Anon Plangprasopchok

Constructing folksonomies from user-specified relations on flickr

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.

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.

A probabilistic approach for learning folksonomies from structured data

Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation [7] to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.

Modeling Social Annotation: A Bayesian Approach

Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, for example, Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by thousands of users can potentially be used to infer categorical knowledge, classify documents, or recommend new relevant information. Traditional text inference methods do not make the best use of social annotation, since they do not take into account variations in individual users’ perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes the interests of individual annotators into account in order to find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the synthetic and real-world data by comparing its performance to Latent Dirichlet Allocation on the topic extraction task. For the latter evaluation, we apply the model to infer topics of Web resources from social annotations obtained from Delicious in order to discover new resources similar to a specified one. Our empirical results demonstrate that the proposed model is a promising method for exploiting social knowledge contained in user-generated annotations.

Analyzing microblogs with affinity propagation

Recently, there has been a great deal of interest in analyzing inherent structures in posts on microblogs such as Twitter. While many works utilize a well-known topic modeling technique, we instead propose to apply Affinity Propagation [4] (AP) to analyze such a corpus, and we hypothesize that AP may provide different perspective to the traditional approach. Our preliminary analysis raises some interesting facts and issues, which suggest future research directions.

Exploiting Data Semantics to Discover, Extract, and Model Web Sources

We describe Deimos, a system that automatically discovers and models new sources of information.The system exploits four core technologies developed by our group that makes an end-to-end solution to this problem possible. First, given an example source, Deimos finds other similar sources online. Second, it invokes and extracts data from these sources. Third, given the syntactic structure of a source, Deimos maps its inputs and outputs to semantic types. Finally, it infers the sources semantic definition, i.e., the function that maps the inputs to the outputs. Deimos is able to successfully automate these steps by exploiting a combination of background knowledge and data semantics. We describe the challenges in integrating separate components into a unified approach to discovering, extracting and modeling new online sources. We provide an end-to-end validation of the system in two information domains to show that it can successfully discover and model new data sources in those domains.

Constructing folksonomies by integrating structured metadata

Aggregating many personal hierarchies into a common taxonomy, also known as a folksonomy, presents several challenges due to its sparseness, ambiguity, noise, and inconsistency. We describe an approach to folksonomy learning based on relational clustering that addresses these challenges by exploiting 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. We evaluate the learned folksonomy quantitatively by automatically comparing it to a reference taxonomy created by the Open Directory Project. Our empirical results suggest that the proposed approach improves upon the state-of-the-art folksonomy learning method.