This paper presents a novel approach to Linked Data exploration that uses Encyclopedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder.
Aemoo: Linked Data exploration based on Knowledge Patterns
IOS Press, Amsterdam , Paesi Bassi
Semantic web (Print) 8 (2017): 87–112. doi:10.3233/SW-160222
info:cnr-pdr/source/autori:Nuzzolese, Andrea Giovanni; Presutti, Valentina; Gangemi, Aldo; Peroni, Silvio; Ciancarini, Paolo/titolo:Aemoo: Linked Data exploration based on Knowledge Patterns/doi:10.3233/SW-160222/rivista:Semantic web (Print)/anno:2017/pag