1,721,066 research outputs found
Automatic document-level semantic metadata annotation using folksonomies and domain ontologies
Full text linkThe last few years have witnessed a fast growth of the concept of Social Software. Be it video sharing such as YouTube, photo sharing such as Flickr, community building such as MySpace, or social bookmarking such as del.icio.us. These websites contain valuable user-generated metadata called folksonomies. Folksonomies are ad hoc, light-weight knowledge representation artefacts to describe web resources using people’s own vocabulary. The cheap metadata contained in such websites presents potential opportunities for us (researchers) to benefit from. This thesis presents a novel tool that uses folksonomies to automatically generate metadata with educational semantics in an attempt to provide semantic annotations to bookmarked web resources, and to help in making the vision of the Semantic Web a reality. The tool comprises two components: the tags normalisation process and the semantic annotation process. The tool uses the del.icio.us social bookmarking service as a source for folksonomy tags. The tool was applied to a case study consisting of a framework for evaluating the usefulness of the generated semantic metadata within the context of a particular eLearning application. This implementation of the tool was evaluated over three dimensions: the quality, the searchability and the representativeness of the generated semantic metadata. The results show that folksonomy tags were acceptable for creating semantic metadata. Moreover, folksonomy tags showed the power of aggregating people’s intelligence. The novel contribution of this work is the design of a tool that utilises folksonomy tags to automatically generate metadata with fine gained and extra educational semantics
FAsTA: A Folksonomy-Based Automatic Metadata Generator
No full textFolksonomies provide a free source of keywords describing web resources, however, these keywords are free form and unstructured. In this paper, we describe a novel tool that converts folksonomy tags into semantic metadata, and present a case study consisting of a framework for evaluating the usefulness of this metadata within the context of a particular eLearning application. The evaluation shows the number of ways in which the generated semantic metadata adds value to the raw folksonomy tags
Harnessing the wisdom of crowds: how to semantically annotate web resource using folksonomies
No full textThis paper proposes a system for automatically adding semantic metadata to web resources. This can be done by converting people generated tags (a.k.a folksonomies) associated with bookmarked web sites saved at del.icio.us bookmarking service to a formal metadata representation, hence RDF
Delicious Learning Resources
No full textThis paper presents a novel approach to semantic annotation of learning resources using a blend of folksonomy keywords and ontology-based semantic annotation. This approach attempts to match folksonomy terms (after normalization), from bookmarked resources saved in a bookmarking service such as del.icio.us, against terms in the ontology (which operates as a controlled vocabulary). The approach will provide an attribute-value relationship that is semantically rich and adds ‘intelligence’ to search for learning resources in a specific subject domain
Folksonomies versus Automatic Keyword Extraction: An Empirical Study
No full textThis paper reports on an evaluation of the keywords produced by Yahoo API context-based term extractor compared to a folksonomy set for the same website. The evaluation process is made in two ways: automatically, by measuring the percentage of overlap between the folksonomy set and Yahoo keywords set; and subjectively, by asking a human indexer to rate the quality of the generated keywords from both systems. The result of the experiment will be considered as an evidence for the rich semantics of folksonomies, thus, they can be used in generating “Semantic Metadata” for annotating web resources stored in del.icio.us
Creating structure from disorder: using folksonomies to create semantic metadata
No full textThis paper reports on an on-going research project to create educational semantic metadata out of folksonomies. The paper describes a simple scenario for the usage of the generated semantic metadata in teaching, and describes the ‘FolksAnnotation’ tool which applies an organization scheme to tags in a specific domain of interest. The contribution of this paper is to describe an evaluation framework which will allow us to validate our claim that folksonomies are potentially a rich source of metadata
Measuring the Semantic Value of Folksonomies
Full text linkSemantic Metadata, which describes the meaning of documents, can be produced either manually or else semi-automatically using information extraction techniques. Manual techniques are expensive if they rely on skilled cataloguers, but a possible alternative is to make use of community produced annotations such as those collected in folksonomies. This paper reports on an experiment that we carried out to validate the assumption that folksonomies carry more semantic value than keywords extracted by machines. The experiment has been carried-out in two ways: automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set; and subjectively, by asking a human indexer to evaluate the quality of the generated keywords from both systems. The result of the experiment can be considered as evidence for the rich semantics of folksonomies, demonstrating that folksonomies used in the del.icio.us bookmarking service can be used in the process of generating semantic metadata to annotate web resources
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Full text linkFinding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
FolksAnnotation: A Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies
Full text linkThere are many resources on the Web which are suitable for educational purposes. Unfortunately the task of identifying suitable resources for a particular educational purpose is difficult as they have not typically been annotated with educational metadata. However, many resources have now been annotated in an unstructured manner within contemporary social bookmaking services. This paper describes a novel tool called ‘FolksAnnotation’ that creates annotations with educational semantics from the del.icio.us bookmarking service, guided by appropriate domain ontologies
Replacing the Monolithic LOM: A Folksonomic Approach
Full text linkIEEE-LOM is a well-known metadata standard for describing learning resources. However, many problems are associated with this kind of representation, which include the number of fields to be filled and the amount of time needed to fill them. To overcome this hurdle, we propose the use of cheap unstructured metadata to create structured semantic metadata, this metadata is called folksonomy. In this paper we show an approach that uses folksonomy tags to create structured metadata using semantic web technologies. The generated folksonomic metadata are then evaluated against a human expert annotation
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