This document discusses three case studies that demonstrate the usefulness of knowledge organization systems (KOS) in information retrieval applications. The case studies show that: 1) Controlled vocabulary searches using term mappings between domains improved search effectiveness compared to searches without mappings. 2) Query expansion using recommendations from discipline-specific search term recommendation services led to better search results than using a general recommendation service. 3) Usage of search term recommendations in an interactive IR system was found to positively correlate with successful search outcomes in subsequent search steps.

1. Measuring the usefulness of
Knowledge Organization Systems
in
Information Retrieval applications
Philipp Mayr
Observatory for Knowledge Organisation
Systems KNOWeSCAPE workshop, Valletta,
Malta
February 01, 2017

2. GESIS
• We are developing interactive information retrieval systems for
searching indexed literature and data sets
• We follow the principle „research-based service“; develop research
prototypes, test and evaluate them and implement the features which
are working for the end users
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3. Intro
• Typical difficulties in searching digital libraries (DL)
– Vagueness between search and indexing terms
– How to support searchers with controlled vocabulary?
• Assumption: a user’s search (experience) should
improve by using Knowledge Organization Systems
(KOS):
– Vague search tasks
– Unfamiliar fields
– Cross domain searches
• Case studies to demonstrate the effectiveness of
KOS in different search scenarios 3

4. Case Study 1: Information retrieval
experiment
• Intra- and interdisciplinary cross-
concordances in the project
KoMoHe
– Social Sciences-SocSci; SocSci-
Economics; SocSci-Psychology; Politics-
Economics; Medicine-Psychology, …
• Information retrieval evaluation of the
mappings (effectiveness of
intellectual mapping)
4Controlled terms

5. Case Study 1
• How effective are the mappings in an actual search?
Does the application of term mappings (TT) improve
search over a non-transformed subject (i.e. controlled
vocabulary) search (CT)?
• Real queries, only equivalence relations, 13 thesaurus
mappings
5
Mayr/Petras 2008
• Overlap and more
identical terms in
intradisciplinary
mappings
• Interdisciplinary
mappings made the
strongest effect

6. Case Study 2: Information retrieval
experiment
• Discipline-specific Search-Term-
Recommendation (STR) Services in IRM
project
• Are recommendations from discipline
specific STRs better suited for query
expansion than general ones?
• Co-occurence of terms
in title/description and
assigned controlled
terms
• 17 STR services
– 16 discipline-spec.
– 1 global
6Lüke et al. 2012

7. Case Study 2
• Are recommendations from discipline specific STRs better suited for
query expansion (QE) than general ones?
• 100 topics from the GIRT corpus, top 4 recommendations to expand the
original query
• gSTR = global STR; tSTR = topical STR; bSTR = best-performing STR
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Lüke et al. 2012
• QE with specific STRs leads to significantly better results than QE with a
general STR
• Selecting the best matching specific STR in an automatic way is a major

8. Case Study 3: Interactive IR
experiment
• Measuring the utility and
performance of Search Term
Recommendation (STR) Services in
AMUR project
• Logfile-based evaluation of STR
usage and later search session
success
• We defined positive signals (export,
save, email, full text …) in the
system
enter_search_term→select_term_from_reco
mmender→search→view_record_1→view_r
ecord_2→view_record_3→export_record
• Analysis of one year of log data
8
Hienert/Mutschke 2016

9. Case Study 3
• Usage of the STR significantly often implicates the
occurrence of positive signals during the following session
steps
9
Hienert/Mutschke 2016

10. Conclusions
• Information retrieval and
interactice IR settings are able to
demonstrate the utility of KOS
usage (usefulness)
– In experimental settings
– In user evaluations
• Each methodology has pros and
cons
– Effort and significance in small user
studies
– Too controlled, system-based,
without real users
• Terminology mapping projects 10
IR
Interactive
IR
Availability of corpora high low
Reproducibility high low
Control high low
Measures medium medium
Effort low high
Significance medium medium
Generalisability medium medium
Realistic Scenario no high

11. Outlook
• Integrate different recommender systems in
real retrieval tasks (search sessions)
• Use and evaluate recommenders for query
expansion and as dynamic features in IR, in
the retrieval process (AMUR project)
• Develop new measures of utility of
recommender systems
– E.g. measure task completion rates or goal
satisfaction
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12. References
• Hienert, D. & Mutschke, P. (2016). A Usefulness-based Approach for
Measuring the Local and Global Effect of IIR Services. In Proceedings of
the 2016 ACM on Conference on Human Information Interaction and
Retrieval (CHIIR '16). ACM, New York, NY, USA, 153-162.
http://dx.doi.org/10.1145/2854946.2854962
• Lüke, T., Schaer, P., & Mayr, P. (2012). Improving Retrieval Results with
discipline-specific Query Expansion. In International Conference on Theory
and Practice of Digital Libraries (TPDL 2012) (pp. 408–413). Paphos,
Cyprus: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-642-
33290-6_44
• Mayr, P., & Petras, V. (2008). Cross-concordances: terminology mapping
and its effectiveness for information retrieval. In 74th IFLA World Library and
Information Congress. Québec, Canada: IFLA. Retrieved from
http://www.ifla.org/IV/ifla74/papers/129-Mayr_Petras-en.pdf
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13. Thank you
Contact:
Dr Philipp Mayr
GESIS - Leibniz Institute for the Social Sciences,
Germany
Email: philipp.mayr@gesis.org
Twitter: @philipp_mayr
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