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                              DATABASE SEMINAR
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               Peter Wood, Birkbeck, University of London
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               Flexible Querying of Graph-Structured Data
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                  Tuesday, May 29, 2012 -- h 11:30
                      **** Sala riunioni ****
             Dipartimento di Informatica e Automazione
                       Universita' Roma Tre
                       Via Vasca Navale, 79
                           primo piano 
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                            ABSTRACT

We consider the problem of a user querying graph-structured data
without being fully aware of its structure.  Included with the data may
be an ontology defined using a simple subset of RDFS.  Using regular
expressions in queries provides a certain amount of flexibility when
querying such structures, but additional flexibility is gained if the
system also supports query relaxation and query approximation.  Query
relaxation refers to using the ontology in order to generalise a user's
query.  Query approximation involves modifications to a user's query so
that it matches the data.  In both cases, we define a notion of
distance which reflects how closely the executed query matches the
user's original query. Query answers are returned to the user ranked in
terms of this distance metric.  We use as a motivating example data
representing educational and career timelines of users who wish to find
what career and educational opportunities might be available to them in
the future.

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Peter Wood obtained his BSc and MSc in Computer Science from the
University of Cape Town (UCT), South Africa.  He obtained a PhD in
Computer Science in 1989 from the University of Toronto.  He then spent
nine years back at UCT, before joining King's College London in 1998.
He moved to Birkbeck in 2001.  His main interests throughout this time
have been in query languages and the efficient evaluation of queries,
including graph-based languages, visual languages, Datalog and XPath.
He has also done some work on ECA languages, graph algorithms, XML
compression, and most recently on top-k queries applied to recommender
systems.