10 research outputs found
A Novel Combined Term Suggestion Service for Domain-Specific Digital Libraries
Interactive query expansion can assist users during their query formulation
process. We conducted a user study with over 4,000 unique visitors and four
different design approaches for a search term suggestion service. As a basis
for our evaluation we have implemented services which use three different
vocabularies: (1) user search terms, (2) terms from a terminology service and
(3) thesaurus terms. Additionally, we have created a new combined service which
utilizes thesaurus term and terms from a domain-specific search term
re-commender. Our results show that the thesaurus-based method clearly is used
more often compared to the other single-method implementations. We interpret
this as a strong indicator that term suggestion mechanisms should be
domain-specific to be close to the user terminology. Our novel combined
approach which interconnects a thesaurus service with additional statistical
relations out-performed all other implementations. All our observations show
that domain-specific vocabulary can support the user in finding alternative
concepts and formulating queries.Comment: To be published in Proceedings of Theories and Practice in Digital
Libraries (TPDL), 201
Query Expansion for Survey Question Retrieval in the Social Sciences
In recent years, the importance of research data and the need to archive and
to share it in the scientific community have increased enormously. This
introduces a whole new set of challenges for digital libraries. In the social
sciences typical research data sets consist of surveys and questionnaires. In
this paper we focus on the use case of social science survey question reuse and
on mechanisms to support users in the query formulation for data sets. We
describe and evaluate thesaurus- and co-occurrence-based approaches for query
expansion to improve retrieval quality in digital libraries and research data
archives. The challenge here is to translate the information need and the
underlying sociological phenomena into proper queries. As we can show retrieval
quality can be improved by adding related terms to the queries. In a direct
comparison automatically expanded queries using extracted co-occurring terms
can provide better results than queries manually reformulated by a domain
expert and better results than a keyword-based BM25 baseline.Comment: to appear in Proceedings of 19th International Conference on Theory
and Practice of Digital Libraries 2015 (TPDL 2015
Assessing Visualization Techniques for the Search Process in Digital Libraries
In this paper we present an overview of several visualization techniques to
support the search process in Digital Libraries (DLs). The search process
typically can be separated into three major phases: query formulation and
refinement, browsing through result lists and viewing and interacting with
documents and their properties. We discuss a selection of popular visualization
techniques that have been developed for the different phases to support the
user during the search process. Along prototypes based on the different
techniques we show how the approaches have been implemented. Although various
visualizations have been developed in prototypical systems very few of these
approaches have been adapted into today's DLs. We conclude that this is most
likely due to the fact that most systems are not evaluated intensely in
real-life scenarios with real information seekers and that results of the
interesting visualization techniques are often not comparable. We can say that
many of the assessed systems did not properly address the information need of
cur-rent users.Comment: 23 pages, 14 figures, pre-print to appear in "Wissensorganisation mit
digitalen Technologien" (deGruyter
How do practitioners, PhD students and postdocs in the social sciences assess topic-specific recommendations?
"In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled by recommender services. We call these services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender
(ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n preprocessed
recommendations from each recommender for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations are highly relevant to the researchers topic and can easily be integrated for search in Digital Libraries. The average precision for top ranked recommendations is 0.749 for author names, 0.743 for search terms and 0.728 for journal names.
The relevance distribution differs largely across topics and researcher types. Practitioners seem to favor author name recommendations while postdocs have rated author name recommendations the lowest. In the experiment the small postdoc group favors journal name recommendations." (author's abstract
How do practitioners, PhD students and postdocs in the social sciences assess topic-specific recommendations?
Abstract. In this paper we describe a case study where researchers in the social sciences (n=19) assess topical relevance for controlled search terms, journal names and author names which have been compiled by recommender services. We call these services Search Term Recommender (STR), Journal Name Recommender (JNR) and Author Name Recommender (ANR) in this paper. The researchers in our study (practitioners, PhD students and postdocs) were asked to assess the top n preprocessed recommendations from each recommender for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations are highly relevant to the researchers topic and can easily be integrated for search in Digital Libraries. The average precision for top ranked recommendations is 0.749 for author names, 0.743 for search terms and 0.728 for journal names. The relevance distribution di↵ers largely across topics and researcher types. Practitioners seem to favor author name recommendations while postdocs have rated author name recommendations the lowest. In the experiment the small postdoc group favors journal name recommendations
Suchunterstützung in akademischen Suchmaschinen
Der Beitrag schließt an die Ausarbeitungen zu wissenschaftlichen Suchmaschinen,
Query Understanding und Spezialsuchen der Bände 1 und 2 an. Es soll
gezeigt werden, wie durch die Konvergenz von qualitativem Fach-Content und
Suchtechnologien Mehrwerte gerade für Expertensuchen generiert werden können.
Die Beispiele aus unterschiedlichen akademischen Suchmaschinen (u.a. BASE,
Web of Knowledge, Pubmed, Scopus, sowiport, Google Scholar und Deutsche Digitale
Bibliothek) sollen das illustrieren, insofern sie grundsätzliche Fragen und
Lösungsvorschläge zeigen, die aber über den einzelnen Anwendungsfall hinausweisen.
Als in der Praxis erprobte State-of-the-Art-Dienste werden sie gleichwohl
mit konkreten Beschreibungen der informationstechnischen Grundlagen untermauert