6,489 research outputs found
Semantic Tagging on Historical Maps
Tags assigned by users to shared content can be ambiguous. As a possible
solution, we propose semantic tagging as a collaborative process in which a
user selects and associates Web resources drawn from a knowledge context. We
applied this general technique in the specific context of online historical
maps and allowed users to annotate and tag them. To study the effects of
semantic tagging on tag production, the types and categories of obtained tags,
and user task load, we conducted an in-lab within-subject experiment with 24
participants who annotated and tagged two distinct maps. We found that the
semantic tagging implementation does not affect these parameters, while
providing tagging relationships to well-defined concept definitions. Compared
to label-based tagging, our technique also gathers positive and negative
tagging relationships. We believe that our findings carry implications for
designers who want to adopt semantic tagging in other contexts and systems on
the Web.Comment: 10 page
Enriching ontological user profiles with tagging history for multi-domain recommendations
Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer
Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations
Reason Maintenance - Conceptual Framework
This paper describes the conceptual framework for reason maintenance developed as part of
WP2
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
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