Towards a Cloud-Based Service for Maintaining and Analyzing Data About Scientific Events

We propose the new cloud-based service OpenResearch for managing and analyzing data about scientific events such as conferences and workshops in a persistent and reliable way. This includes data about scientific articles, participants, acceptance rates, submission numbers, impact values as well as organizational details such as program committees, chairs, fees and sponsors. OpenResearch is a centralized repository for scientific events and supports researchers in collecting, organizing, sharing and disseminating information about scientific events in a structured way. An additional feature currently under development is the possibility to archive web pages along with the extracted semantic data in order to lift the burden of maintaining new and old conference web sites from public research institutions. However, the main advantage is that this cloud-based repository enables a comprehensive analysis of conference data. Based on extracted semantic data, it is possible to determine quality estimations, scientific communities, research trends as well the development of acceptance rates, fees, and number of participants in a continuous way complemented by projections into the future. Furthermore, data about research articles can be systematically explored using a content-based analysis as well as citation linkage. All data maintained in this crowd-sourcing platform is made freely available through an open SPARQL endpoint, which allows for analytical queries in a flexible and user-defined way.Comment: A completed version of this paper had been accepted in SAVE-SD workshop 2017 at WWW conferenc

Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML

OpenAIRE, the Open Access Infrastructure for Research in Europe, comprises a database of all EC FP7 and H2020 funded research projects, including metadata of their results (publications and datasets). These data are stored in an HBase NoSQL database, post-processed, and exposed as HTML for human consumption, and as XML through a web service interface. As an intermediate format to facilitate statistical computations, CSV is generated internally. To interlink the OpenAIRE data with related data on the Web, we aim at exporting them as Linked Open Data (LOD). The LOD export is required to integrate into the overall data processing workflow, where derived data are regenerated from the base data every day. We thus faced the challenge of identifying the best-performing conversion approach.We evaluated the performances of creating LOD by a MapReduce job on top of HBase, by mapping the intermediate CSV files, and by mapping the XML output.Comment: Accepted in 0th Metadata and Semantics Research Conferenc

SAVE-SD 2017: Third Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data

The third edition of the Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data (SAVE-SD 2017) is taking place in Perth, Australia on the 3rd of April 2017, co-located with the 26th International World Wide Web Conference. The main goal of the workshop is to provide a venue for researchers, publishers and other companies to engage in discussions about semantics, analytics and visualisations on scholarly data

Una revisión de la investigación de Global Reporting Initiative (GRI) con informes de sostenibilidad: conjunto de datos 1999-2020

This review paper aims to identify the main areas of studies in the field of the Global Reporting Initiative (GRI) research with sustainability reports. Using a bibliometric analysis, this study evaluated 955 published documents retrieved from the Scopus database to find a research review structure on GRI with sustainability topics from 1999 to 2020 by utilizing the bibliometric package in VOSviewer and Harzing’s Publish or Perish. This paper examined the most effective journals, authors, countries, institutions, subject area, keywords, citation, co-authorship, co-citation, bibliographic coupling, and co-occurrences networks. Also, this paper demonstrated the intellectual structure of the research and perceived obstacles to growth in the literature. The results show that the trend of publications has been growing over the past 20 years. This study offers a comprehensive understanding and publication of past studies trends and suggests that it will be a much greater number of articles in this field over the next decade which help the future direction of researchers in this area.Este trabajo tiene como objetivo identificar las principales áreas de estudio en la investigación de Global Reporting Initiative (GRI). Mediante un análisis bibliométrico, este estudio evaluó 955 documentos publicados recuperados de la base de datos Scopus para encontrar una estructura de revisión de la investigación sobre GRI con temas de sostenibilidad desde 1999 hasta 2020 utilizando el paquete bibliométrico de VOSviewer y Publish or Perish de Harzing. Este trabajo examinó las revistas, autores, países, instituciones, área temática, palabras clave, citación, coautoría, co-citación, acoplamiento bibliográfico y redes de co-ocurrencias más eficaces. Asimismo, este trabajo demostró la estructura intelectual de la investigación y los obstáculos percibidos para el crecimiento de la bibliografía. Los resultados muestran que en los últimos 20 años la tendencia de las publicaciones ha ido en aumento. Este estudio ofrece una comprensión global y la publicación de las tendencias de los estudios anteriores y sugiere que habrá un número mucho mayor de artículos en este campo durante la próxima década

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)

5* Knowledge Graph Embeddings with Projective Transformations

Performing link prediction using knowledge graph embedding (KGE) models is a popular approach for knowledge graph completion. Such link predictions are performed by measuring the likelihood of links in the graph via a transformation function that maps nodes via edges into a vector space. Since the complex structure of the real world is reflected in multi-relational knowledge graphs, the transformation functions need to be able to represent this complexity. However, most of the existing transformation functions in embedding models have been designed in Euclidean geometry and only cover one or two simple transformations. Therefore, they are prone to underfitting and limited in their ability to embed complex graph structures. The area of projective geometry, however, fully covers inversion, reflection, translation, rotation, and homothety transformations. We propose a novel KGE model, which supports those transformations and subsumes other state-of-the-art models. The model has several favorable theoretical properties and outperforms existing approaches on widely used link prediction benchmarks