DISSERTATION ABSTRACT

A Probabilistic Framework for Information
Modelling and Retrieval Based on User
Annotations on Digital Objects
Ingo Frommholz
University of Duisburg-Essen∗
ingo@is.inf.uni-due.de
http://www.is.inf.uni-due.de/staff/ingo.html.en
Annotations are a means to make critical remarks, to explain and comment things, to add
notes and give opinions, and to relate objects. Nowadays, they can be found in digital libraries
and collaboratories, for example as a building block for scientific discussion on the one hand or
as private notes on the other hand. We further find them in product reviews, scientific databases
and many “Web 2.0” applications; even well-established concepts like emails can be regarded
as annotations in a certain sense. Digital annotations can be (textual) comments, markings (i.e.
highlighted parts) and references to other documents or document parts. Since annotations convey
information which is potentially important to satisfy a user’s information need, this thesis tries toanswer the question of how to exploit annotations for information retrieval. It introduces POLAR,
a probabilistic, logic-based framework for annotation-based retrieval, and gives a first answer tothe question if retrieval effectiveness can be improved with annotations.
A survey of the “annotation universe” reveals some facets of annotations; for example, they canbe content level annotations (extending the content of the annotated object) or meta level ones(saying something about the annotated object). Furthermore, there might be positive or negativeannotations. Besides the annotations themselves, other objects created during the process ofannotation can be interesting for retrieval, these being the annotated fragments. These objects areintegrated into an object-oriented model comprising digital objects such as structured documents
and annotations as well as fragments. In this model, the different relationships among the variousobjects are reflected. From this model, the basic data structure for annotation-based retrieval,the structured annotation hypertext, is derived.
In order to thoroughly exploit the information contained in structured annotation hypertexts,
a probabilistic, object-oriented logical framework called POLAR is developed. In POLAR, structured annotation hypertexts can be modelled by means of probabilistic propositions and fourvalued logics. POLAR allows for specifying several relationships among annotations and annotated(sub)parts or fragments. Queries can be posed to extract the knowledge contained in structured
annotation hypertexts. POLAR supports annotation-based retrieval, i.e. document and discussion
search, by applying an augmentation strategy (knowledge augmentation, propagating propositions∗

Now at Department of Computing Science, University of Glasgow. Email: ingo@dcs.gla.ac.uk.

from subcontexts like annotations, or relevance augmentation, where retrieval status values arepropagated) in conjunction with probabilistic inference, where P (d → q), the probability that adocument d implies a query q, is estimated. POLAR’s semantic is based on possible worlds andaccessibility relations. It is implemented on top of four-valued probabilistic Datalog, which allows
for the reflection of the polarity of annotations and for the consideration of possible inconsistentknowledge in discussion threads.
POLAR’s core retrieval functionality, knowledge augmentation with probabilistic inference, isevaluated for discussion and document search. The experiments show that all relevant POLAR
objects (annotation targets, fragments and content annotations) are able to increase retrieval
effectiveness when used as a context for discussion or document search. Additional experiments
reveal that we can determine the polarity of annotations with an accuracy of around 80% by
applying machine learning techniques.
Available online at http://duepublico.uni-due.de/servlets/DerivateServlet/Derivate-21216