Improving Semantic Relatedness in Paths for Storytelling with Linked Data on the Web
Laurens De Vocht, Christian Beecks, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle · Lecture notes in computer science · 2015
Algorithmic storytelling over Linked Data on the Web is a challenging task in which many graph-based pathfinding approaches experience issues with consistency regarding the resulting path that leads to a story. In order to mitigate arbitrariness and increase consistency, we propose to improve the semantic relatedness of concepts mentioned in a story by increasing the relevance of links between nodes through additional domain delineation and refinement steps. On top of this, we propose the implementation of an optimized algorithm controlling the pathfinding process to obtain more homogeneous search domain and retrieve more links between adjacent hops in each path. Preliminary results indicate the potential of the proposal. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.