=Paper=
{{Paper
|id=Vol-500/paper-5
|storemode=property
|title=Feedback-driven Ontology Reorganisation
|pdfUrl=https://ceur-ws.org/Vol-500/paper5.pdf
|volume=Vol-500
}}
==Feedback-driven Ontology Reorganisation==
<pdf width="1500px">https://ceur-ws.org/Vol-500/paper5.pdf</pdf>
<pre>
                       Abstract – PhD Seminar 2009

           Feedback-Driven Ontology Reorganisation

                                      Elmar P. Wach

                  Hummelsbüttler Hauptstraße 43, 22339 Hamburg, Germany
                     Technikerstraße 21a, 6020 Innsbruck, Austria
                      wach@elmarpwach.com, elmar.wach@sti2.at


    This research aims to create a semantic-based recommender system for e-
commerce applications that is capable to optimise itself by processing implicit user
feedback. E-commerce recommenders have become business relevant in filtering the
vast information available in the Internet (and e-shops) to present useful search results
and product recommendations to the customer.
    Most of times, ontologies in e-commerce recommenders are used for the user pro-
filing, and there has been put less effort in researching the use of domain ontologies.
Approaches in other domains like media (e.g. TV or newspapers) research the rec-
ommendation result with the different recommender categories (i.e. content-based fil-
tering, collaborative filtering, hybrid approaches). They neither address the question
of self-improvement of the recommendations nor enhance the system to an adaptive
one. While a formally described domain offers all the commonly known advantages,
it is, moreover, capable to adapt to new situations like a given (implicit) user feed-
back. Due to fast changing domains, markets, and customer behaviour, it is inefficient
and very expensive to manually process user feedbacks. These shortcomings are
aimed to be solved with an automated, adaptive system by combining the use of a
domain ontology with the processing of implicit user feedbacks to give better recom-
mendations to the user of the e-commerce recommender from time to time.
    The main research question is how the given feedback can lead to a self-
improvement of the ontology.
    Hence, the feedback has to be transformed by an improvement strategy into input
information that can be processed by the system. As the product categories used by
the recommender are represented in ontologies, the research to be done is in the field
of ontology reorganisation, evolution, and versioning. In favour, ontology label man-
agement and ABox axioms will be introduced for effectively reorganising the ontol-
ogy, which is the basis for achieving various customer interactions. According to the
respective results and reported feedbacks the ontology gets reorganised, and adapted
recommendations are presented to the customer.
    For validating this research a “real world” conversational content-based e-
commerce recommender system is used, the domain modelled is the product category
“digital cameras”, and two feedback channels – from the web application and from
user-generated content – are utilised. After each to be defined number of accom-
plished recommendation processes the impact of the ontology reorganisation on the
success criterion (e.g. the conversion rate) is analysed and evaluated at the application
level and reported to the ontology.

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