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Informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients. Meanwhile, social media is an infrastructure that allows public instant sharing of information, thus it has recently become popular in medical applications. In this study, we apply Multi Distance Knowledge Graph Embeddings (MDE) to link physicians and surgeons to the latest medical breakthroughs that are shared as the research results on Twitter. Our study shows that using this method physicians can be informed about the new findings in their field given that they have an account dedicated to their profession., Comment: AI for Good, Data Science for Social Good, Machine learning for Social Good, Twitter Data, Knowledge Graph Embeddings, Medical Research

Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns. However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero. They also lose information when learning relations with reflexive patterns since they become symmetric and transitive. We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms. Our solution is based on two principles: 1) we use a limit-based loss instead of a margin ranking loss and, 2) by learning independent embedding vectors for each of the terms we can collectively train and predict using contradicting distance terms. We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns. We propose MDE as a neural network model that allows us to map non-linear relations between the embedding vectors and the expected output of the score function. Our empirical results show that MDE performs competitively to state-of-the-art embedding models on several benchmark datasets., Comment: Accepted paper in ECAI 2020

An increasing number of scientific publications are created in open and transparent peer review models: a submission is published first, and then reviewers are invited, or a submission is reviewed in a closed environment but then these reviews are published with the final article, or combinations of these. Reasons for open peer review include giving better credit to reviewers and enabling readers to better appraise the quality of a publication. In most cases, the full, unstructured text of an open review is published next to the full, unstructured text of the article reviewed. This approach prevents human readers from getting a quick impression of the quality of parts of an article, and it does not easily support secondary exploitation, e.g., for scientometrics on reviews. While document formats have been proposed for publishing structured articles including reviews, integrated tool support for entire open peer review workflows resulting in such documents is still scarce. We present AR-Annotator, the Automatic Article and Review Annotator which employs a semantic information model of an article and its reviews, using semantic markup and unique identifiers for all entities of interest. The fine-grained article structure is not only exposed to authors and reviewers but also preserved in the published version. We publish articles and their reviews in a Linked Data representation and thus maximize their reusability by third-party applications. We demonstrate this reusability by running quality-related queries against the structured representation of articles and their reviews., Comment: submitted to review to "Publications"

Collaborative scientific authoring is increasingly being supported by software tools. Traditionally, desktop-based authoring tools had the most advanced editing features, allowed for more formatting options, and included more import/export filters. Web-based tools have excelled in their collaboration support. Recently, developers on both sides have been trying to close this gap by extending desktop-based tools to better support collaboration and by making web-based tools richer in functionality. To verify to what extent these developments actually meet the needs of researchers, we gathered precise requirements towards better tool support for scientific authoring and reviewing workflows by interviewing 213 users and studying a corpus of 27 documents. We present the design of the survey and interpret its results. The conclusion is that WYSIWYG and offline desktop authoring tools continue to be more popular among academics than text-based and online editors., Comment: This is the version of the article before review and it is currently under review. This version is subject to updates

Scholarly document creation continues to face various obstacles. Scholarly text production requires more complex word processors than other forms of texts because of the complex structures of citations, formulas and figures. The need for peer review, often single-blind or double-blind, creates needs for document management that other texts do not require. Additionally, the need for collaborative editing, security and strict document access rules means that many existing word processors are imperfect solutions for academics. Nevertheless, most papers continue to be written using Microsoft Word (Sadeghi et al. 2017). We here analyze some of the problems with existing academic solutions and then present an argument why we believe that running an open source academic writing solution for academic purposes, such as Fidus Writer, on a Network Attached Storage (NAS) server could be a viable alternative., Comment: 15 pages, paper presented at the Enabling Decentralised Scholarly Communication workshop co-located with the Extended Semantic Web Conference (ESWC 2017)

The objective of the OSCOSS research project on "Opening Scholarly Communication in the Social Sciences" is to build a coherent collaboration environment that facilitates scholarly communication workflows of social scientists in the roles of authors, reviewers, editors and readers. This paper presents the implementation of the core of this environment: the integration of the Fidus Writer academic word processor with the Open Journal Systems (OJS) submission and review management system., Comment: 11 pages, 2 figures, submitted to the journal IWP