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Tag Similarity in Folksonomies
"... ABSTRACT. Folksonomies- collections of user-contributed tags, proved to be efficient in reducing the inherent semantic gap. However, user tags are noisy; thus, they need to be processed before they can be used by further applications. In this paper, we propose an approach for bootstrapping semantics ..."
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semantics from folksonomy tags. Our goal is to automatically identify semantically related tags. The approach is based on creating probability distribution for each tag based on co-occurrence statistics. Subsequently, the similarity between two tags is determined by the distance between their corresponding
J.A.: Mining tag similarity in folksonomies
- SMUC
, 2011
"... Folksonomies are becoming increasingly popular, both among users who find them simple and intuitive to use, and scien-tists as interesting research objects. Folksonomies can be viewed as large informal sources of semantics. Harnessing the semantics for search or concept extraction requires us to be ..."
Abstract
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Cited by 2 (0 self)
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to be able to recognize linguistic similarity between tags. In this paper we propose an approach that uses a combination of morpho-syntactic and semantic similarity measures with-out using any external linguistic resources to mine tag pairs that can be reduced to base tags. Our approach is based
Semantic analysis of tag similarity measures in collaborative tagging systems
, 2008
"... Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learni ..."
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Cited by 24 (1 self)
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learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation
A Weighted Tag Similarity Measure Based on a Collaborative Weight Model
, 2010
"... The problem of measuring semantic relatedness between social tags remains largely open. Given the structure of social bookmarking systems, similarity measures need to be addressed from a social bookmarking systems perspective. We address the fundamental problem of weight model for tags over which ev ..."
Abstract
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Cited by 1 (0 self)
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The problem of measuring semantic relatedness between social tags remains largely open. Given the structure of social bookmarking systems, similarity measures need to be addressed from a social bookmarking systems perspective. We address the fundamental problem of weight model for tags over which
Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure
"... Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this is ..."
Abstract
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Cited by 2 (0 self)
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-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy
Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure
"... Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this is ..."
Abstract
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-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy
A unified architecture for natural language processing: Deep neural networks with multitask learning
, 2008
"... We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and sem ..."
Abstract
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Cited by 340 (13 self)
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We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically
Evaluating Similarity Measures for Emergent Semantics of Social Tagging
"... Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, ..."
Abstract
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Cited by 71 (8 self)
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. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we
Data Clustering: 50 Years Beyond K-Means
, 2008
"... Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and m ..."
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Cited by 294 (7 self)
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and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering
Results 1 - 10
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