An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations

Abstract

Abstract. This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's efficiency.

Leveraging Fragmental Semantic Data to Enhance Services Discovery

“…To build a useful domain ontology, we will try to keep only high-rank keywords (for example, the top 200 keywords in the domain) in the domain ontology. The method introduced in [7] can be used to automatically build a hierarchical ontology graph. An edge between two nodes represents the similarity between the two keywords, whose original value can be retrieved from the Wordnet.…”

“…Zhou et al [7] presents an algorithm that automatically generates hierarchical concept relationships from social annotations. In our research, we leverage the algorithm to generate the initial domain ontology hierarchy.…”

Building a Large-scale Software Programming Taxonomy from Stackoverflow

“…It builds the largest taxonomy which contains over 2.7 million classes from 1.7 billion web pages. For the social tag-based approaches, Mianwei Zhou et al [6] introduced an unsupervised model to automatically derive hierarchical semantics from social annotations. Jie Tang et al [7] proposed a learning approach to capture the hierarchical semantic structure of tags.…”

“…A recent literature [3] argued that the quality and the scale of taxonomy would significantly benefit the performance when applied in software engineering. On the other hand, there have been a considerable amount of research works on taxonomy construction [4], [5], [6], [7], [8]. The value of automatic taxonomy construction is two folds.…”

How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems

“…[18] presented an approach to capture emergent semantics from a folksonomy by deriving lightweight ontologies. In the sequel, several methods of capturing emergent semantics in the form of (i) measures of semantic tag relatedness [11], (ii) tag clusterings [19] and (iii) mapping tags to concepts in existing ontologies [20] were proposed. In our own previous work [2] we examined the effects of user behavior on emergent semantics in the Delicious system.…”