SemScape: Visualizating Semantic Web Data Landscapes with Cytoscape 3.0 Andrea Splendiani 12 , Andra Waagmeester 3 , Carina Haupt 4 , and Helena Deus 1 1 DERI, Galway, Ireland 2 intelliLeaf ltd, Cambridge, UK 3 Maastricht University, NL 4 Bonn-Aachen Institute for Information Technology, University of Bonn, DE Abstract. Core to the success of applying Semantic Web technologies (SWT) towards supporting Life Sciences research is the availability of tools that lower the entry barrier for adoption by biomedical researchers. Researchers need to easily and intuitively exploit and query the wealth of data that is available behind as SPARQL endpoints. Here, we present SemScape, a semantic-web enabled plugin for the popular network biol- ogy software Cytoscape. SemScape can be used to query any knowledge bases with a SPARQL endpoint by leveraging familiarity with existing software and intuitiveness of big data exploitation through a mechanism that encapsulates the complexity of data in parametric context depen- dent queries. We believe SemScape can provide a valuable resource both for data consumers and data publishers. 1 Introduction The Semantic web and linked data efforts in Life Sciences domains have produced a large amount of structured information ready for querying. However, the entry barrier for researchers willing to use this information on their daily tasks is too great still, posing a particular problem for user interaction and query assembly, especially in the Life Sciences domain. Despite the well-recognized advantages of Linked Data and RDF formalisms in enabling the seamless aggregation of data from multiple distributed sources, these formalisms inherently rely on a format that sets data free from the shackles of a predefined schema. As a side-effect, data represented in these formats is challenging to query since users cannot rely on organized data maps made available a-priori. While on- tologies can provide such a map, there is still a disconnect between ontologies proposed by the semantic web communities, and the data descriptors that are actually used in RDF representations. As such, it is difficult to find ontologies that encompasses and arbitrary mashup of data, which is typical scenario for the use of data published in RDF, in the Life Sciences and beyond. In the life sciences domain, the problem is further compounded by the realization that po- tential users of the data are typically not experts in semantic technologies, and ease of use as well as domain oriented interfaces are even more relevant. Hence the need for tools that provide an accessible interface to semantic web