SAVE-SD 2016 - 2016 WORKSHOP ON “SEMANTICS, ANALYTICS AND VISUALISATION: ENHANCING SCHOLARLY DATA” (SAVE-SD 2016)

Fter the great success of the past edition, we are pleased to announce SAVE-SD 2016, which wants to bring together publishers, companies and researchers from different fields (including Document and Knowledge Engineering, Semantic Web, Natural Language Processing, Scholarly Communication, Bibliometrics, Human-Computer Interaction, Information Visualisation, Bioinformatics, and Life Sciences) in order to bridge the gap between the theoretical/academic and practical/industrial aspects in regards to scholarly data. The following topics will be addressed:
semantics of scholarly data, i.e. how to semantically represent, categorise, connect and integrate scholarly data, in order to foster reusability and knowledge sharing;
analytics on scholarly data, i.e. designing and implementing novel and scalable algorithms for knowledge extraction with the aim of understanding research dynamics, forecasting research trends, fostering connections between groups of researchers, informing research policies, analysing and interlinking experiments and deriving new knowledge;
visualisation of and interaction with scholarly data, i.e. providing novel user interfaces and applications for navigating and making sense of scholarly data and highlighting their patterns and peculiarities.
Topics
We would encourage submission of papers covering, but not limited to, one or more of the following topics:
Semantics
Data models (e.g., ontologies, vocabularies, schemas) for the description of scholarly data and the linking between scholarly data and academic papers that report or cite them
Description of citations and citation networks
Theoretical models describing the rhetorical and argumentative structure of scholarly papers and their application in practice
Description and use of provenance information of scholarly data
From digital libraries of scholarly papers to Linked Open Datasets: models, applicability and challenges
Definition and description of scholarly publishing processes
Modelling licences for scholarly documents and data
Analytics
Assessing the quality and/or trust of scholarly data
Pattern discovery of scholarly data
Citation analysis and prediction
Scientific claims identification from textual contents
New indicators for measuring the quality and relevance of research
Comparison between standard metrics (e.g., h-index, impact factor, citation counting) and alternative metrics in real-case scenarios
Automatic or semi-automatic approaches to making sense of research dynamics
Content- and data-based semantic similarity of scholarly papers
Citation generation
Automatic semantic enhancement of existing scholarly libraries and papers
Reconstruction, forecasting and monitoring of scholarly data
Visualisation & Interaction
Novel user interfaces for interaction with paper, metadata, content, and data
Visualisation of citation networks according to multiple dimensions (e.g., citation counting, citation functions, kinds of citing/cited entities)
Visualisation of related papers or data according to multiple dimensions (semantic similarity of abstracts, keywords, etc.)
Applications for making sense of scholarly data
Usability studies on existing interfaces (e.g., Web sites, Web applications, smartphone apps) for browsing scholarly data
Scholarly data and ubiquity: accessing scholarly information from multiple devices (PC, tablet, smartphones)
Applications for the (semi-)automatic annotation of scholarly papers