A Multi-Relational Network to Support the Scholarly Communication Process

The general pupose of the scholarly communication process is to support the creation and dissemination of ideas within the scientific community. At a finer granularity, there exists multiple stages which, when confronted by a member of the community, have different requirements and therefore different solutions. In order to take a researcher's idea from an initial inspiration to a community resource, the scholarly communication infrastructure may be required to 1) provide a scientist initial seed ideas; 2) form a team of well suited collaborators; 3) located the most appropriate venue to publish the formalized idea; 4) determine the most appropriate peers to review the manuscript; and 5) disseminate the end product to the most interested members of the community. Through the various delinieations of this process, the requirements of each stage are tied soley to the multi-functional resources of the community: its researchers, its journals, and its manuscritps. It is within the collection of these resources and their inherent relationships that the solutions to scholarly communication are to be found. This paper describes an associative network composed of multiple scholarly artifacts that can be used as a medium for supporting the scholarly communication process.Comment: keywords: digital libraries and scholarly communicatio

Editorial: Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation)

The increasing interest in analysing, describing, and improving the research process requires the development of new forms of scholarly data publication and analysis that integrates lessons and approaches from the field of Semantic Technologies, Science of Science, Digital Libraries, and Artificial Intelligence. This editorial summarises the content of the Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation), which aims to showcase some of the most interesting research efforts in the field. This issue includes an extended version of the best papers of the last two editions of the “Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination” (SAVE-SD 2017 and 2018) workshop at The Web Conference

Linking to Scientific Data: Identity Problems of Unruly and Poorly Bounded Digital Objects

Within information systems, a significant aspect of search and retrieval across information objects, such as datasets, journal articles, or images, relies on the identity construction of the objects. This paper uses identity to refer to the qualities or characteristics of an information object that make it definable and recognizable, and can be used to distinguish it from other objects. Identity, in this context, can be seen as the foundation from which citations, metadata and identifiers are constructed. In recent years the idea of including datasets within the scientific record has been gaining significant momentum, with publishers, granting agencies and libraries engaging with the challenge. However, the task has been fraught with questions of best practice for establishing this infrastructure, especially in regards to how citations, metadata and identifiers should be constructed. These questions suggests a problem with how dataset identities are formed, such that an engagement with the definition of datasets as conceptual objects is warranted. This paper explores some of the ways in which scientific data is an unruly and poorly bounded object, and goes on to propose that in order for datasets to fulfill the roles expected for them, the following identity functions are essential for scholarly publications: (i) the dataset is constructed as a semantically and logically concrete object, (ii) the identity of the dataset is embedded, inherent and/or inseparable, (iii) the identity embodies a framework of authorship, rights and limitations, and (iv) the identity translates into an actionable mechanism for retrieval or reference

University Libraries and the Open Research Knowledge Graph

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

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

Improving Access to Scientific Literature with Knowledge Graphs

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.Der Verbreitung wissenschaftlicher Erkenntnisse hat sich seit vielen hundert Jahren nicht grundlegend verändert: Er erfolgt in der Regel dokumentenbasiert - früher als klassischer Aufsatz auf Papier gedruckt und heute online als PDF. Mit rund 2,5 Millionen neuen Forschungsbeiträgen pro Jahr ertrinken Forscher in einer Flut von pseudo-digitalisierten PDF-Publikationen. Als Folge davon wird die Forschung stark geschwächt. In diesem Artikel plädieren wir dafür, wissenschaftliche Beiträge in strukturierter und semantischer Form als Wissensgraph zu repräsentieren. Der Vorteil ist, dass die in einem Wissensgraph dargestellten Informationen für Maschinen und Menschen lesbar sind. Als Beispiel geben wir einen Überblick über den Open Research Knowledge Graph (ORKG), einen Dienst, der diesen Ansatz umsetzt. Für die Erstellung des Wissensgraph setzen wir eine Mischung aus manuellen (crowd/expert sourcing) und (halb-)automatisierten Techniken ein. Nur mit einer solchen Kombination aus menschlicher und maschineller Intelligenz können wir die erforderliche Qualität der Darstellung erreichen, um neuartige Explorations- und Unterstützungsdienste für Forscher zu ermöglichen. Im Ergebnis kann ein Wissensgraph wie der ORKG verwendet werden, um einen komprimierten Überblick über den Stand der Technik in Bezug auf eine bestimmte Forschungsaufgabe zu geben, z.B. als tabellarischer Vergleich der Beiträge nach verschiedenen Merkmalen der Ansätze. Weitere mögliche intuitive Nutzungsschnittstellen zu solchen wissenschaftlichen Wissensgraphen sind domänenspezifische Visualisierungen oder die Beantwortung natürlichsprachlicher Fragen mittels Question Answering.Peer Reviewe

Workflow-centric research objects: First class citizens in scholarly discourse.

A workflow-centric research object bundles a workflow, the provenance of the results obtained by its enactment, other digital objects that are relevant for the experiment (papers, datasets, etc.), and annotations that semantically describe all these objects. In this paper, we propose a model to specify workflow-centric research objects, and show how the model can be grounded using semantic technologies and existing vocabularies, in particular the Object Reuse and Exchange (ORE) model and the Annotation Ontology (AO).We describe the life-cycle of a research object, which resembles the life-cycle of a scienti?c experiment

A Perceptually Based Comparison of Image Similarity Metrics

The assessment of how well one image matches another forms a critical component both of models of human visual processing and of many image analysis systems. Two of the most commonly used norms for quantifying image similarity are L1 and L2, which are specific instances of the Minkowski metric. However, there is often not a principled reason for selecting one norm over the other. One way to address this problem is by examining whether one metric, better than the other, captures the perceptual notion of image similarity. This can be used to derive inferences regarding similarity criteria the human visual system uses, as well as to evaluate and design metrics for use in image-analysis applications. With this goal, we examined perceptual preferences for images retrieved on the basis of the L1 versus the L2 norm. These images were either small fragments without recognizable content, or larger patterns with recognizable content created by vector quantization. In both conditions the participants showed a small but consistent preference for images matched with the L1 metric. These results suggest that, in the domain of natural images of the kind we have used, the L1 metric may better capture human notions of image similarity