PapersQ8
We found the following papers:
| Paper | Authors | Venue | year | abstract | future work |
|---|---|---|---|---|---|
| Analysing Scholarly Communication Metadata of Computer Science Events | Said Fathalla Sahar Vahdati Christoph Lange Sören Auer | TPDL | 2017 | Over the past 30 years, we have observed the impact of the
ubiquitous availability of the Internet, email, and web-based services on scholarly communication. The preparation of manuscripts as well as the organization of conferences, from submission to peer review to publication, have become considerably easier and efficient. A key question now is what were the measurable effects on scholarly communication in computer science? Of particular interest are the following questions: Did the number of submissions to conferences increase? How did the selection processes change? Is there a proliferation of publications? We shed light on some of these questions by analyzing comprehensive scholarly communication metadata from a large number of computer science conferences of the last 30 years. Our transferable analysis methodology is based on descriptive statistics analysis as well as exploratory data analysis and uses crowd-sourced, semantically represented scholarly communication metadata from OpenResearch.org. | In further research, we aim to expand the analysis to other fields of science
and to smaller events. Also, it is interesting to assess the impact of digitisation with regard to further scholarly communication means, such as journals (which are more important in fields other than computer science), workshops, funding calls and proposal applications as well as awards. Although large parts of our analysis methodology are already automated, we plan to further optimise the process so that analysis can be almost instantly generated from the OpenResearch data basis. |
| Integration of Scholarly Communication Metadata using Knowledge Graphs | Afshin Sadeghi Christoph Lange Maria-Esther Vidal Sören Auer | TPDL | 2017 | Important questions about the scientific community, e.g., what authors
are the experts in a certain field, or are actively engaged in international collaborations, can be answered using publicly available datasets. However, data required to answer such questions is often scattered over multiple isolated datasets. Recently, the Knowledge Graph (KG) concept has been identified as a means for interweaving heterogeneous datasets and enhancing answer completeness and soundness. We present a pipeline for creating high quality knowledge graphs that comprise data collected from multiple isolated structured datasets. As proof of concept, we illustrate the different steps in the construction of a knowledge graph in the domain of scholarly communication metadata (SCM-KG). Particularly, we demonstrate the benefits of exploiting semantic web technology to reconcile data about authors, papers, and conferences. We conducted an experimental study on an SCM-KG that merges scientific research metadata from the DBLP bibliographic source and the Microsoft Academic Graph. The observed results provide evidence that queries are processed more effectively on top of the SCM-KG than over the isolated datasets, while execution time is not negatively affected. | In the context of the OSCOSS project on Opening Scholarly Communication
in the Social Sciences, the SCM-KG approach will be used for providing authors with precise and complete lists of references during the article writing process. |
| Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles | Said Fathalla Sahar Vahdati Sören Auer Christoph Lange | TPDL | 2017 | Despite significant advances in technology, the way how research is done and especially communicated has not changed much. We have the vision that ultimately researchers will work on a common knowledge base comprising comprehensive descriptions of their research, thus making research contributions transparent and comparable. The current approach for structuring, systematizing and comparing research results is via survey or review articles. In this article, we describe how surveys for research fields can be represented in a semantic way, resulting in a knowledge graph that describes the individual research problems, approaches, implementations and evaluations in a structured and comparable way. We present a comprehensive ontology for capturing the content of survey articles. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the resulting knowledge graph by using it to answer queries about the different research contributions covered by the survey and evaluate how well the query answers serve readers’ information needs, in comparison to having them extract the same information from reading a survey paper. | Integrating our methodology with the procedure of publishing survey articles can
help to create a paradigm shift. We plan to further extend the ontology to cover other research methodologies and fields. For a more robust implementation of the proposed approach, we are planning to use and significantly expand the OpenResearch.org platform and a user-friendly SPARQL auto-generation services for accessing metadata analysis for non-expert users. More comprehensive evaluation of the services will be done after the implementation of the curation, exploration and discovery services. In addition, our intention is to develop and foster a living community around OpenResearch.org and SemSur, to extend the ontology and to ingest metadata to cover other research fields. |
| Towards a Knowledge Graph for Science | Sören Auer Viktor Kovtun Manuel Prinz Anna Kasprzik Markus Stocker | WIMS | 2018 | The document-centric workflows in science have reached (or already exceeded) the limits of adequacy. This is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. This presents an opportunity to rethink the dominant paradigm of document-centric scholarly information communication and transform it into knowledge-based information flows by representing and expressing information through semantically rich, interlinked knowledge graphs. At
the core of knowledge-based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future. By integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs. Furthermore, research results become directly comparable and easier to reuse. As our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure. | The work presented here delineates our initial steps towards a
knowledge graph for science. By testing existing and developing new components, we have so far focused on some core technical aspects of the infrastructure. Naturally, there are a number of research problems and implementation issues as well as a range of socio-technical aspects that need to be addressed in order to realize the vision. Dimensions of open challenges are, among others: • the low-threshold integration of researchers through methods of crowd-sourcing, human-machine interaction, and social networks; • automated analysis, quality assessment, and completion of the knowledge graph as well as interlinking with external sources; • support for representing fuzzy information, scientific discourse and the evolution of knowledge; • development of new methods of exploration, retrieval, and visualization of knowledge graph information. |
