LISC 2014 - 4th Workshop on Linked Science 2014? Making Sense Out of Data (LISC2014)

Traditionally scientific dissemination has been relying heavily on publications and presentations. The findings reported in these articles are often backed by large amounts of diverse data produced by complex experiments, computer simulations, and observations of physical phenomena. Although publications, methods and datasets are often related, due to this avalanche of data it remains extremely hard to correlate, reuse and leverage scientific data. Semantic Web technologies provide a promising means for publishing, sharing, and interlinking data to facilitate data reuse and the necessary correlation, integration, and synthesis of data across levels of theory, techniques and disciplines. However, even when these data become discoverable and accessible, significant challenges remain in making intelligent understandings of these data and scientific discoveries that we anticipated.
Our past three series (LISC2011, LISC2012 and LISC2013) have seen many novel ideas of using Semantic Web technologies for integrating scientific data (for example about real experiments or from simulations), or enabling reproducibility of research via online tools and Linked Data. The theme for LISC2014 is “Making Sense out of Data Through Linked Science”. Here we focus on new ways of discovering interesting patterns from scientific data, which could lead to research validation or identification of new hypotheses and acceleration of the scientific research cycle. We encourage both new results through making use of semantic reasoning or making innovative combination of existing technologies (such as visualization, data mining, machine learning, and natural language processing) with SW technologies to enable better understanding of data. One goal is to create both an incentive for scientists to consider the Linked Science approach for their scientific data management and an incentive for technologists from different disciplines to work together towards the vision of powering science with technologies.
Topics of Interest
Topics for submissions include, but are not limited to:
● Data profiling and quality profiling of Linked Science Data
● Pattern discovery
● Semantic query generation
● (Semi-)Automatic hypothesis generation
● Augmented human reasoning
● Interactive semantic systems
● Active discovery
● Methodology for explorative empirical research on linked data
● Citation generation
● Reasoning mechanisms for linking scientific datasets
● Integration of quantitative and qualitative scientific information
● Novel visualization of scientific data
● Scientific Information Retrieval
● Linked Data-based scientific experiments
● Integration of experimental data using Semantic Web
● Linked Citizen Science
● Formal representations of scientific data
● Ontologies for scientific information
● Semantic similarity in science applications
● Semantic integration of crowd sourced scientific data
● Provenance, quality, privacy and trust of scientific information
● Support for data publishing for sharing and reuse
● Sharing of experimental setups for replication and reproducibility studies
● Case studies on linked science, i.e., astronomy, biology, environmental and socio- economic impacts of global warming, statistics, environmental monitoring, cultural heritage, etc.