Algorithms for Generating Ontology Based Visualization from Semantic Search Results
ABSTRACT This paper presents algorithmic approaches for the transformation of list based search results from a semantic search engines (i.e. SemSearch) into a visual representation. We present the results and methodology of our preliminary implementation of the semantic search visualization engine (ViSS). ViSS, takes as input the semantic structure of semantic search results and reproduces them into a summarized, conclusive and meaningful graphical representation. Depending on the semantic search results our algorithms select the best visual representations.The current version of ViSS supports visual formats of directed graphs, charts and tables.
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ABSTRACT: The rise of the Social Web and advances in the Semantic Web provides unprecedented possibilities for the development of novel methods to enhance the information retrieval (IR) process by including varying degrees of semantics. We shed light on the corresponding notion of semantically-enhanced information retrieval by presenting state-of-the art techniques in related research areas. We describe techniques based on the main processes of a typical IR workflow and map them onto three main types of semantics, which vary from formal semantic knowledge representations and content-based semantics to social semantics emerging through usage and user interactions.Computer Science Review 05/2013; 8:25–46. DOI:10.1016/j.cosrev.2013.03.001