10,152 research outputs found
Ordinary Search Engine Users Carrying Out Complex Search Tasks
Web search engines have become the dominant tools for finding information on
the Internet. Due to their popularity, users apply them to a wide range of
search needs, from simple look-ups to rather complex information tasks. This
paper presents the results of a study to investigate the characteristics of
these complex information needs in the context of Web search engines. The aim
of the study is to find out more about (1) what makes complex search tasks
distinct from simple tasks and if it is possible to find simple measures for
describing their complexity, (2) if search success for a task can be predicted
by means of unique measures, and (3) if successful searchers show a different
behavior than unsuccessful ones. The study includes 60 people who carried out a
set of 12 search tasks with current commercial search engines. Their behavior
was logged with the Search-Logger tool. The results confirm that complex tasks
show significantly different characteristics than simple tasks. Yet it seems to
be difficult to distinguish successful from unsuccessful search behaviors. Good
searchers can be differentiated from bad searchers by means of measurable
parameters. The implications of these findings for search engine vendors are
discussed.Comment: 60 page
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Web Site Recommendation Modelling Assisted by Ontologies Networks
Web site recommendation systems help to get high quality information. The modeling of recommendation system involves the combination of many features:metrics of quality, quality criteria, recommendation criteria, user profile, specific domain, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of this features. In this paper we propose a ontology network based process for web site recommendation modeling. This ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria. Particularly, this approach is illustrated for the health domain
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
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