15,431 research outputs found
Experiments in Clustering Homogeneous XML Documents to Validate an Existing Typology
This paper presents some experiments in clustering homogeneous XMLdocuments
to validate an existing classification or more generally anorganisational
structure. Our approach integrates techniques for extracting knowledge from
documents with unsupervised classification (clustering) of documents. We focus
on the feature selection used for representing documents and its impact on the
emerging classification. We mix the selection of structured features with fine
textual selection based on syntactic characteristics.We illustrate and evaluate
this approach with a collection of Inria activity reports for the year 2003.
The objective is to cluster projects into larger groups (Themes), based on the
keywords or different chapters of these activity reports. We then compare the
results of clustering using different feature selections, with the official
theme structure used by Inria.Comment: (postprint); This version corrects a couple of errors in authors'
names in the bibliograph
Cross-lingual document retrieval categorisation and navigation based on distributed services
The widespread use of the Internet across countries has increased the need for access to document collections
that are often written in languages different from a userâs native language. In this paper we describe Clarity, a
Cross Language Information Retrieval (CLIR) system for English, Finnish, Swedish, Latvian and Lithuanian.
Clarity is a fully-fledged retrieval system that supports the user during the whole process of query formulation,
text retrieval and document browsing. We address four of the major aspects of Clarity: (i) the user-driven
methodology that formed the basis for the iterative design cycle and framework in the project, (ii) the system
architecture that was developed to support the interaction and coordination of Clarityâs distributed services, (iii)
the data resources and methods for query translation, and (iv) the support for Baltic languages. Clarity is an
example of a distributed CLIR system built with minimal translation resources and, to our knowledge, the only
such system that currently supports Baltic languages
Browsing a digital library: A new approach for the New Zealand digital library
Browsing is part of the information seeking process, used when information needs are ill-defined or unspecific. Browsing and searching are often interleaved during information seeking to accommodate changing awareness of information needs. Digital Libraries often support full-text search, but are not so helpful in supporting browsing. Described here is a novel browsing system created for the Greenstone software used by the New Zealand Digital Library that supports users in a more natural approach to the information seeking process. Š Springer-Verlag Berlin Heidelberg 2003
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
Hierarchical structuring of Cultural Heritage objects within large aggregations
Huge amounts of cultural content have been digitised and are available
through digital libraries and aggregators like Europeana.eu. However, it is not
easy for a user to have an overall picture of what is available nor to find
related objects. We propose a method for hier- archically structuring cultural
objects at different similarity levels. We describe a fast, scalable clustering
algorithm with an automated field selection method for finding semantic
clusters. We report a qualitative evaluation on the cluster categories based on
records from the UK and a quantitative one on the results from the complete
Europeana dataset.Comment: The paper has been published in the proceedings of the TPDL
conference, see http://tpdl2013.info. For the final version see
http://link.springer.com/chapter/10.1007%2F978-3-642-40501-3_2
Ensuring the discoverability of digital images for social work education : an online tagging survey to test controlled vocabularies
The digital age has transformed access to all kinds of educational content not only in text-based format but also digital images and other media. As learning technologists and librarians begin to organise these new media into digital collections for educational purposes, older problems associated with cataloguing and classifying non-text media have re-emerged. At the heart of this issue is the problem of describing complex and highly subjective images in a reliable and consistent manner. This paper reports on the findings of research designed to test the suitability of two controlled vocabularies to index and thereby improve the discoverability of images stored in the Learning Exchange, a repository for social work education and research. An online survey asked respondents to "tag", a series of images and responses were mapped against the two controlled vocabularies. Findings showed that a large proportion of user generated tags could be mapped to the controlled vocabulary terms (or their equivalents). The implications of these findings for indexing and discovering content are discussed in the context of a wider review of the literature on "folksonomies" (or user tagging) versus taxonomies and controlled vocabularies
CHORUS Deliverable 4.3c: Affect, appeal, and sentiment as factors influencing interaction with multi media information
The 7th CHORUS workshop on âAffect, Appeal, and Sentiment as Factors Influencing Interaction with
Multimedia Informationâ was held on May 28, 2009, Brussels, immediately following the Third CHORUS
Conference, hosted by the European Commission at their Avenue Beaulieu premises. Participation was limited
to invited speakers, and comprised sixteen researchers from fourteen research institutes in eight countries
From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web
A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results
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