Towards the semantic formalization of science

The past decades have witnessed a huge growth in scholarly information published on the Web, mostly in unstructured or semi-structured formats, which hampers scientific literature exploration and scientometric studies. Past studies on ontologies for structuring scholarly information focused on describing scholarly articles' components, such as document structure, metadata and bibliographies, rather than the scientific work itself. Over the past four years, we have been developing the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modeling the research findings in various fields of modern science resulting in a knowledge graph. Here, we introduce this ontology suite and discuss the design considerations taken into account during its development. We deem that within the next years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work

Modeling Dislocation Dynamics Data Using Semantic Web Technologies

Research in the field of Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a distinct type of defect called "dislocation". This defect significantly affects various material properties, including strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behavior through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modeled using semantic web technologies through annotating data with ontologies. We extend the already existing Dislocation Ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology) allowing for representing the dislocation simulation data efficiently. Moreover, we show a real-world use case by representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) that illustrates the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility to query DisLocKG

Scholarly event characteristics in four fields of science : a metrics-based analysis

One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org. © 2020, The Author(s)

Semantic Representation of Physics Research Data

Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing

A comprehensive quality assessment framework for scientific events

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories—events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics’ values coincide with the intuitive agreement of the community on its “top conferences”. Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors

A comprehensive quality assessment framework for scientific events

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories—events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics’ values coincide with the intuitive agreement of the community on its “top conferences”. Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors. © 2020, The Author(s)

Investigating pronouns: the use of “we” in Secondary School Students’ essay writing

The present study is an attempt to analyze the linguistic constituent; pronoun,B“we” as stated in the essays written by third year secondary students of Arab Republic of Egypt in two Secondary schools. As the study under investigation depends so much on a corpus and descriptive analysis, it explores the L2 output of learners in their writing of essay compositions context. It provides examples of pronouns employed by the students. This particular study was aimed at investigating pronouns’ frequencies, the singular Pronouns, and the Plural Pronouns of the written texts, highlighting the students’ understanding of making use of this linguistic constituent when they write. A corpus-based learner utilized a corpus investigation that has tagging and a frequency of part of speech (POS) of examination employing a software known as concordance will be the scheme of analysis. The results depicted an over-use of the 1st person plural as a subjective personal pronoun (we). The analysis discussion of the pedagogical contribution will be introduced

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade

The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics

Domain level ontology design: DISO and MDMC-NEP Provenance

How can a computer understand the relations of data or objects from the real world? Ontologies are semantic artifacts that capture knowledge about their domain of interest in a machine-understandable form. The main goal of developing ontologies is to formalize concepts and their relations through which humans express meaning and to use them as a communication interface to machines. Thus, ontology development is an important step towards generating linked and FAIR data. Within HMC we support and co-develop domain and application-level ontologies. Here we present two developments: Dislocation Ontology (DISO) and Model and Data-Driven Materials Characterization Provenance (MDMC-PROV). DISO: An important class of materials is crystalline materials, e.g., metals and semiconductors, which nearly always contain defects, the “dislocations”. This type of defect determines many important material properties, e.g., strength and ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. However, there is still a lack of common standards to formally describe and represent disclocations. Thus, in this work we develop the dislocation ontology (DISO), which is a domain ontology that defines the concepts and relationships related to linear defects in crystalline materials. DISO is published [1] through a persistent URL following W3C best practices for publishing Linked data. MDMC-Prov: The rapid development of science and technology in everyday large data generation does not match the data understanding. These days, understanding how experiments are performed and results are derived become more complex due to a lack of provenance documentation. Therefore, the provenance must be tracked, described, and managed over the research process. Thus, in this work, we report an application ontology that can capture provenance information in materials science experiments. The ontology is based on the MDMC glossary [2], which defines the common terms in the materials science experiments. From each term, we map to PROV-O [3]. These ensure the validity, reproducibility, and reusability of the data. [1] https://purls.helmholtz-metadaten.de/diso [2] https://jl-mdmc-helmholtz.de [3] https://www.w3.org/TR/2013/NOTE-prov-primer-20130430/ DISO:  An important class of materials is crystalline materials, e.g., metals and semiconductors, which nearly always contain defects, the “dislocations”. This type of defect determines many important material properties, e.g., strength and ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. However, there is still a lack of common standards to formally describe and represent disclocations. Thus, in this work we develop the dislocation ontology (DISO), which is a domain ontology that defines the concepts and relationships related to linear defects in crystalline materials. DISO is published1 through a persistent URL following W3C best practices for publishing Linked data. MDMC-Prov: The rapid development of science and technology in everyday large data generation does not match the data understanding. These days, understanding how experiments are performed and results are derived become more complex due to a lack of provenance documentation. Therefore, the provenance must be tracked, described, and managed over the research process. Thus, in this work, we report an application ontology that can capture provenance information in materials science experiments. The ontology is based on the MDMC glossary, which defines the common terms in the materials science experiments. From each term, we map to PROV-O3. These ensure the validity, reproducibility, and reusability of the data