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Invited
speakers
Amol
Deshpande
(University of Maryland, USA)
“PrDB: Managing large-scale correlated
probabilistic databases”
Increasing numbers of real-world application
domains are generating
data that is inherently noisy, incomplete, and
probabilistic in nature. Statistical
inference and probabilistic modeling often introduce another layer of
uncertainty on top of that.
Examples of such data include measurement data collected by sensor
networks, observation data in the context of social networks, scientific
and biomedical data, and data collected by various online
cyber-sources. Over the last few
years, numerous approaches have been proposed, and several systems built,
to integrate uncertainty into databases. However, these approaches
typically make simplistic and restrictive assumptions concerning the
types of uncertainties that can be represented. Most importantly, they
often make highly restrictive independence assumptions, and cannot easily
model rich correlations among the tuples or attribute values. Furthermore,
they typically lack support for specifying uncertainties at different
levels of abstractions, needed to handle large-scale uncertain datasets.
In this talk, I will begin by presenting our
work on building a probabilistic data management system, called PrDB,
aimed at supporting rich correlation structures often present in
real-world uncertain datasets. I will present the PrDB representation
model, which is based on probabilistic graphical models, and its key
abstractions, and show how these enable PrDB to support uncertainties
specified at various abstraction levels, from schema-level uncertainties
that apply to entire relations to tuple-specific uncertainties that apply
only to a specific tuple or a specific set of tuples. Query evaluation in
PrDB can be seen as equivalent to inference in graphical models, and I
will present some of the key novel techniques that we have developed to
efficiently evaluate various types of queries over large-scale
probabilistic databases. I will then briefly discuss our ongoing work and
some of the open research challenges in this area.
Thomas
Lukasiewicz (Oxford University, UK; TU Vienna, Austria)
“Uncertainty in the Semantic Web”
The Semantic Web (SW) has recently attracted
much attention, both from academia and industry, and is widely regarded
as the next step in the evolution of the World Wide Web. It aims at an
extension of the current Web by standards and technologies that help
machines to understand the information on the Web so that they can
support richer discovery, data integration, navigation, and automation of
tasks. The main ideas behind the SW are to add a machine-understandable
"meaning" to Web pages, to use ontologies for a precise definition
of shared terms in Web resources, to use KR technology for automated
reasoning from Web resources, and to apply cooperative agent technology
for processing the information of the Web. The SW consists of several
hierarchical layers, where the Ontology layer, in form of the OWL Web
Ontology Language, is currently the highest layer of sufficient maturity.
On top of and in integration with the Ontology layer, sophisticated
representation and reasoning capabilities for the Rules, Logic, and Proof
layers of the SW are currently being developed next. Managing uncertainty
and/or vagueness is another area, which has started to play an important
role in research towards the SW. There are a plethora of applications on
the Web, which require the management of uncertain and/or vague
information. Some of the most prominent technologies for dealing with
uncertainty are probably the ranking algorithms standing behind Web
search engines. More generally, formalisms for dealing with uncertainty
and/or vagueness are especially applied in ontology mapping, data
integration, information retrieval, and database querying. Vagueness and
imprecision also abound in multimedia information processing and
retrieval, and are an important aspect of natural language interfaces to
the Web.
In this talk, I will first give an overview of
important applications on the Web that require the management of
uncertainty and/or vagueness. I will then describe some own
representation and reasoning formalisms for the SW, which extend ontology
languages as well as hybrid languages integrating rules and ontologies by
the ability to handle uncertainty and/or vagueness.
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