publications

2018

1. End-to-End Learning for Answering Structured Queries Directly over Text Groth, Paul, Scerri, Antony, Ron Daniel, Jr., and Allen, Bradley P. 2018 [arXiv]
2. 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]
3. 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]
4. 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]