275 research outputs found
Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
Many social Web sites allow users to annotate the content with descriptive
metadata, such as tags, and more recently to organize content hierarchically.
These types of structured metadata provide valuable evidence for learning how a
community organizes knowledge. For instance, we can aggregate many personal
hierarchies into a common taxonomy, also known as a folksonomy, that will aid
users in visualizing and browsing social content, and also to help them in
organizing their own content. However, learning from social metadata presents
several challenges, since it is sparse, shallow, ambiguous, noisy, and
inconsistent. We describe an approach to folksonomy learning based on
relational clustering, which exploits structured metadata contained in personal
hierarchies. Our approach clusters similar hierarchies using their structure
and tag statistics, then incrementally weaves them into a deeper, bushier tree.
We study folksonomy learning using social metadata extracted from the
photo-sharing site Flickr, and demonstrate that the proposed approach addresses
the challenges. Moreover, comparing to previous work, the approach produces
larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on
Knowledge Discovery and Data Mining(KDD) 201
The horse before the cart: improving the accuracy of taxonomic directions when building tag hierarchies
Content on the Web is huge and constantly growing, and building taxonomies for such content can help with navigation and organisation, but building taxonomies manually is costly and time-consuming. An alternative is to allow users to construct folksonomies: collective social classifications. Yet, folksonomies are inconsistent and their use for searching and browsing is limited. Approaches have been suggested for acquiring implicit hierarchical structures from folksonomies, however, but these approaches suffer from the âpopularity-generalityâ problem, in that popularity is assumed to be a proxy for generality, i.e. high-level taxonomic terms will occur more often than low-level ones. To tackle this problem, we propose in this paper an improved approach. It is based on the HeymannâBenz algorithm, and works by checking the taxonomic directions against a corpus of text. Our results show that popularity works as a proxy for generality in at most 90.91% of cases, but this can be improved to 95.45% using our approach, which should translate to higher-quality tag hierarchy structure
Enhancing information retrieval in folksonomies using ontology of place constructed from Gazetteer information
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesFolksonomy (from folk and taxonomy) is an approach to user metadata creation where users describe information objects with a free-form list of keywords (âtagsâ). Folksonomy has have proved to be a useful information retrieval tool that support the emergence of âcollective intelligenceâ or âbottom-upâ light weight semantics. Since there are no guiding rules or restrictions on the users, folksonomy has some drawbacks and problems as lack of hierarchy, synonym control, and semantic precision. This research aims at enhancing information retrieval in folksonomy, particularly that of location information, by establishing explicit relationships between place name tags. To accomplish this, an automated approach is developed. The approach starts by retrieving tags from Flickr. The tags are then filtered to identify those that represent place names. Next, the gazetteer service that is a knowledge organization system for spatial information is used to query for the place names. The result of the search from the gazetteer and the feature types are used to construct an ontology of place. The ontology of place is formalized from place name concepts, where each place has a âPart-Ofâ relationship with its direct parent. The ontology is then formalized in OWL (Web Ontology Language). A search tool prototype is developed that extracts a place name and its parent name from the ontology and use them for searching in Flickr. The semantic richness added to Flickr search engine using our approach is tested and the results are evaluated
Adding Context to Social Tagging Systems
Many of the features of Web 2.0 encourage users to actively interact with each other. Social tagging systems represent one of the good examples that reflect this trend on the Web. The primary purpose of social tagging systems is to facilitate shared access to resources. Our focus in this paper is on the attempts to overcome some of the limitations in social tagging systems such as the flat structure of folksonomies and the absence of semantics in terms of information retrieval. We propose and develop an integrated approach, social tagging systems with directory facility, which can overcome the limitations of both traditional taxonomies and folksonomies. Our preliminary experiments indicate that this approach is promising and that the context provided by the directory facility improves the precision of information retrieval. As well, our synonym detection algorithm is capable of finding synonyms in social tagging systems without any external inputs
Comparing the hierarchy of author given tags and repository given tags in a large document archive
Folksonomies - large databases arising from collaborative tagging of items by
independent users - are becoming an increasingly important way of categorizing
information. In these systems users can tag items with free words, resulting in
a tripartite item-tag-user network. Although there are no prescribed relations
between tags, the way users think about the different categories presumably has
some built in hierarchy, in which more special concepts are descendants of some
more general categories. Several applications would benefit from the knowledge
of this hierarchy. Here we apply a recent method to check the differences and
similarities of hierarchies resulting from tags given by independent
individuals and from tags given by a centrally managed repository system. The
results from out method showed substantial differences between the lower part
of the hierarchies, and in contrast, a relatively high similarity at the top of
the hierarchies.Comment: 10 page
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Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies
Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search
Ontologies and tag-statistics
Due to the increasing popularity of collaborative tagging systems, the
research on tagged networks, hypergraphs, ontologies, folksonomies and other
related concepts is becoming an important interdisciplinary topic with great
actuality and relevance for practical applications. In most collaborative
tagging systems the tagging by the users is completely "flat", while in some
cases they are allowed to define a shallow hierarchy for their own tags.
However, usually no overall hierarchical organisation of the tags is given, and
one of the interesting challenges of this area is to provide an algorithm
generating the ontology of the tags from the available data. In contrast, there
are also other type of tagged networks available for research, where the tags
are already organised into a directed acyclic graph (DAG), encapsulating the
"is a sub-category of" type of hierarchy between each other. In this paper we
study how this DAG affects the statistical distribution of tags on the nodes
marked by the tags in various real networks. We analyse the relation between
the tag-frequency and the position of the tag in the DAG in two large
sub-networks of the English Wikipedia and a protein-protein interaction
network. We also study the tag co-occurrence statistics by introducing a 2d
tag-distance distribution preserving both the difference in the levels and the
absolute distance in the DAG for the co-occurring pairs of tags. Our most
interesting finding is that the local relevance of tags in the DAG, (i.e.,
their rank or significance as characterised by, e.g., the length of the
branches starting from them) is much more important than their global distance
from the root. Furthermore, we also introduce a simple tagging model based on
random walks on the DAG, capable of reproducing the main statistical features
of tag co-occurrence.Comment: Submitted to New Journal of Physic
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