Discovery Hub: exploratory search on remote linked data sources

  • Marie N.
  • Ribiere M.
  • Rodio F.

Exploratory search systems are built specifically to help the users during cognitive consuming search tasks like learning or topic investigation. The richness of the linked data datasets, their formal semantics and their connected nature can significantly help in this context. In this paper, we present a method to perform exploratory search on remote LOD sources. It uses a linked-data based recommender that identifies resources which are strongly related to the user’s topic of interest. This recommendation step aims to ease the exploratory search task by focusing the user’s attention on the results that convey a lot of knowledge. To perform it on remote data sources we propose a semantic spreading activation algorithm coupled with a graph sampling technique. This paper also details the analysis of the algorithm behavior over DBpedia and describes an implementation: the Discovery Hub application. Finally the results of a user evaluation are presented.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...