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.
Several researchers have investigated various techniques to construct conceptual hierarchies from social metadata. Most of the previous work utilizes tag statistics as evidence. Mika @cite_6 uses a graph-based approach to construct a network of related tags, projected from either a user-tag or object-tag association graphs; then induces broader narrower relations using betweenness centrality and set theory. Other works apply clustering techniques to tags, and use their co-occurrence statistics to produce conceptual hierarchies @cite_4 . Heymann and Garcia-Molina @cite_0 use centrality in the similarity graph of tags. The tag with the highest centrality is considered more abstract than one with a lower centrality; thus it should be merged to the hierarchy first, to guarantee that more abstract nodes are closer to the root. Schmitz @cite_20 applied a statistical subsumption model @cite_10 to induce hierarchical relations among tags. Since these works are based on tag statistics, they are likely to suffer from the popularity vs. generality'' problem, where a tag may be used more frequently not because it is more general, but because it is more popular among users.
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We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness. Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies. In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment. Socialtaggingis anincreasingly popularphenomenon with substantial impact on Music Information Retrieval (MIR). Tags express the personal perspectives of the user on the music items (such as songs, artists, or albums) they tagged. These personal perspectives should be taken into account inMIRtasksthatassessthesimilaritybetweenmusicitems. In this paper, we propose an novel approach for clustering music items represented in social tagging systems. Its characteristic is that it determines similarity between items by preserving the 3-way relationships among the inherent dimensions of the data, i.e., users, items, and tags. Conversely to existing approaches that use reductions to 2way relationships (between items-users or items-tags), this characteristic allows the proposed algorithm to consider the personal perspectives of tags and to improve the clustering quality. Due to the complexity of social tagging data, we focus on spectral clustering that has been proven effective in addressing complex data. However, existing spectral clustering algorithms work with 2-way relationships. To overcome this problem, we develop a novel data-modeling scheme and a tag-aware spectral clustering procedure that uses tensors (high-dimensional arrays) to store the multigraph structures that capture the personalised aspects of similarity. Experimental results with data from Last.fm indicate the superiority of the proposed method in terms of clustering quality over conventional spectral clustering approaches that consider only 2-way relationships.