Matches in ScholarlyData for { ?s ?p ?o. }
- 17 title "Social Network Aggregation Using Face-Recognition".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
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- item-1 type ListItem.
- item-1 hasContent patrick-minder.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent abraham-bernstein.
- item-2 previous item-1.
- 2 creator claudia-wagner.
- 2 creator markus-strohmaier.
- 2 creator yulan-he.
- 2 type InProceedings.
- 2 label "Pragmatic metadata matters: How data about the usage of data effects semantic user models".
- 2 sameAs 2.
- 2 abstract "Online social media such as wikis, blogs or message boardsenable large groups of users to generate and socialize around content.With increasing adoption of such media, the number of users who interactwith user-generated content and perform certain activities on it (suchas authoring content, replying to it or liking it) grows. As a result,the amount of pragmatic metadata - i.e. data about the usage of data- is growing as well. This paper sets out to explore if and how suchmetadata has an eect on the quality of semantic user models that canbe learned from user generated content. To this end, we use data from anonline message board (the Irish boards.ie), incorporate dierent types ofpragmatic metadata into topic modeling algorithms and evaluate themby comparing their predictive performance. Our evaluation is based onthe assumption that "better" user models will be able to predict futurecontent of users more accurately and will need less time and trainingdata. Our results suggest that not all types of pragmatic metadata areequally useful for acquiring accurate semantic user models, and sometypes of pragmatic metadata can even have detrimental eects. One ofour key observations is that semantic models of users which are based onthe content to which they replied (rather than the content which theyauthored) perform better than others. To the best of our knowledge, thisis the rst work that demonstrates an eect between pragmatic metadataon one hand, and the quality of semantic user models based on user-generated content on the other. Our results are relevant for scientistsinterested in topic modeling algorithms and pragmatic metadata, andfor engineers interested in semantic analysis of users and textual contentin social media.".
- 2 hasAuthorList authorList.
- 2 isPartOf proceedings.
- 2 keyword "Semantic Metadata".
- 2 keyword "Topic Models".
- 2 keyword "User Generated Content".
- 2 keyword "User Profiles".
- 2 title "Pragmatic metadata matters: How data about the usage of data effects semantic user models".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
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- item-1 type ListItem.
- item-1 hasContent claudia-wagner.
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- item-2 hasContent markus-strohmaier.
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- item-3 type ListItem.
- item-3 hasContent yulan-he.
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- 21 creator andreas-hotho.
- 21 creator dominik-benz.
- 21 creator gerd-stumme.
- 21 creator martin-atzmueller.
- 21 type InProceedings.
- 21 label "Towards Mining Semantic Maturity in Social Bookmarking Systems".
- 21 sameAs 21.
- 21 abstract "The existence of emergent semantics within social metadata(such as tags in bookmarking systems) has been proven by a large numberof successful approaches making the implicit semantic structures explicit.However, much less attention has been given to the factors which influencethe ’maturing’ process of these structures over time. A natural hypothesisis that tags become semantically more and more mature whenever manyusers use them in the same contexts. This would allow to describe atag by a specific and informative ’semantic fingerprint’ in the context oftagged resoures. However, the question of assessing the quality of suchfingerprints has been seldomly addressed.In this paper, we provide a systematic approach of mining semantic ma-turity profiles within folksonomy-based tag properties. Our ultimate goalis to provide a characterization of ’mature tags’. Additionally, we con-sider semantic information about the tags as a gold-standard source forthe characterization of the collected results. Our initial results suggestthat a suitable composition of tag properties allows the identification ofmore mature tag subsets. The presented work has implications for a number of problems related to social tagging systems, including tag ranking,tag recommendation, and the capturing of light-weight ontologies fromtagging data.".
- 21 hasAuthorList authorList.
- 21 isPartOf proceedings.
- 21 keyword "bookmark".
- 21 keyword "folksonomies".
- 21 keyword "semantics".
- 21 keyword "social web".
- 21 title "Towards Mining Semantic Maturity in Social Bookmarking Systems".
- authorList type List.
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- item-1 type ListItem.
- item-1 hasContent martin-atzmueller.
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- item-2 type ListItem.
- item-2 hasContent dominik-benz.
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- item-3 type ListItem.
- item-3 hasContent andreas-hotho.
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- item-4 type ListItem.
- item-4 hasContent gerd-stumme.
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- 3 creator adam-westerski.
- 3 creator carlos-a.-iglesias.
- 3 creator fernando-tapia-rico.
- 3 type InProceedings.
- 3 label "Linked Opinions: Describing Sentiments on the Structured Web of Data".
- 3 sameAs 3.
- 3 abstract "In the paper we report on the results of our experiments on the construction of the opinion ontology. Our aim is to show the benefits of publishing in the open, on the Web, the results of the opinion mining process in a structured form. On the road to achieving this, we attempt to answer the research question to what extent opinion information can be formalised in a unified way. Furthermore, as part of the evaluation, we experiment with the usage of Semantic Web technologies and show particular use cases that support our claims.".
- 3 hasAuthorList authorList.
- 3 isPartOf proceedings.
- 3 keyword "appliance".
- 3 keyword "idea management".
- 3 keyword "knowledge management".
- 3 keyword "ontology".
- 3 keyword "opinion mining".
- 3 keyword "structured data".
- 3 title "Linked Opinions: Describing Sentiments on the Structured Web of Data".
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- authorList hasFirstItem item-1.
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