Aemoo: Linked Data exploration based on Knowledge Patterns

Issue title: Visual exploration and analysis of Linked Data

Guest editors: Aba-Sah Dadzie and Emmanuel Pietriga

Article type: Research Article

Authors: Nuzzolese, Andrea Giovanni^{a; *} | Presutti, Valentina^a | Gangemi, Aldo^{a; c} | Peroni, Silvio^{a; b} | Ciancarini, Paolo^{a; b}

Affiliations: [a] STLab-ISTC, Consiglio Nazionale delle Ricerche, Rome, Italy | [b] Department of Computer Science and Engineering, University of Bologna, Bologna, Italy | [c] LIPN, Université Paris 13, Sorbone Cité, UMR CNRS, Paris, France

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: 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.

Keywords: Exploratory search, knowledge exploration, visual exploration, Knowledge Patterns, analysis of Linked Data

DOI: 10.3233/SW-160222

Journal: Semantic Web, vol. 8, no. 1, pp. 87-112, 2017