9,677 research outputs found
Folks in Folksonomies: Social Link Prediction from Shared Metadata
Web 2.0 applications have attracted a considerable amount of attention
because their open-ended nature allows users to create light-weight semantic
scaffolding to organize and share content. To date, the interplay of the social
and semantic components of social media has been only partially explored. Here
we focus on Flickr and Last.fm, two social media systems in which we can relate
the tagging activity of the users with an explicit representation of their
social network. We show that a substantial level of local lexical and topical
alignment is observable among users who lie close to each other in the social
network. We introduce a null model that preserves user activity while removing
local correlations, allowing us to disentangle the actual local alignment
between users from statistical effects due to the assortative mixing of user
activity and centrality in the social network. This analysis suggests that
users with similar topical interests are more likely to be friends, and
therefore semantic similarity measures among users based solely on their
annotation metadata should be predictive of social links. We test this
hypothesis on the Last.fm data set, confirming that the social network
constructed from semantic similarity captures actual friendship more accurately
than Last.fm's suggestions based on listening patterns.Comment: http://portal.acm.org/citation.cfm?doid=1718487.171852
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
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
ïŹrst a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform
Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
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