71 research outputs found
Improving Retrieval Results with discipline-specific Query Expansion
Choosing the right terms to describe an information need is becoming more
difficult as the amount of available information increases.
Search-Term-Recommendation (STR) systems can help to overcome these problems.
This paper evaluates the benefits that may be gained from the use of STRs in
Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and
one giving general recommendations, and compare the retrieval performance of
these STRs. The main findings are: (1) QE with specific STRs leads to
significantly better results than QE with a general STR, (2) QE with specific
STRs selected by a heuristic mechanism of topic classification leads to better
results than the general STR, however (3) selecting the best matching specific
STR in an automatic way is a major challenge of this process.Comment: 6 pages; to be published in Proceedings of Theory and Practice of
Digital Libraries 2012 (TPDL 2012
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
Implications of Inter-Rater Agreement on a Student Information Retrieval Evaluation
This paper is about an information retrieval evaluation on three different
retrieval-supporting services. All three services were designed to compensate
typical problems that arise in metadata-driven Digital Libraries, which are not
adequately handled by a simple tf-idf based retrieval. The services are: (1) a
co-word analysis based query expansion mechanism and re-ranking via (2)
Bradfordizing and (3) author centrality. The services are evaluated with
relevance assessments conducted by 73 information science students. Since the
students are neither information professionals nor domain experts the question
of inter-rater agreement is taken into consideration. Two important
implications emerge: (1) the inter-rater agreement rates were mainly fair to
moderate and (2) after a data-cleaning step which erased the assessments with
poor agreement rates the evaluation data shows that the three retrieval
services returned disjoint but still relevant result sets.Comment: 7 pages, 3 figures, LWA 2010, Workshop I
A Science Model Driven Retrieval Prototype
This paper is about a better understanding on the structure and dynamics of
science and the usage of these insights for compensating the typical problems
that arises in metadata-driven Digital Libraries. Three science model driven
retrieval services are presented: co-word analysis based query expansion,
re-ranking via Bradfordizing and author centrality. The services are evaluated
with relevance assessments from which two important implications emerge: (1)
precision values of the retrieval service are the same or better than the
tf-idf retrieval baseline and (2) each service retrieved a disjoint set of
documents. The different services each favor quite other - but still relevant -
documents than pure term-frequency based rankings. The proposed models and
derived retrieval services therefore open up new viewpoints on the scientific
knowledge space and provide an alternative framework to structure scholarly
information systems.Comment: 8 pages, 4 figures, Cologne Conference on Interoperability and
Semantics in Knowledge Organizatio
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