/3380786.3391396] [Code]
7. Lost or Found? Discovering Data Needed for Research Gregory, Kathleen, Groth, Paul, Scharnhorst, Andrea, and Wyatt, Sally Harvard Data Science Review 2020 [Link] [DOI:10.1162/99608f92.e38165eb]
8. Estimating the imageability of words by mining visual characteristics from crawled image data Kastner, Marc A., Ide, Ichiro, Nack, Frank, Kawanishi, Yasutomo, Hirayama, Takatsugu, Deguchi, Daisuke, and Murase, Hiroshi Multimedia Tools and Applications 2020 [Link] [DOI:10.1007/s11042-019-08571-4]
9. FAIR Data Reuse – the Path through Data Citation Groth, Paul, Cousijn, Helena, Clark, Tim, and Goble, Carole Data Intelligence 2020 [Link] [DOI:10.1162/dint_a_00030]
10. Message Passing Query Embedding Daza, Daniel, and Cochez, Michael In ICML Workshop - Graph Representation Learning and Beyond 2020 [arXiv] [Link]

2019

1. How Relevant Is Your Choice? Kolhoff, Lobke, and Nack, Frank In ICIDS 2019. Lecture Notes in Computer Science, vol 11869 2019 [Abs]

   With the release of the film Black Mirror: Bandersnatch Netflix entered the area of interactive streamed narratives. We performed a qualitative analysis with 169 Netflix subscribers that had watched the episode. The key findings show (1) participants are initially engaged because of curiosity and the novelty value, and desire to explore the narrative regardless of satisfaction, (2) perceived agency is limited due to arbitrary choices and the lack of meaningful consequences, (3) the overall experience is satisfactory but adaptions are desirable in future design to make full use of the potential of the format.

2. Transfer Learning for Biomedical Named Entity Recognition with BioBERT Symeonidou, Anthi, Sazonau, Viachaslau, and Groth, Paul In 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. 2019 [Link]
3. Understanding data search as a socio-technical practice Gregory, Kathleen M, Cousijn, Helena, Groth, Paul, Scharnhorst, Andrea, and Wyatt, Sally Journal of Information Science 2019 [Abs] [Link] [DOI:10.1177/0165551519837182]

   Open research data are heralded as having the potential to increase effectiveness, productivity and reproducibility in science, but little is known about the actual practices involved in data search. The socio-technical problem of locating data for reuse is often reduced to the technological dimension of designing data search systems. We combine a bibliometric study of the current academic discourse around data search with interviews with data seekers. In this article, we explore how adopting a contextual, socio-technical perspective can help to understand user practices and behaviour and ultimately help to improve the design of data discovery systems.

4. Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines Gregory, Kathleen, Groth, Paul, Cousijn, Helena, Scharnhorst, Andrea, and Wyatt, Sally Journal of the Association for Information Science and Technology 2019 [Abs] [Link] [DOI:10.1002/asi.24165]

   A cross-disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

5. End-to-End Learning for Answering Structured Queries Directly over Text Groth, Paul T., Scerri, Antony, Daniel, Ron, and Allen, Bradley P. In Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2019) Co-located with the 16th Extended Semantic Web Conference 2019 (ESWC 2019), Portoroz, Slovenia, June 2, 2019. 2019 [arXiv] [Link]

2018

1. Open Information Extraction on Scientific Text: An Evaluation Groth, Paul T., Lauruhn, Michael, Scerri, Antony, and Daniel, Ron In Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018 2018 [Link]
2. Elsevier’s Healthcare Knowledge Graph and the Case for Enterprise Level Linked Data Standards DeJong, Alex, Bord, Radmila, Dowling, Will, Hoekstra, Rinke, Moquin, Ryan, O, Charlie, Samarasinghe, Mevan, Snyder, Paul, Stanley, Craig, Tordai, Anna, Trefry, Michael, and Groth, Paul T. In Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th - to - 12th, 2018. 2018 [Link]
3. Use of Internal Testing Data to Help Determine Compensation for Crowdsourcing Tasks Lauruhn, Michael, Groth, Paul T., Harper, Corey A., and Deus, Helena F. In Proceedings of the 2nd International Workshop on Augmenting Intelligence with Humans\--in-\-the-\-Loop co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, California, October 9th, 2018. 2018 [Link]

2017

1. Storing, Tracking, and Querying Provenance in Linked Data Wylot, Marcin, Cudré-Mauroux, Philippe, Hauswirth, Manfred, and Groth, Paul T. IEEE Trans. Knowl. Data Eng. 2017 [Link] [DOI:10.1109/TKDE.2017.2690299]
2. PROV2R: Practical Provenance Analysis of Unstructured Processes Stamatogiannakis, Manolis, Athanasopoulos, Elias, Bos, Herbert, and Groth, Paul T. ACM Trans. Internet Techn. 2017 [Link] [DOI:10.1145/3062176]
3. Indicators for the use of robotic labs in basic biomedical research: a literature analysis Groth, Paul, and Cox, Jessica PeerJ 2017 [Abs] [Link] [DOI:10.7717/peerj.3997]

   Robotic labs, in which experiments are carried out entirely by robots, have the potential to provide a reproducible and transparent foundation for performing basic biomedical laboratory experiments. In this article, we investigate whether these labs could be applicable in current experimental practice. We do this by text mining 1,628 papers for occurrences of methods that are supported by commercial robotic labs. Using two different concept recognition tools, we find that 86%–89% of the papers have at least one of these methods. This and our other results provide indications that robotic labs can serve as the foundation for performing many lab-based experiments.

4. Linked Data Management Hauswirth, Manfred, Wylot, Marcin, Grund, Martin, Groth, Paul T., and Cudré-Mauroux, Philippe 2017 [Link] [DOI:10.1007/978-3-319-49340-4_9]

2016

1. Applying Universal Schemas for Domain Specific Ontology Expansion Groth, Paul T., Pal, Sujit, McBeath, Darin, Allen, Brad, and Daniel, Ron In Proceedings of the 5th Workshop on Automated Knowledge Base Construction, AKBC@NAACL-HLT 2016, San Diego, CA, USA, June 17, 2016 2016 [Link]
2. The FAIR Guiding Principles for scientific data management and stewardship Wilkinson, Mark D., Dumontier, Michel, Aalbersberg, IJsbrand Jan, Appleton, Gabrielle, Axton, Myles, Baak, Arie, Blomberg, Niklas, Boiten, Jan Willem, da Silva Santos, Luiz Bonino, Bourne, Philip E., Bouwman, Jildau, Brookes, Anthony J., Clark, Tim, Crosas, Mercè, Dillo, Ingrid, Dumon, Olivier, Edmunds, Scott, Evelo, Chris T., Finkers, Richard, Gonzalez-Beltran, Alejandra, Gray, Alasdair J.G., Groth, Paul, Goble, Carole, Grethe, Jeffrey S., Heringa, Jaap, ’t Hoen, Peter A C, Hooft, Rob, Kuhn, Tobias, Kok, Ruben, Kok, Joost, Lusher, Scott J., Martone, Maryann E, Mons, Albert, Packer, Abel L., Persson, Bengt, Rocca-Serra, Philippe, Roos, Marco, van Schaik, Rene, Sansone, Susanna Assunta, Schultes, Erik, Sengstag, Thierry, Slater, Ted, Strawn, George, Swertz, Morris A., Thompson, Mark, Van Der Lei, Johan, Van Mulligen, Erik, Velterop, Jan, Waagmeester, Andra, Wittenburg, Peter, Wolstencroft, Katherine, Zhao, Jun, and Mons, Barend Scientific Data 2016 [Abs] [DOI:10.1038/sdata.2016.18]

   There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

3. Sources of Change for Modern Knowledge Organization Systems Lauruhn, Michael, and Groth, Paul KNOWLEDGE ORGANIZATION 2016 [arXiv] [DOI:10.5771/0943-7444-2016-8-622]