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Keyword-based access to relational data: To reproduce, or to not reproduce?

Alex Badan, Luca Benvegnù, Matteo Biasetton, Giovanni Bonato, Alessandro Brighente, Stefano Marchesin, Alberto Minetto, Leonardo Pellegrina, Alberto Purpura, Riccardo Simionato, Matteo Tessarotto, Andrea Tonon and Nicola Ferro
Conference Paper Proc. 25th Italian Symposium on Advanced Database Systems (SEBD 2017), pages 166-177, 2017.

Abstract

We investigate the problem of the reproducibility of keywordbased access systems to relational data. These systems address a challenging and important issue, i.e. letting users to access in natural language databases whose schema and instance are possibly unknown. However, neither there are shared implementations of state-of-the-art algorithms nor experimental results are easily replicable. We explore the difficulties in reproducing such systems and experimental results by implementing from scratch several state-of-the-art algorithms and testing them on shared datasets

Towards open-source shared implementations of keyword-based access systems to relational data

Alex Badan, Luca Benvegnù, Matteo Biasetton, Giovanni Bonato, Alessandro Brighente, Alberto Cenzato, Piergiorgio Ceron, Giovanni Cogato, Stefano Marchesin, Alberto Minetto, Leonardo Pellegrina, Alberto Purpura, Riccardo Simionato, Nicolò Soleti, Matteo Tessarotto, Andrea Tonon, Federico Vendramin and Nicola Ferro
Workshop Paper Proceedings of 1st EDBT/ICDT Workshop on Keyword-based Access and Ranking at Scale (KARS 2017) - Proc. of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017). CEUR Workshop Proceedings , Vol. 1810, ISSN 1613-0073, 2017

Abstract

Keyword-based access systems to relational data address a challenging and important issue, i.e. letting users to exploit natural language to access databases whose schema and instance are possibly unknown. Unfortunately, there are almost no shared implementations of such systems and this hampers the reproducibility of experimental results. We explore the difficulties in reproducing such systems and share implementations of several state-of-the-art algorithms

An Adaptive Cross-Site User Modelling Platform for Cultural Heritage Websites

Maristella Agosti, Séamus Lawless, Stefano Marchesin and Vincent Wade
Conference Paper Proc. 13th Italian Research Conference on Digital Libraries (IRCDL 2017), in press, 2017

Abstract

This paper discusses an adaptive cross-site user modelling platform for cultural heritage websites. The objective is to present the overall design of this platform that allows for information exchange techniques, which can be subsequently used by websites to provide tailored personalisation to users that request it. The information exchange is obtained by implementing a third party user model provider that, through the use of an API, interfaces with custom-built module extensions of websites based on the Web-based Content Management System (WCMS) Drupal. The approach is non-intrusive, not hindering the browsing experience of the user, and has a limited impact on the core aspects of the websites that integrate it. The design of the API ensures user’s privacy by not disclosing personal browsing information to non-authenticated users. The user can enable/disable the cross-site service at any time