14,210 research outputs found
Science Models as Value-Added Services for Scholarly Information Systems
The paper introduces scholarly Information Retrieval (IR) as a further
dimension that should be considered in the science modeling debate. The IR use
case is seen as a validation model of the adequacy of science models in
representing and predicting structure and dynamics in science. Particular
conceptualizations of scholarly activity and structures in science are used as
value-added search services to improve retrieval quality: a co-word model
depicting the cognitive structure of a field (used for query expansion), the
Bradford law of information concentration, and a model of co-authorship
networks (both used for re-ranking search results). An evaluation of the
retrieval quality when science model driven services are used turned out that
the models proposed actually provide beneficial effects to retrieval quality.
From an IR perspective, the models studied are therefore verified as expressive
conceptualizations of central phenomena in science. Thus, it could be shown
that the IR perspective can significantly contribute to a better understanding
of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric
Applying Science Models for Search
The paper proposes three different kinds of science models as value-added
services that are integrated in the retrieval process to enhance retrieval
quality. The paper discusses the approaches Search Term Recommendation,
Bradfordizing and Author Centrality on a general level and addresses
implementation issues of the models within a real-life retrieval environment.Comment: 14 pages, 3 figures, ISI 201
Search Process as Transitions Between Neural States
Search is one of the most performed activities on the World Wide
Web. Various conceptual models postulate that the search process
can be broken down into distinct emotional and cognitive states
of searchers while they engage in a search process. These models
significantly contribute to our understanding of the search process.
However, they are typically based on self-report measures, such as
surveys, questionnaire, etc. and therefore, only indirectly monitor
the brain activity that supports such a process. With this work,
we take one step further and directly measure the brain activity
involved in a search process. To do so, we break down a search
process into five time periods: a realisation of Information Need,
Query Formulation, Query Submission, Relevance Judgment and
Satisfaction Judgment. We then investigate the brain activity between
these time periods. Using functional Magnetic Resonance
Imaging (fMRI), we monitored the brain activity of twenty-four participants
during a search process that involved answering questions
carefully selected from the TREC-8 and TREC 2001 Q/A Tracks.
This novel analysis that focuses on transitions rather than states
reveals the contrasting brain activity between time periods – which
enables the identification of the distinct parts of the search process
as the user moves through them. This work, therefore, provides an
important first step in representing the search process based on the
transitions between neural states. Discovering more precisely how
brain activity relates to different parts of the search process will
enable the development of brain-computer interactions that better
support search and search interactions, which we believe our study
and conclusions advance
How users assess web pages for information-seeking
In this paper, we investigate the criteria used by online searchers when assessing the relevance of web pages for information-seeking tasks. Twenty four participants were given three tasks each, and indicated the features of web pages which they employed when deciding about the usefulness of the pages in relation to the tasks. These tasks were presented within the context of a simulated work-task situation. We investigated the relative utility of features identified by participants (web page content,structure and quality), and how the importance of these features is affected by the type of information-seeking task performed and the stage of the search. The results of this study provide a set of criteria used by searchers to decide about the utility of web pages for different types of tasks. Such criteria can have implications for the design of systems that use or recommend web pages
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
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