Natalia Manola

A survey of context-aware cross-digital library personalization

The constant interaction of users with different Digital Libraries (DLs) and the subsequent scattering of user information across them raise the need not only for Digital Library interoperability but also for cross-Digital Library personalization. The latter calls for sharing and combining of user-information across different DL systems so that a DL system may take advantage of data from others. To achieve this goal, DL systems should be able to maintain compliant and interoperable user models and profiles that enable propagation and reconciliation of user information across different DLs. In this paper, we motivate the need for cross-Digital Library personalization, we define and examine user model, profile, and context interoperability, and we survey and discuss existing user model interoperability approaches.

Information inference in scholarly communication infrastructures: the OpenAIREplus project experience

The Information Inference Framework presented in this paper provides a general-purpose suite of tools enabling the definition and execution of flexible and reliable data processing workflows whose nodes offer application-specific processing capabilities. The IIF is designed for the purpose of processing big data, and it is implemented on top of Apache Hadoop-related technologies to cope with scalability and high-performance execution requirements. As a proof of concept we will describe how the framework is used to support linking and contextualization services in the context of the OpenAIRE infrastructure for scholarly communication.

Visual-Based Classification of Figures from Scientific Literature

Authors of scientific publications and books use images to present a wide spectrum of information. Despite the richness of the visual content of scientific publications the figures are usually not taken into consideration in the context of text mining methodologies towards the automatic indexing and retrieval of scientific corpora. In this work, we present a system for automatic categorization of figures from scientific literature to a set of predefined classes. We have employed a wide range of visual features that achieve high discrimination ability between the adopted classes. A real-world dataset has been compiled and annotated in order to train and evaluate the proposed method using three different classification schemata.

Discovering and Visualizing Interdisciplinary Content Classes in Scientific Publications

Text visualization is a rather important task related to scientific corpora, since it provides a way of representing these corpora in terms of content, leading to reinforcement of human cognition compared to abstract and unstructured text. In this paper, we focus on visualizing funding-specific scientific corpora in a supervised context and discovering interclass similarities which indicate the existence of inter-disciplinary research. This is achieved through training a supervised classification � visualization model based on the arXiv classification system. In addition, a funding mining submodule is used which identifies documents of particular funding schemes. This is conducted, in order to generate corpora of scientific publications that share a common funding scheme (e.g. FP7-ICT). These categorized sets of documents are fed as input to the visualization model in order to generate content representations and to discover highly correlated content classes. This procedure can provide a high level monitoring which is important for research funders and governments in order to be able to quickly respond to new developments and trends.

A functionality perspective on digital library interoperability

Digital Library (DL) interoperability requires addressing a variety of issues associated with functionality. We report on the analysis and solutions identified by the Functionality Working Group of the DL.org project during its deliberations on DL interoperability. Ultimately, we hope that work based on our perspective will lead to improved architectures and software, as well as to greater interoperability, for next-generation DL systems.

Connecting the local and the online in information management

With the popularity of social media sites, digital content is increasingly stored and managed online. At the same time, the desktop and local storage continues to provide a personal environment in which users perform their daily tasks. Thus, to accomplish their tasks, users need to continuously switch between local and remote resources and applications, often carrying the burden of coordinating and synchronizing these in a consistent way. In this demonstration, we describe a system, called ScholarLynk, that bridges the local and online worlds and allows users to manage both local and online resources in a uniform way and in collaboration with others.

Open Science As-A-Service For Research Communities: Openaire-Connect Project

OpenAIRE-Connect fosters transparent evaluation of results and facilitates reproducibility of science for research communities by enabling a scientific communication ecosystem supporting exchange of artefacts, software, packages of artefacts, and links between them across communities and across content providers. To this aim, OpenAIRE-Connect will introduce and implement the concept of Open Science as a Service (OSaaS) on top of the existing OpenAIRE infrastructure1, by delivering out-of-the-box, on-demand deployable tools in support of Open Science. This poster outlines the results of the development of the OSaaS tools for research communities, highlighting disciplinary use cases of the OpenAIRE dashboard for Research Communities.

Content Visualization of Scientific Corpora Using an Extensible Relational Database Implementation

A method for supervised classification and visualization of collections of scientific publications is presented. By integrating a text classification module, which leads to class probability estimation, along with a dimensionality reduction technique, which represents each class in the 2-D space, any collection of unlabelled documents can be visualized. The classification and visualization modules have been trained on three different datasets and respective categorizations. We provide an example of our system’s functionality by visualizing the content of collections of publications which share a common funding scheme. In order to implement this, we have developed a funding mining submodule which identifies documents of particular funding schemes. All the individual modules have been implemented using the madIS system, which provides data analysis functionalities via an extended relational database.

Supervised Content Visualization of Scientific Publications: A Case Study on the ArXiv Dataset

A supervised approach to visualization of collections of scientific documents is presented. We have implemented a text classification module, which leads to class probability estimations, along with a dimensionality reduction technique which represents each class in the 2-D space. Integrating those two procedures, any collection of unlabelled documents can be visualized. The arXiv dataset has been adopted for training the classification and visualization modules. We demonstrate the system’s functionality on a corpus of automatically detected publications of particular EU FP7 funding categories.