Browsing by Author "Aizawa, Akiko"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- ItemCaching and Reproducibility: Making Data Science Experiments Faster and FAIRer(Lausanne : Frontiers Media, 2022) Schubotz, Moritz; Satpute, Ankit; Greiner-Petter, André; Aizawa, Akiko; Gipp, BelaSmall to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence is twofold. First, subsequent researchers must spend significant work hours building upon the proposed hypotheses or experimental framework. In the worst case, others cannot reproduce the experiment and reuse the findings for subsequent research. Second, suppose the ad-hoc research software fails during often long-running computational expensive experiments. In that case, the overall effort to iteratively improve the software and rerun the experiments creates significant time pressure on the researchers. We suggest making caching an integral part of the research software development process, even before the first line of code is written. This article outlines caching recommendations for developing research software in data science projects. Our recommendations provide a perspective to circumvent common problems such as propriety dependence, speed, etc. At the same time, caching contributes to the reproducibility of experiments in the open science workflow. Concerning the four guiding principles, i.e., Findability, Accessibility, Interoperability, and Reusability (FAIR), we foresee that including the proposed recommendation in a research software development will make the data related to that software FAIRer for both machines and humans. We exhibit the usefulness of some of the proposed recommendations on our recently completed research software project in mathematical information retrieval.
- ItemCollaborative annotation and semantic enrichment of 3D media(New York,NY,United States : Association for Computing Machinery, 2022) Rossenova, Lozana; Schubert, Zoe; Vock, Richard; Sohmen, Lucia; Günther, Lukas; Duchesne, Paul; Blümel, Ina; Aizawa, AkikoA new FOSS (free and open source software) toolchain and associated workflow is being developed in the context of NFDI4Culture, a German consortium of research- and cultural heritage institutions working towards a shared infrastructure for research data that meets the needs of 21st century data creators, maintainers and end users across the broad spectrum of the digital libraries and archives field, and the digital humanities. This short paper and demo present how the integrated toolchain connects: 1) OpenRefine - for data reconciliation and batch upload; 2) Wikibase - for linked open data (LOD) storage; and 3) Kompakkt - for rendering and annotating 3D models. The presentation is aimed at librarians, digital curators and data managers interested in learning how to manage research datasets containing 3D media, and how to make them available within an open data environment with 3D-rendering and collaborative annotation features.
- ItemComparative Verification of the Digital Library of Mathematical Functions and Computer Algebra Systems(Berlin ; Heidelberg : Springer, 2022) Greiner-Petter, André; Cohl, Howard S.; Youssef, Abdou; Schubotz, Moritz; Trost, Avi; Dey, Rajen; Aizawa, Akiko; Gipp, Bela; Fisman, Dana; Rosu, GrigoreDigital mathematical libraries assemble the knowledge of years of mathematical research. Numerous disciplines (e.g., physics, engineering, pure and applied mathematics) rely heavily on compendia gathered findings. Likewise, modern research applications rely more and more on computational solutions, which are often calculated and verified by computer algebra systems. Hence, the correctness, accuracy, and reliability of both digital mathematical libraries and computer algebra systems is a crucial attribute for modern research. In this paper, we present a novel approach to verify a digital mathematical library and two computer algebra systems with one another by converting mathematical expressions from one system to the other. We use our previously developed conversion tool (referred to as ) to translate formulae from the NIST Digital Library of Mathematical Functions to the computer algebra systems Maple and Mathematica. The contributions of our presented work are as follows: (1) we present the most comprehensive verification of computer algebra systems and digital mathematical libraries with one another; (2) we significantly enhance the performance of the underlying translator in terms of coverage and accuracy; and (3) we provide open access to translations for Maple and Mathematica of the formulae in the NIST Digital Library of Mathematical Functions.
- ItemTinyGenius: Intertwining natural language processing with microtask crowdsourcing for scholarly knowledge graph creation(New York,NY,United States : Association for Computing Machinery, 2022) Oelen, Allard; Stocker, Markus; Auer, Sören; Aizawa, AkikoAs the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.