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There is generalagreementthat the quality of Machine Learning and
Kno-
edgeDiscoveryoutputstronglydependsnotonlyonthequalityofsourcedata
andsophisticationoflearningalgorithms,butalsoonadditional,task/domain
speci?c input provided by domain experts for the particular
session. There is however less agreement on whether, when and how
such input can and should e?ectively be formalized and reused as
explicit prior knowledge. In the ?rst ofthe two parts into which
the book is divided, we aimed to - vestigate current developments
and new insights on learning techniques that exploit prior
knowledge and on promising application areas. With respect to
application areas, experiments on bio-informatics / medical and Web
data environments are described. This part comprises a selection of
extended c- tributionstothe workshopPrior Conceptual Knowledge
inMachine Learning and Knowledge Discovery (PriCKL), held at
ECML/PKDD 2007 18th - ropean Conference on Machine Learning and
11th European Conference on
PrinciplesandPracticeofKnowledgeDiscoveryinDatabases).Theworkshop
is part of the activities of the "SEVENPRO - Semantic Virtual
Engineering for Product Design" project of the European 6th
Framework Programme. The second part of the book has been motivated
by the speci?cation of Web 2.0. We observe Web 2.0 as a powerful
means of promoting the Web as a social medium, stimulating
interpersonal communication and fostering the sharing of content,
information, semantics and knowledge among people. Chapters are
authored by participants to the workshop Web Mining 2.0,
heldatECML/PKDD2007.Theworkshophostedresearchontheroleofweb
mininginandfortheWeb2.0.Itispartoftheactivitiesoftheworkinggroups
"UbiquitousData-InteractionandDataCollection"and"HumanComputer
Interaction and Cognitive Modelling" of the Coordination Action
"KDubiq - Knowledge Discovery in Ubiquitous Environments" of the
European 6th Framework Programme.
Intelligent Information Access techniques attempt to overcome the
limi- tions of current search devices by providing personalized
information items and product/service recommendations. They
normally utilize direct or in- rect user input and facilitate the
information search and decision processes, according to user needs,
preferences and usage patterns. Recent devel- ments at the
intersection of Information Retrieval, Information Filtering,
MachineLearning,UserModelling,NaturalLanguageProcessingandHuman-
Computer Interaction o?er novel solutions that empower users to go
beyond
single-sessionlookuptasksandthataimatservingthemorecomplexrequi-
ment: "Tell me what I don't know that I need to know".
Information?ltering
systems,speci?callyrecommendersystems,havebeenrevolutionizingtheway
information seekers?nd what they want, becausethey e?ectively prune
large
informationspacesandhelpusersinselectingitemsthatbestmeettheirneeds
and preferences. Recommender systems rely strongly on the use of
various machine learning tools and algorithms for learning how to
rank, or predict user evaluation, of items. Information Retrieval
systems, on the other hand, also attempt to address similar
?ltering and ranking problems for pieces of information such as
links, pages, and documents. But they generally focus on the
development of global retrieval techniques, often neglecting
individual user needs and preferences. The book aims to investigate
current developments and new insights into methods, techniques and
technologies for intelligent information access from a
multidisciplinary perspective. It comprises six chapters authored
by part- ipants in the research event Intelligent Information
Access,heldinCagliari (Italy) in December 2008.
Intelligent Information Access techniques attempt to overcome the
limi- tions of current search devices by providing personalized
information items and product/service recommendations. They
normally utilize direct or in- rect user input and facilitate the
information search and decision processes, according to user needs,
preferences and usage patterns. Recent devel- ments at the
intersection of Information Retrieval, Information Filtering,
MachineLearning,UserModelling,NaturalLanguageProcessingandHuman-
Computer Interaction o?er novel solutions that empower users to go
beyond
single-sessionlookuptasksandthataimatservingthemorecomplexrequi-
ment: "Tell me what I don't know that I need to know".
Information?ltering
systems,speci?callyrecommendersystems,havebeenrevolutionizingtheway
information seekers?nd what they want, becausethey e?ectively prune
large
informationspacesandhelpusersinselectingitemsthatbestmeettheirneeds
and preferences. Recommender systems rely strongly on the use of
various machine learning tools and algorithms for learning how to
rank, or predict user evaluation, of items. Information Retrieval
systems, on the other hand, also attempt to address similar
?ltering and ranking problems for pieces of information such as
links, pages, and documents. But they generally focus on the
development of global retrieval techniques, often neglecting
individual user needs and preferences. The book aims to investigate
current developments and new insights into methods, techniques and
technologies for intelligent information access from a
multidisciplinary perspective. It comprises six chapters authored
by part- ipants in the research event Intelligent Information
Access,heldinCagliari (Italy) in December 2008.
There is generalagreementthat the quality of Machine Learning and
Kno-
edgeDiscoveryoutputstronglydependsnotonlyonthequalityofsourcedata
andsophisticationoflearningalgorithms, butalsoonadditional,
task/domain speci?c input provided by domain experts for the
particular session. There is however less agreement on whether,
when and how such input can and should e?ectively be formalized and
reused as explicit prior knowledge. In the ?rst ofthe two parts
into which the book is divided, we aimed to - vestigate current
developments and new insights on learning techniques that exploit
prior knowledge and on promising application areas. With respect to
application areas, experiments on bio-informatics / medical and Web
data environments are described. This part comprises a selection of
extended c- tributionstothe workshopPrior Conceptual Knowledge
inMachine Learning and Knowledge Discovery (PriCKL), held at
ECML/PKDD 2007 18th - ropean Conference on Machine Learning and
11th European Conference on
PrinciplesandPracticeofKnowledgeDiscoveryinDatabases).Theworkshop
is part of the activities of the "SEVENPRO - Semantic Virtual
Engineering for Product Design" project of the European 6th
Framework Programme. The second part of the book has been motivated
by the speci?cation of Web 2.0. We observe Web 2.0 as a powerful
means of promoting the Web as a social medium, stimulating
interpersonal communication and fostering the sharing of content,
information, semantics and knowledge among people. Chapters are
authored by participants to the workshop Web Mining 2.0,
heldatECML/PKDD2007.Theworkshophostedresearchontheroleofweb
mininginandfortheWeb2.0.Itispartoftheactivitiesoftheworkinggroups
"UbiquitousData-InteractionandDataCollection"and"HumanComputer
Interaction and Cognitive Modelling" of the Coordination Action
"KDubiq - Knowledge Discovery in Ubiquitous Environments" of the
European 6th Framework Programme.
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