Domain-Independent Extraction of Scientific Concepts from Research Articles

Brack, Arthur; D’Souza, Jennifer; Hoppe, Anett; Auer, Sören; Ewerth, Ralph

doi:10.1007/978-3-030-45439-5_17

Cited by 47 publications

(61 citation statements)

References 36 publications

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“…genes, datasets or countries) can be discovered more reliably, the recognition of other more vaguely defined entities (e.g. materials, processes) is very error-prone (Brack 2020).…”

Section: Dies Ist Ein Preprintmentioning

confidence: 99%

Improving Access to Scientific Literature with Knowledge Graphs

Auer

Oelen

Haris

et al. 2020

Bibliothek Forschung Und Praxis

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The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.

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“…genes, datasets or countries) can be discovered more reliably, the recognition of other more vaguely defined entities (e.g. materials, processes) is very error-prone (Brack 2020).…”

Section: Dies Ist Ein Preprintmentioning

confidence: 99%

Improving Access to Scientific Literature with Knowledge Graphs

Auer

Oelen

Haris

et al. 2020

Bibliothek Forschung Und Praxis

Self Cite

View full text Add to dashboard Cite

show abstract

“…The structural science mathematics provides particularly suitable content for the ORKG: Its published prose is clear and dense from a linguistic point of view. However, as [4] have shown the high degree of abstraction in mathematics makes a conceptualisation consisting of the categories process, method, material, and data, which have been adapted to empirical sciences, inexpedient. The ORKG is not limited to this model but its feature, the abstract annotator, has been shown to be out of its depth with regard to mathematics.…”

Section: Operational Research As a Use Case From Mathematicsmentioning

confidence: 99%

“…An exemplary comparison of three selected papers can be found at https://www. orkg.org/orkg/comparison?contributions=R12018,R12059,R12193 4. https://www.zotero.org/.…”

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confidence: 99%

Operational Research Literature as a Use Case for the Open Research Knowledge Graph

Runnwerth

Stocker

Auer

2020

Lecture Notes in Computer Science

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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 habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.

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“…Citation sentiment analysis is used to determine sentiment polarity of clinical trial papers using n-gram and sentiment lexicon features on annotated corpus [2]. A summary of a corpus of research papers, domain-independent structural relations between abstracts and domain of scholarly medical articles, state-of-the-art deep learning baseline was constructed and has been reported [3]. Given a particular paper of interest, CiteSeer can display the context of how the paper is cited or indexed in subsequent publications with a summary of the paper in electronic format [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Rarely Explored Materials and Methods Sections from Research Journals using Machine Learning Techniques

Jayaram¹,

Prakash²,

Jayaram³

2020

IJACSA

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The scientific community is expanding by leaps and bounds every day owing to pioneering and path breaking scientific literature published in journals around the globe. Viewing as well as retrieving this data is a challenging task in today's fast paced world. The essence and importance of scientific research papers for the expert lies in their experimental and theoretical results along with the sanctioned research projects from the organizations. Since scant work has been done in this direction, the alternative option is to explore text mining by machine learning techniques. Myriad journals are available on material research which throws light on a gamut of materials, synthesis methods, and characterization methods used to study properties of the materials. Application of materials has many diversified areas, hence selected papers from "Journal of Material Science" where "Materials and Methods" sections contains names of the method, characterization techniques (instrumental methods), algorithms, images, etc. used in research work. The "Acknowledgment" section conveys information about authors' proximity, collaborations with organizations that are again not explored for the citation network. In the present articulated work, our attempt is to derive a means to automatically extract methods or terminologies used in characterization techniques, author, organization data from "Materials and Methods" and "Acknowledgment" sections, using machine learning techniques. Another goal of this research is to provide a data set for characterization terms, classification and an extended version of the existing citation network for material research. The complete dataset will help new researchers to select research work, find new domains and techniques to solve advanced scientific research problems.

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Domain-Independent Extraction of Scientific Concepts from Research Articles

Cited by 47 publications

References 36 publications

Improving Access to Scientific Literature with Knowledge Graphs

Improving Access to Scientific Literature with Knowledge Graphs

Operational Research Literature as a Use Case for the Open Research Knowledge Graph

Automatic Extraction of Rarely Explored Materials and Methods Sections from Research Journals using Machine Learning Techniques

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