Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG.
We leverage the Open Research Knowledge Graph - a scholarly infrastructure that supports the creation, curation, and reuse of structured, semantic scholarly knowledge - and present an approach for persistent identification of FAIR scholarly knowledge. We propose a DOI-based persistent identification of ORKG Papers, which are machine-actionable descriptions of the essential information published in scholarly articles. This enables the citability of FAIR scholarly knowledge and its discovery in global scholarly communication infrastructures (e.g., DataCite, OpenAIRE, and ORCID). While publishing…
A plethora of scholarly knowledge is being published on distributed scholarly infrastructures. Querying a single infrastructure is no longer sufficient for researchers to satisfy information needs. We present a GraphQL-based federated query service for executing distributed queries on numerous, heterogeneous scholarly infrastructures (currently, ORKG, DataCite and GeoNames), thus enabling the integrated retrieval of scholarly content from these infrastructures. Furthermore, we present the methods that enable cross-walks between artefact metadata and artefact content across scholarly infrastruc…
Scientists always look for the most accurate and relevant answer to their queries on the scholarly literature. Traditional scholarly search systems list documents instead of providing direct answers to the search queries. As data in knowledge graphs are not acquainted semantically, they are not machine-readable. Therefore, a search on scholarly knowledge graphs ends up in a full-text search, not a search in the content of scholarly literature. In this demo, we present a faceted search system that retrieves data from a scholarly knowledge graph, which can be compared and filtered to better sati…
Despite being a key element in the process of disseminating scientific knowledge, editorial notices are often obscured and not clearly linked to the papers to which they refer. In the present paper, we describe established methods of aggregating notice data, and introduce a novel method of finding editorial notices in the scientific literature. Specifically, we describe how article titles denote notices to existing publications, and how this information can be used to tie notices to papers in an automated fashion. Finally, as part of a broader movement to make science more transparent, we make…
Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an ex…
Despite improved digital access to scholarly literature in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. Scholarly knowledge remains locked in representations that are inadequate for machine processing. The Open Research Knowledge Graph (ORKG) is an infrastructure for representing, curating and exploring scholarly knowledge in a machine actionable manner. We demonstrate the core functionality of ORKG for representing research contributions published in scholarly articles. A video of the demonstration and the …
A plethora of scientific software packages are published in repositories, e.g., Zenodo and figshare. These software packages are crucial for the reproducibility of published research. As an additional route to scholarly knowledge graph construction, we propose an approach for automated extraction of machine actionable (structured) scholarly knowledge from published software packages by static analysis of their (meta)data and contents (in particular scripts in languages such as Python). The approach can be summarized as follows. First, we extract metadata information (software description, prog…
Scientists always look for the most accurate and relevant answers to their queries in the literature. Traditional scholarly digital libraries list documents in search results, and therefore are unable to provide precise answers to search queries. In other words, search in digital libraries is metadata search and, if available, full-text search. We present a methodology for improving a faceted search system on structured content by leveraging a federation of scholarly knowledge graphs. We implemented the methodology on top of a scholarly knowledge graph. This search system can leverage content …
Unraveling the Ai2 Asta Scholarly Research Assistant Citation System
The iMetrics Lab. Department of Audiovisual Communication, Documentation and History of Art, Universitat Politècnica de València（瓦伦西亚理工大学）；Departament de Biblioteconomia, Documentació i Comunicació Audiovisual, Universitat de Barcelona（巴塞罗那大学）
L3S Research Center（L3S研究中心）；Leibniz University Hannover（莱布尼茨汉诺威大学）；Cerence GmbH（Cerence有限公司）；Zerotha Research（Zerotha研究）；TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心）
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exB…
TIB Leibniz Information Centre for Science and Technology（TIB莱布尼茨科学与技术信息中心）；L3S Research Center（L3S研究中心）
We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classification, and 5. text classification. In this article, we describe the outcomes of this pilot annotati…
University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）
Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline. In this paper, we examine the literature review practices of data scientists. Data science represents a field seeing an exponential rise in papers, and increasingly drawing on and being applied in numerous diverse disciplines. Recent efforts have seen the development of several tools intended to help data scientists cope with a d…
The 7 Habits of Highly Effective Research Communicators
Web 2.0和同时出现的Library 2.0平台帮助图书馆和信息专业人员超越其物理边界，接触新的受众。在一个全球化的社会中，信息成为非常有用的资源，可以增强边缘化社区的社会经济能力，促进普通公民的经济繁荣，丰富自由思想的知识。学术信息对于致力于知识进步的研究人员来说既具有发展性又具有功能性。我们在进行学术研究的同时，必须认识到信息流和信息生态的中继。我们参考的出版学术文献有助于我们创造新知识。同样，我们发表的学术著作应该延伸到未来的研究人员，以再生下一个知识层面。幸运的是，当今的研究传播者有许多免费提供的个性化数字化工具，以推广到具有类似研究兴趣的全球化研究受众。这些工具和技术已经被世界各地不同学科领域的许多研究人员采用，南亚的LIS研究人员应该积极利用这些工具和技术，在全球传播他们的学术研究作品。这种新发现的热情将很快成为LIS领域研究传播者的积极习惯和文化实践的组成部分。
The trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles. We reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was included in our analysis i…
L3S Research Center（L3S研究中心）；Leibniz University of Hannover（汉诺威莱布尼茨大学）；TIB Leibniz Information Centre for Science and Technology（TIB莱布尼茨科学技术信息中心）
Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variet…
Eugene Garfield's Scholarly Impact: A Scientometric Review
引文索引的概念已经深入到研究本身的许多部分以及研究发挥不可或缺作用的广泛环境中，从研究评估，众多指标到越来越广泛的科学学科。在这篇文章中，我们向尤金·加菲尔德致敬，并对他带给我们的智力资产进行了科学计量学回顾。此外，我们探讨了知识景观，随后演变连接到他的许多想法。我们说明了科学文献的系统综述可能揭示什么，以及我们可以从通过引用诱导模式传达的丰富信息中学到什么。这项研究是由CiteSpace进行的，CiteSpace是基于Web of Science和Scopus数据的众多科学绘图工具之一。如果没有加菲尔德的发明，这些都不可能实现。
Review articles are a means to structure state-of-the-art literature and to organize the growing number of scholarly publications. However, review articles are suffering from numerous limitations, weakening the impact the articles could potentially have. A key limitation is the inability of machines to access and process knowledge presented within review articles. In this work, we present SmartReviews, a review authoring and publishing tool, specifically addressing the limitations of review articles. The tool enables community-based authoring of living articles, leveraging a scholarly knowledg…
During the current century, each major coronavirus outbreak has triggered a quick surge of academic publications on this topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19), however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts in an unprecedented way. In less than five months, more than 12,000 research items have been indexed while the number increasing every day. With the crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many acade…
Making Software FAIR: A machine-assisted workflow for the research software lifecycle
The Open University（开放大学）；CORE；Knowledge Media institute（知识媒体研究所）；Inria（法国国家信息与自动化研究所）；Science Miner；Brno University of Technology（布尔诺理工大学）；Polish Academy of Sciences（波兰科学院）；European Bioinformatics institute（欧洲生物信息研究所）
The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication h…
Six years after the seminal paper on FAIR was published, researchers still struggle to understand how to implement FAIR. For many researchers FAIR promises long-term benefits for near-term effort, requires skills not yet acquired, and is one more thing in a long list of unfunded mandates and onerous requirements on scientists. Even for those required to or who are convinced they must make time for FAIR research practices, the preference is for just-in-time advice properly sized to the scientific artifacts and process. Because of the generality of most FAIR implementation guidance, it is diffic…
Falcon 7b for Software Mention Detection in Scholarly Documents
Fraunhofer Institute for Applied Information Technology（弗劳恩霍夫应用信息技术研究所）；University of Koblenz（科布伦茨大学）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；University Hospital of Cologne（科隆大学医院）
Comparison of Feature Learning Methods for Metadata Extraction from PDF Scholarly Documents
Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所FIT）；University Hospital of Cologne（科隆大学医院）；University of Koblenz（科布伦茨大学）；Soochow University（苏州大学）
As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to the society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We in…
In recent decades, there has been a major shift towards improved digital access to scholarly works. However, even now that these works are available in digital form, they remain document-based, making it difficult to communicate the knowledge they contain. The next logical step is to extend these works with more flexible, fine-grained, semantic, and context-sensitive representations of scholarly knowledge. The Open Research Knowledge Graph (ORKG) is a platform that structures and interlinks scholarly knowledge, relying on crowdsourced contributions from researchers (as a crowd) to acquire, cur…
Stakeholders in research and scientific publishing are gradually joining the Open-Access (OA) movement, which is gaining momentum to become nowadays at the heart of scientific policies in high-income countries. The rise of OA generates profound changes in the chain of production and dissemination of knowledge. Free access to peer-reviewed research methods and results has contributed to the dynamics of science observed in recent years. The modes of publication and access have also evolved; the classic model, based on journal subscriptions is gradually giving way to new economic models that have…
Data citations provide a foundation for studying research data impact. Collecting and managing data citations is a new frontier in archival science and scholarly communication. However, the discovery and curation of research data citations is labor intensive. Data citations that reference unique identifiers (i.e. DOIs) are readily findable; however, informal mentions made to research data are more challenging to infer. We propose a natural language processing (NLP) paradigm to support the human task of identifying informal mentions made to research datasets. The work of discovering informal da…
Automated scholarly paper review: Concepts, technologies, and challenges
Xiamen University（厦门大学）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室）
Identifying important scholarly literature at an early stage is vital to the academic research community and other stakeholders such as technology companies and government bodies. Due to the sheer amount of research published and the growth of ever-changing interdisciplinary areas, researchers need an efficient way to identify important scholarly work. The number of citations a given research publication has accrued has been used for this purpose, but these take time to occur and longer to accumulate. In this article, we use altmetrics to predict the short-term and long-term citations that a s…
The recent Italian regulations about the open-access availability of publicly-funded research publications, and the documentation landscape in astrophysics
