Semantic Annotation of Data Processing Pipelines in Scientific Publications

Mesbah, Sepideh; Fragkeskos, Kyriakos; Lofi, Christoph; Bozzon, Alessandro; Houben, Geert‐Jan

doi:10.1007/978-3-319-58068-5_20

Cited by 13 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various domain-specific ontologies exist, for instance, mathematics [65] (e.g. definitions, assertions, proofs), machine learning [62,74] (e.g. dataset, metric, model, experiment), and physics [96] (e.g.…”

Section: Scientific Ontologiesmentioning

confidence: 99%

Analysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies

et al. 2021

View full text Add to dashboard Cite

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications, and outline possible solutions.

show abstract

Section: Scientific Ontologiesmentioning

confidence: 99%

Analysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies

et al. 2021

View full text Add to dashboard Cite

show abstract

“…complexity [49] and clarity [15]) and on task recommendation [44] could benefit from the adoption of Semantic Web approaches for knowledge representation and named entity linking. We also identified initial work on using Linked Data to publish research results, in order to support research reproducibility [2,13,30,31] which we hope will be adopted on a larger scale by the community.…”

Section: Overall Trendsmentioning

confidence: 99%

Semantic Web and Human Computation: The status of an emerging field

Sabou

Aroyo

Bontcheva

et al. 2018

Self Cite

View full text Add to dashboard Cite

This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this editorial we inspect and reflect on the evolution of research at the intersection of Semantic Web and Human Computation. We use a methodology based on Systematic Mapping Studies to collect quantitative bibliographic data which we analyze through the lens of research topics envisioned by the research manifesto to characterize the evolution of research in this area, thus providing a context for introducing the papers of this special issue. We found evidences of a thriving research field; while steadily maturing, the field offers a number of open research opportunities for work where Semantic Web best practices and techniques are applied to support and improve the state-of-the-art in Human Computation, but also for work that exploits the strength of both areas to address scientifically and societally relevant issues.

show abstract

“…"machine learning", "stock price index") [24]. Automatic concept extraction from text has received much attention in the past decade [3,[18][19][20]26], and thus there exist a number of publicly available concept extractor tools, relying on techniques such as termfrequency analysis [26], co-occurrence graph [20], etc. Extracting concepts from MOOCs content is, however, a challenging task due to the low-frequency problem [23]: MOOCs videos are relatively short documents and due to the small number of words, statistical techniques (e.g.…”

Section: Concept Extractionmentioning

confidence: 99%

Concept Focus: Semantic Meta-Data for Describing MOOC Content

Mesbah

Chen

Torre

et al. 2018

Lifelong Technology-Enhanced Learning

Self Cite

View full text Add to dashboard Cite

MOOCs promised to herald a new age of open education. However, efficient access to MOOC content is still hard, thus unnecessarily complicating many use cases like efficient re-use of material, or tailored access for lifelong learning scenarios. One of the reasons for this lack of accessibility is the shortage of meaningful semantic metadata describing MOOC content and the resulting learning experience. In this paper, we explore Concept Focus, a new type of meta-data for describing a perceptual facet of modern video-based MOOCs, capturing how focused a learning resource is topic-wise, which is often an indicator of clarity and understandability. We provide the theoretical foundations of Concept Focus and outline a methodical workflow of how to automatically compute it for MOOC lectures. Furthermore, we show that the learners' consumption behavior is correlated with a MOOC lecture's Concept Focus, thus underlining that this type of meta-data is indeed relevant for user-centric querying, personalizing or even designing the MOOC experience. For showing this, we performed an extensive study with real-life MOOCs and 12,849 learners over the duration of three months.

show abstract

Semantic Annotation of Data Processing Pipelines in Scientific Publications

Cited by 13 publications

References 15 publications

Analysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies

Analysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies

Semantic Web and Human Computation: The status of an emerging field

Concept Focus: Semantic Meta-Data for Describing MOOC Content

Contact Info

Product

Resources

About