Results 1 - 10
of
46
Laublet P.: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data
- Proceedings of the Linked Data on the Web (LDOW2008) workshop at WWW2008
, 2008
"... This paper introduces MOAT, a lightweight Semantic Web framework that provides a collaborative way to let Web 2.0 content producers give meanings to their tags in a machinereadable way. To achieve this goal, this approach relies on Linked Data principles, using URIs from existing resources to define ..."
Abstract
-
Cited by 75 (4 self)
- Add to MetaCart
(Show Context)
This paper introduces MOAT, a lightweight Semantic Web framework that provides a collaborative way to let Web 2.0 content producers give meanings to their tags in a machinereadable way. To achieve this goal, this approach relies on Linked Data principles, using URIs from existing resources to define these meanings. That way, users can create interlinked RDF data and let their content enter the Semantic Web, while solving some limits of free-tagging at the same time.
The state of the art in tag ontologies: A semantic model for tagging and folksonomies
- In Proceedings of the International Conference on Dublin Core and Metadata Applications
, 2008
"... There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variat ..."
Abstract
-
Cited by 33 (3 self)
- Add to MetaCart
There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.
Ontology of Folksonomy: A New Modeling Method
"... Ontologies and tagging systems are two different ways to organize the knowledge present in Web. The first one has a formal fundamental that derives from descriptive logic and artificial intelligence. The other one is simpler and it integrates heterogeneous contents, and it is based on the collaborat ..."
Abstract
-
Cited by 13 (1 self)
- Add to MetaCart
(Show Context)
Ontologies and tagging systems are two different ways to organize the knowledge present in Web. The first one has a formal fundamental that derives from descriptive logic and artificial intelligence. The other one is simpler and it integrates heterogeneous contents, and it is based on the collaboration of users in the Web 2.0. In this paper we propose a method to model tagging systems like folksonomies using ontologies. In our proposal, structured information (ontologies) can be extracted from knowledge built in a simple and collaborative way (folksonomies). Furthermore, we provide an analytical expression to evaluate the system requirements to store the derived ontology.
Bridging Ontologies and Folksonomies to Leverage Knowledge Sharing on the Social Web: a Brief Survey
"... Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and fo ..."
Abstract
-
Cited by 12 (4 self)
- Add to MetaCart
Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web. 1
Social Semantic Cloud of Tag: Semantic Model for Social Tagging
- in Agent and Multi-Agent Systems: Technologies and Applications, Springer Berlin
, 2008
"... Abstract. Tagging has proven to be a successful and efficient way for creating metadata through a human collective intelligence. It can be con-sidered not only an application of individuals for expressing one’s inter-ests, but also as a starting point for leveraging social connections through collab ..."
Abstract
-
Cited by 10 (2 self)
- Add to MetaCart
(Show Context)
Abstract. Tagging has proven to be a successful and efficient way for creating metadata through a human collective intelligence. It can be con-sidered not only an application of individuals for expressing one’s inter-ests, but also as a starting point for leveraging social connections through collaborative user participations. A number of users have contributed to tag resources in web sites such as Del.icio.us, Flickr etc. However, there is no uniform structure to describe tags and user’s activities. This makes difficult to share and represent tag data among people. The SCOT (Social Semantic Cloud of Tags) ontology is aimed to represent the structure and semantics of a set of tags and promotes their global sharing. The paper introduce the SCOT ontology and methods of its representation. 1
Enrichment and Ranking of the YouTube Tag Space and Integration with the Linked Data Cloud
, 2009
"... ..."
(Show Context)
Improving search and navigation by combining ontologies and social tags
- OTM Workshops, volume 5333 of Lecture Notes in Computer Science
, 2008
"... Abstract. The Semantic Web has the ambitious goal of enabling complex autonomous applications to reason on a machine-processable version of the World Wide Web. This, however, would require a coordinated effort not easily achievable in practice. On the other hand, spontaneous communities, based on s ..."
Abstract
-
Cited by 7 (0 self)
- Add to MetaCart
(Show Context)
Abstract. The Semantic Web has the ambitious goal of enabling complex autonomous applications to reason on a machine-processable version of the World Wide Web. This, however, would require a coordinated effort not easily achievable in practice. On the other hand, spontaneous communities, based on social tagging, recently achieved noticeable consensus and diffusion. The goal of the TagOnto system is to bridge between these two realities by automatically mapping (social) tags to more structured domain ontologies, thus, providing assistive, navigational features typical of the Semantic Web. These novel searching and navigational capabilities are complementary to more traditional search engine functionalities. The system, and its intuitive AJAX interface, are released and demonstrated on-line.
Understanding PLE as an Essential Component of the Learning Process. ED-Media AACE
, 2008
"... Abstract: This exploratory paper discusses the learning potential of PLE, not simply as a technological artefact but as an instrument of the learning process. It tries to identify the role of PLE in learning process and to point out the conditions to become more efficient learning instrument. Firstl ..."
Abstract
-
Cited by 7 (1 self)
- Add to MetaCart
(Show Context)
Abstract: This exploratory paper discusses the learning potential of PLE, not simply as a technological artefact but as an instrument of the learning process. It tries to identify the role of PLE in learning process and to point out the conditions to become more efficient learning instrument. Firstly, PLE should foster self-direction and reflexivity, and learning resources should be made available to the learner to support its metacognitive activity. Secondly, since PLE's are more and more inter-connected thanks to online tools, they raise the same issues of knowledge exchange as for online communities: difficulties to connect resources and to exploit the data available. Sharing should be improved by developing solutions bridging personal annotations (personomies) with their collections (folksonomies) and more structured knowledge representations (ontologies). Thirdly, research results should be used by institutions to improve the process of learning and teaching, and the design of VLEs.
Social Ranking: Finding Relevant Content in Web 2.0
- In Proceedings of International Workshop on Recommender Systems
, 2008
"... Abstract. Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely crea ..."
Abstract
-
Cited by 6 (1 self)
- Add to MetaCart
(Show Context)
Abstract. Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose a method to increase the efficiency of searches within Web 2.0 that is grounded on recommender system techniques. We measure users ’ similarity based on their past tag activity. We infer tags ’ relationships based on their association to content. We then propose a mechanism to answer a user’s query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. We evaluate the effectiveness of this mechanism when performing searches on the CiteULike dataset. 1
Automatically Structuring Domain Knowledge from Text: an Overview of Current Research
, 2011
"... This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.
