=Paper=
{{Paper
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|title=Preface
|pdfUrl=https://ceur-ws.org/Vol-781/preface.pdf
|volume=Vol-781
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==Preface==
https://ceur-ws.org/Vol-781/preface.pdf
Proceedings of the 2nd Workshop on
Semantic Personalized Information Management:
Retrieval and Recommendation
SPIM 2011
Workshop Organizers and Program Chairs
Marco de Gemmis
Department of Computer Science,
University of Bari “Aldo Moro”, Italy.
Ernesto William De Luca
School IV – Electrical Engineering and Computer Science,
Berlin Institute of Technology, Germany.
Tommaso Di Noia
Electrical & Electronics Engineering Department,
Technical University of Bari, Italy.
Aldo Gangemi
Italian National Research Council (ISTC-CNR),
Institute for Cognitive Sciences and Technology, Italy.
Michael Hausenblas
National University of Ireland (NUIG), Galway.
DERI – Digital Enterprise Research Institute, Ireland.
Pasquale Lops
Department of Computer Science,
University of Bari “Aldo Moro”, Bari, Italy.
Thomas Lukasiewicz
Department of Computer Science,
University of Oxford, United Kingdom
Till Plumbaum
School IV – Electrical Engineering and Computer Science,
Berlin Institute of Technology, Germany.
Giovanni Semeraro
Department of Computer Science,
University of Bari “Aldo Moro”, Italy.
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These are the Proceedings of the 2nd Workshop on Semantic Personalized Information
Management: Retrieval and Recommendation (SPIM 2011), held in conjunction with
the 10th International Semantic Web Conference (ISWC 2011). The workshop aims at
improving the exchange of ideas between the different communities involved in the
research on semantic personalized information management and covers a wide range
of interdisciplinary topics: semantic social web, machine learning hybridized with
semantics for personalization, techniques for (semantic) user modeling, recommender
systems, personalized information retrieval, semantic interaction, use of semantic
technologies in UI/HCI, linked data consumption for PIM, semantic search and
exploratory browsing.
The workshop received an enthusiastic feedback from the SPIM community
with a total of 20 submitted papers. 13 papers have been accepted and this highlights
an increasing interest in the workshop topics. Indeed, during the first workshop edition
in 2010, 7 papers were presented. This is a clear indication that "semantic personalized
information management" is a very interesting and timely topic.
The set of accepted papers substantially covers the proposed topics, with some
additional specific subjects: folksonomies, interaction and knowledge patterns for
automatic explanation, CMS, business intelligence, etc. We can coarsely group the 13
accepted papers as follows:
Recommendation and classification:
• Improving Tag-based Resource Recommendation with Association Rules on
Folksonomies
• Finding similar research papers using language models
• Towards Ranking in Folksonomies for Personalized Recommender Systems in
E-Learning
• User's food preference extraction for cooking recipe recommendation
• Performance Measures for Multi-Graded Relevance
• A Dimensionality Reduction Approach for Semantic Document Classification
• Personalized Filtering of Twitter Stream
User modelling
• Classifying Users and Identifying their Interests in Folksonomies
• User Modeling for the Social Semantic Web
Various PIM support
• Personalization in Skipforward, an Ontology-Based Distributed Annotation
System
• A Model for Assisting Business Users along Analytical Processes
• A Privacy Preference Manager for the Social Semantic Web
• User-sensitive Explanations under a Knowledge Pattern Lens
In the following, we summarize the background motivation for the scientific and
practical relevance of the workshop.
Motivation
Finding and managing information is a crucial task in our everyday life, and especially
on the Web, the user is confronted with a huge amount of information. Therefore,
search engines have become an essential tool for the majority of users for finding
information on the Web.
While search engines implementing the canonical search paradigm are
adequate for most ad-hoc keyword-based retrieval tasks, they reach limits when user
needs have to be satisfied in a personalized way. Today’s search engines have a very
limited consideration of individual user’s preferences or context given by previous
searches for distinguishing the relevance of a document with respect to the meaning of
a user query (experiences so far seem restricted to massive log analyses and
experimental things like Google Squared, which however does not address
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personalization). With the advent of the Semantic Web, new opportunities emerge for
semantic information retrieval systems to better match user needs. Next-generation
search engines should implement a novel search paradigm, 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 to potentially interested users. An emerging approach is to use Web 2.0 and
Semantic Web technologies to model information about users, their needs and
preferences, their context and relations, and to incorporate data from other resources
like Linked Open Data (http://linkeddata.org). 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 search and recommendation process,
better satisfying the users’ information need.
A new generation of systems is emerging, which fully understand the items
they deal with, and new methods for modelling user information, combining user
content and Semantic Web resources, as well as new algorithms for processing that
data, are thus needed.
Why the topic is of particular interest at this time
More and more real-world applications in different areas are going to integrate
recommender systems to personalize retrieval issues, results, and in general the user
interaction.
Successful workshops and international conferences in the last few years
(ACM Recommender Systems, User Modelling, AAAI, ECAI, IJCAI, SIGIR) show
the growing interest and research potential of these systems. Recent developments of
the Semantic Web community offer novel strategies to represent data about users,
items and their relations that might improve the current state of the art of search and
recommendation systems.
The challenge is to investigate whether and how this large amount of wide-
coverage and linked semantic knowledge can significantly improve the
search/recommendation process in those tasks that cannot be solved merely through a
straightforward matching of queries and documents.
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.
October2011
The workshop chairs
Marco de Gemmis
Ernesto William De Luca
Tommaso Di Noia
Aldo Gangemi
Michael Hausenblas
Pasquale Lops
Thomas Lukasiewicz
Till Plumbaum
Giovanni Semeraro
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SPIM 2011 Program Committee
Program Committee
Fabian Abel L3S Research Center
Sahin Albayrak DAI-Labor, Technische Universität Berlin, Germany
Claudio Bartolini
Marco Brambilla Politecnico di Milano
Andrea Cali University of London, Birkbeck College
Charles Callaway University of Haifa
Ivan Cantador Universidad Autonoma de Madrid
Pablo Castells Universidad Autónoma de Madrid
Federica Cena Department of Computer Science, University of Torino
Philipp Cimiano
Mathieu D’Aquin Knowledge Media Institute, the Open University
Marco De Gemmis Dipartimento di Informatica - University of Bari
Ernesto William De Luca Technische Universität Berlin
Tommaso Di Noia Politecnico di Bari
Nicola Fanizzi Dipartimento di Informatica, Università di Bari
Bettina Fazzinga DEIS - University of Calabria
Miriam Fernandez Knowledge Media Institute
Tim Furche University of Munich
Aldo Gangemi CNR-ISTC
Michael Hausenblas Digital Enterprise Research Institute (DERI), NUI Galway
Tom Heath Talis Systems Ltd
Dominikus Heckmann
Eelco Herder
Dietmar Jannach TU Dortmund
Pasquale Lops University of Bari
Thomas Lukasiewicz Oxford University
Till Plumbaum DAI-Labor, Technische Universität Berlin, Germany
Georg Ruß Otto-von-Guericke-University of Magdeburg
Alan Said TU Berlin
Giovanni Semeraro Dipartimento di Informatica - University of Bari
Wolf Siberski L3S Research Center
Armando Stellato University of Rome, Tor Vergata
Tania Tudorache Stanford University
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Table of Contents
Personalized Filtering of the Twitter Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Pavan Kapanipathi, Fabrizio Orlandi, Amit Sheth and Alexandre Passant
User‐sensitive Explanations under a Knowledge Pattern Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Alessandro Adamou, Paolo Ciancarini, Aldo Gangemi and Valentina Presutti
Towards Ranking in Folksonomies for Personalized Recommender Systems in E‐Learning . . . 22
Mojisola Anjorin, Christoph Rensing and Ralf Steinmetz
Improving Tag‐based Resource Recommendation with Association Rules on Folksonomies. . . 26
Beldjoudi Samia, Hassina Seridi and Catherine Faron Zucker
A Model for Assisting Business Users along Analytical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Corentin Follenfant, David Trastour and Olivier Corby
A Privacy Preference Manager for the Social Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Owen Sacco and Alexandre Passant
Performance Measures for Multi‐Graded Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Christian Scheel, Andreas Lommatzsch and Sahin Albayrak
Classifying Users and Identifying User Interests in Folksonomies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Elias Zavitsanos, George Vouros and Georgios Paliouras
User Modeling for the Social Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
Till Plumbaum, Songxuan Wu, Ernesto William De Luca and Sahin Albayrak
Personalization in Skipforward, an Ontology‐Based Distributed Annotation System . . . . . . . . . . .90
Malte Kiesel and Florian Mittag
User's food preference extraction for cooking recipe recommendation . . . . . . . . . . . . . . . . . . . . . . . 98
Mayumi Ueda, Mari Takahata and Shinsuke Nakajima
Finding similar research papers using language models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
German Hurtado Martin, Steven Schockaert, Chris Cornelis and Helga Naessens
A Dimensionality Reduction Approach for Semantic Document Classification . . . . . . . . . . . . . . . 114
Oskar Ahlgren, Pekka Malo, Ankur Sinha, Pekka Korhonen and Jyrki Wallenius
5