1,470 research outputs found
Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework
The relevance of a document has many facets, going beyond the usual topical one, which have to be considered to satisfy a user's information need. Multiple representations of documents, like user-given reviews or the actual document content, can give evidence towards certain facets of relevance. In this respect polyrepresentation of documents, where such evidence is combined, is a crucial concept to estimate the relevance of a document. In this paper, we discuss how a geometrical retrieval framework inspired by quantum mechanics can be extended to support polyrepresentation. We show by example how different representations of a document can be modelled in a Hilbert space, similar to physical systems known from quantum mechanics. We further illustrate how these representations are combined by means of the tensor product to support polyrepresentation, and discuss the case that representations of documents are not independent from a user point of view. Besides giving a principled framework for polyrepresentation, the potential of this approach is to capture and formalise the complex interdependent relationships that the different representations can have between each other
On the probabilistic logical modelling of quantum and geometrically-inspired IR
Information Retrieval approaches can mostly be classed into probabilistic, geometric or logic-based. Recently, a new unifying framework for IR has emerged that integrates a probabilistic description within a geometric framework, namely vectors in Hilbert spaces. The geometric model leads naturally to a predicate logic over linear subspaces, also known as quantum logic. In this paper we show the relation between this model and classic concepts such as the Generalised Vector Space Model, highlighting similarities and differences. We also show how some fundamental components of quantum-based IR can be modelled in a descriptive way using a well-established tool, i.e. Probabilistic Datalog
A Probabilistic Framework for Information Modelling and Retrieval Based on User Annotations on Digital Objects
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. 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 to answer the question of how to exploit annotations for
information retrieval. It gives a first answer to the question if
retrieval effectiveness can be improved with annotations.
A survey of the "annotation universe" reveals some facets of
annotations; for example, they can be content level annotations
(extending the content of the annotation object) or meta level ones
(saying something about the annotated object). Besides the annotations
themselves, other objects created during the process of annotation can
be interesting for retrieval, these being the annotated fragments.
These objects are integrated 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
various objects 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 introduced. In POLAR, structured annotation
hypertexts can be modelled by means of probabilistic propositions and
four-valued 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 from subcontexts like annotations,
or relevance augmentation, where retrieval status values are propagated)
in conjunction with probabilistic inference, where P(d -> q), the probability
that a document d implies a query q, is estimated.
POLAR's semantics is based on possible worlds and accessibility
relations. It is implemented on top of four-valued probabilistic Datalog.
POLAR's core retrieval functionality, knowledge augmentation with
probabilistic inference, is evaluated for discussion and document
search. The experiments show that all relevant POLAR objects, merged
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%
Towards a geometrical model for polyrepresentation of information objects
The principle of polyrepresentation is one of the
fundamental recent developments in the field of
interactive retrieval. An open problem is how to
define a framework which unifies different as-
pects of polyrepresentation and allows for their
application in several ways. Such a framework
can be of geometrical nature and it may embrace
concepts known from quantum theory. In this
short paper, we discuss by giving examples how
this framework can look like, with a focus on in-
formation objects. We further show how it can be
exploited to find a cognitive overlap of different
representations on the one hand, and to combine
different representations by means of knowledge
augmentation on the other hand. We discuss the
potential that lies within a geometrical frame-
work and motivate its further developmen
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches
Mind the Gap: Another look at the problem of the semantic gap in image retrieval
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback
Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector
- …