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https://ceur-ws.org/Vol-919/preface.pdf
Proceedings of the
International Workshop on
Semantic Technologies meet Recommender
Systems & Big Data
SeRSy 2012
co-located with the:
11th International Semantic Web Conference (ISWC 2012)
November 11, 2012, Boston, USA
Workshop Organizers and Program Chairs
Marco de Gemmis
Department of Computer Science
University of Bari Aldo Moro, Italy
Tommaso Di Noia
Electrical & Electronics Engineering Department
Politecnico of Bari, Italy
Pasquale Lops
Department of Computer Science
University of Bari Aldo Moro, Italy
Thomas Lukasiewicz
Department of Computer Science
University of Oxford, United Kingdom
Giovanni Semeraro
Department of Computer Science
University of Bari Aldo Moro, Italy
Copyright © 2012 for the individual papers by the papers' authors. Copying permitted only
for private and academic purposes.
This volume is published and copyrighted by:
Marco de Gemmis
Tommaso Di Noia
Pasquale Lops
Thomas Lukasiewicz
Giovanni Semeraro
This volume appeared online as CEUR-WS.org/Vol-919 at CEUR Workshop Proceedings,
ISSN 1613-0073
ii
Preface
These are the proceedings of the First Workshop on Semantic Technologies meet
Recommender Systems & Big Data (SeRSy 2012), held in conjunction with the 11th
International Semantic Web Conference (ISWC 2012).
People generally need more and more advanced tools that go beyond those
implementing the canonical search paradigm for seeking relevant information. A new search
paradigm is emerging, where the user perspective is completely reversed: from finding to
being found.
Recommender Systems may help to support this new perspective, because they have
the effect of pushing relevant objects, selected from a large space of possible options, to
potentially interested users. To achieve this result, recommendation techniques generally rely
on data referring to three kinds of objects: users, items and their relations. The widespread
success of Semantic Web techniques, creating a Web of interoperable and machine readable
data, can be also beneficial for recommender systems. Indeed, more and more semantic data
are published following the Linked Data principles, that enable to set up links between objects
in different data sources, by connecting information in a single global data space – the Web of
Data. Today, the Web of Data includes different types of knowledge represented in a
homogeneous form – sedimentary one (encyclopedic, cultural, linguistic, common-sense, …)
and real-time one (news, data streams, …). This data might be useful to interlink diverse
information about users, items, and their relations and implement reasoning mechanisms that
can support and improve the recommendation process.
The challenge is to investigate whether and how this large amount of wide-coverage
and linked semantic knowledge can significantly improve the search process in those tasks
that cannot be solved merely through a straightforward matching of queries and documents.
Such tasks involve finding information from large document collections, categorizing and
understanding that information, and producing some product, such as an actionable decision.
Examples of such tasks include understanding a health problem in order to make a medical
decision, or simply deciding which laptop to buy. Recommender systems support users
exactly in those complex tasks.
The primary goal of the workshop is to showcase cutting edge research in the
intersection of semantic technologies and recommender systems, by taking the best of the two
worlds. This combination may provide the Semantic Web community with important real-
world scenarios where its potential can be effectively exploited into systems performing
complex tasks.
We wish to thank all authors who submitted papers and all workshop participants for
fruitful discussions. We would like to thank the program committee members and external
referees for their timely expertise in carefully reviewing the submissions. We would also like
to thank our invited speaker Ora Lassila for his interesting and stimulating talk.
October 2012
The Workshop Chairs
Marco de Gemmis
Tommaso Di Noia
Pasquale Lops
Thomas Lukasiewicz
Giovanni Semeraro
iii
Program Committee
Fabian Abel L3S Research Centre – Germany
Claudio Bartolini HP Labs @ Palo Alto – USA
Marco Brambilla Politecnico di Milano – Italy
Andrea Calì Birkbeck, University of London – UK
Ivan Cantador Universidad Autónoma de Madrid – Spain
Pablo Castells Universidad Autónoma de Madrid – Spain
Federica Cena University of Turin – Italy
Bettina Fazzinga Università della Calabria – Italy
Tim Furche University of Oxford – UK
Nicola Henze Leibniz Universität Hannover – Germany
Dominikus Heckmann DFKI – Germany
Leo Iaquinta Univ. di Milano Bicocca – Italy
Roberto Mirizzi HP Labs @ Palo Alto – USA
Ahsan Morshed CSIRO – Australia
Enrico Motta Open University in Milton Keynes – UK
Cataldo Musto Università di Bari "Aldo Moro" – Italy
Fedelucio Narducci Univ. di Milano Bicocca – Italy
Vito Claudio Ostuni Politecnico of Bari – Italy
Alexandre Passant seevl.net – Ireland
Gerardo I. Simari University of Oxford – UK
Markus Zanker Alpen–Adria–Universität Klagenfurt – Austria
iv
Table of Contents
Link Prediction in Multi-relational Graphs using Additive Models. . . . . . . . . . . . . . . . . 1-12
Xueyan Jiang, Volker Tresp, Yi Huang and Maximilian Nickel
Driver Recommendations of POIs using a Semantic Content-based Approach. . . . . . . . 13-24
Rahul Parundekar and Kentaro Oguchi
Semantic Network-driven News Recommender Systems: a Celebrity Gossip
Use Case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25-36
Marco Fossati, Claudio Giuliano and Giovanni Tummarello
Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted
by DBpedia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37-48
Vito Claudio Ostuni, Tommaso Di Noia, Roberto Mirizzi, Romito Davide and
Eugenio Di Sciascio
Ontology-based Rules for Recommender Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49-60
Jeremy Debattista, Simon Scerri, Ismael Rivera and Siegfried Handschuh
Ontology-centric Decision Support. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61-72
Marco Rospocher and Luciano Serafini
RING: A Context Ontology for Communication Channel Rule-based Recommender
System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73-82
Miguel Lagares Lemos, Daniel Villanueva Vasquez, Mateusz Radzimski,
Angel Lagares Lemos and Juan Miguel Gómez-Berbís
v