Review of the state of the art: Discovering and Associating Semantics to Tags in Folksonomies (original) (raw)

2010-WI-Modeling Ontology of Folksonomy with Latent Semantics of Tags.pdf

Modeling ontology of folksonomy provides a way of learning light weight ontology's which is a hot topic investigated recently. Previous approaches for modeling ontology of folksonomy either ignores semantics (synonymy, hyponymy or polysemy) or do not simultaneously consider relationships between actors (users), concepts (tags) and instances (resources) or are based on the idea that title words are responsible for generating tags for resources. Latent semantics and user-tag dependencies instead of user-word dependencies however are extremely important. In this paper we address these problems by introducing a latent topic layer into the traditional tripartite Actor-Concept-Instance graph. We thus propose an Actor-Concept-Instance-Topic (ACIT) approach to model ontology from folksonomy in a unified way by directly using tags and users of resources. We illustrate on Bibsonomy dataset that our proposed approach ACIT outperforms title words based approaches Tag-Topic (TT) and (User-Word-Topic) UWT for modeling the ontology of folksonomy.

Modeling Ontology of Folksonomy with Latent Semantics of Tags

Abstract Modeling ontology of folksonomy provides a way of learning light weight ontology's which is a hot topic investigated recently. Previous approaches for modeling ontology of folksonomy either ignores semantics (synonymy, hyponymy or polysemy) or do not simultaneously consider relationships between actors (users), concepts (tags) and instances (resources) or are based on the idea that title words are responsible for generating tags for resources.

An Emergent Culture Model for Discerning Tag Semantics in Folksonomies

Social bookmarking sites as Flickr, del.icio.us, and CiteULike have incorporated the use of tags as way for users to retrieve photos, URLs, and citations in a way that is personally meaningful and which doesn't require learning taxonomies constructed by professionals. These tag sets, or folksonomies, have the potential to enhance interoperability among our information systems, especially those that use computational ontologies. Formal computational ontologies form the foundation for semantic interoperability, but seem to be insufficient in facilitating it because the ontologies developed for different information systems do not have an inherent mechanism for negotiating meaning or recognizing the natural evolution of a lexicon. Coupling folksonomies with formal ontologies holds potential for more productive semantic interoperability among systems. In order to reach that potential, we need to understand more clearly the process of discerning semantics in tag sets as entry points into the complex conceptual networks that generate meaning within cognition. This paper will explore that semantics involved in “emergent semantics” in tag sets and introduce an emergent culture model that will help clarify how folksonomies can be utilized in this endeavor.

Representing and sharing folksonomies with semantics

Journal of Information Science, 2010

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using 'tags' or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. However, there are a number of issues in relation to data sharing and interoperability when processing tag data across heterogeneous tagging platforms. In this paper we introduce a semantic tag model that aims to explicitly offer the necessary structure, semantics and relationships between tags. This approach provides an improved opportunity for representing tag data in the form of reusable constructs at a semantic level. We also demonstrate a prototype that consumes and makes use of shared tag metadata across heterogeneous sources.

Emergent Semantics from Folksonomies: A Quantitative Study

Journal on Data Semantics, 2006

Defining and using ontology to annotate web resources with semantic markups is generally perceived as the primary way to implement the vision of the Semantic Web. The ontology provides a shared and machine understandable semantics for web resources that agents and applications can utilize. This top-down approach (in the sense that an ontology is defined first on top of existing web resources and then used later to markup them), however, has a high barrier to entry and is difficult to scale up. In this paper, we investigate using a bottom-up approach for semantically annotating web resources as supported by the now widely popular social bookmarks services on the web where users can annotate and categorize web resources using “tags” freely choosen by the user without any pre-existing global semantic model. This kind of informal social categories is coined as “folksonomies”. We show how global semantics can be statistically inferred from the folksonomies to semantically annotate the web resources. The global semantic model also disambiguate the tags and group synonymous tags together. Finally, we show that there indeed are hierarchical relations among the emerged concepts in the folksonomy and it is plausible to further identify them if we use more advanced probabilistic models.

folk2onto: Bridging the gap between social tags and ontologies

Proc. of the 1st …, 2008

Ontologies are a useful and attractive tool for classifying documents and, in general, all types of resources. In fact, if all the documents in the web were classified according to a set of standard ontologies, the job of search engines, automatic document processors, etc. would be much easier. However, ontologies are too complex to be used by the general public and, so far, are used only by specialized users. Nonetheless, a more informal type of classifying resources is becoming increasingly popular amongst the general public: social tagging or folksonomies. Many popular websites (del.icio.us, Flickr, Technorati . . .) allow users to participate by annotating web content using social tags. Although they provide an easy way to collaboratively create knowledge, these tags are difficult to machine-process. In this paper, we propose bridging the gap between folksonomies and ontologies so that that the information in social tagging systems will be made easier to process. To that effect, we present the design and implementation of a software application, folk2onto, that can be trained to map social tags into an ontology.

08391 Working Group Summary--Analyzing Tag Semantics Across Tagging Systems}

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.