Matches in ScholarlyData for { ?s ?p ?o. }
- 9 label "Modeling Online Presence".
- 9 sameAs 9.
- 9 hasAuthorList authorList.
- 9 isPartOf proceedings.
- 9 title "Modeling Online Presence".
- authorList type List.
- 10 creator csaba-veres.
- 10 type InProceedings.
- 10 label "LexiTags: An Interlingua for the Social Semantic Web".
- 10 sameAs 10.
- 10 abstract "The paper describes lexitags, a new approach to social semantic tagging whose goal is to allow users to easily enrich resources with semantic metadata from WordNet. This is a paradigm example of the Social Web and the Semantic Web working together: ordinary users help create the metadata so needed by the Semantic Web and in turn, Semantic Web technologies help those users get a richer experience from the Social Web. A family of simple user interfaces for lexitagging is described, as are some methods for the subsequent, automatic generation of lightweight ontologies. These ontologies are presented as an ideal interlingua for the Social Semantic Web.".
- 10 hasAuthorList authorList.
- 10 isPartOf proceedings.
- 10 keyword "WordNet".
- 10 keyword "folksonomy".
- 10 keyword "linked data".
- 10 keyword "metadata".
- 10 keyword "ontology".
- 10 keyword "rdf".
- 10 keyword "semantic web".
- 10 keyword "social web".
- 10 title "LexiTags: An Interlingua for the Social Semantic Web".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasLastItem item-1.
- item-1 type ListItem.
- item-1 hasContent csaba-veres.
- 12 creator amparo-e.-cano.
- 12 creator andrea-varga.
- 12 creator fabio-ciravegna.
- 12 type InProceedings.
- 12 label "Volatile Classification of Point of Interests based on Social Activity Streams".
- 12 sameAs 12.
- 12 abstract "Location sharing services(LSS) like Foursquare, Gowalla and Facebook Places gather information from millions of users who leave trails in locations (i.e. “chekins”) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collective perception of a POI can be used as a real-time dataset from which POI".
- 12 hasAuthorList authorList.
- 12 isPartOf proceedings.
- 12 keyword "DBPedia".
- 12 keyword "Points of Interest".
- 12 keyword "citizen sensing".
- 12 keyword "emerging semantics".
- 12 keyword "social awareness streams".
- 12 keyword "social data mining".
- 12 title "Volatile Classification of Point of Interests based on Social Activity Streams".
- 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 amparo-e.-cano.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent andrea-varga.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent fabio-ciravegna.
- item-3 previous item-2.
- 14 creator enrico-motta.
- 14 creator mathieu-daquin.
- 14 creator salman-elahi.
- 14 type InProceedings.
- 14 label "Semantic Technologies to Support the User-Centric Analysis of Activity Data".
- 14 sameAs 14.
- 14 abstract "There is currently a trend in giving access to users of online services to their own data. In this paper we consider in particular the data which is generated from the interactions between a user and an organisation online: activity data as held in website and web application logs. We show how, we use semantic technologies including RDF integration of log data, SPARQL and lightweight ontology reasoning to aggregate, integrate and analyse activity data from a user-centric point of view.".
- 14 hasAuthorList authorList.
- 14 isPartOf proceedings.
- 14 keyword "activity data".
- 14 keyword "ontology".
- 14 keyword "user-centric analysis".
- 14 title "Semantic Technologies to Support the User-Centric Analysis of Activity Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent mathieu-daquin.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent salman-elahi.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent enrico-motta.
- item-3 previous item-2.
- 17 creator abraham-bernstein.
- 17 creator patrick-minder.
- 17 type InProceedings.
- 17 label "Social Network Aggregation Using Face-Recognition".
- 17 sameAs 17.
- 17 abstract "With the rapid growth of the social web an increasing num- ber of people started to replicate their off-line preferences and lives in an on-line environment. Consequently, the social web provides an enormous source for social network data, which can be used in both commercial and research applications. However, people often take part in multiple social network sites and, generally, they share only a selected amount of data to the audience of a specific platform. Consequently, the interlinkage of social graphs from different sources getting increasingly important for applications such as social network analysis, personalization, or recommender systems. This paper proposes a novel method to enhance available user re-identification systems for social network data aggregation based on face-recognition algorithms. Furthermore, the method is combined with traditional text-based approaches in order to attempt a counter-balancing of the weaknesses of both methods. Using two samples of real-world social networks (with 1610 and 1690 identities each) we show that even though a pure face-recognition based method gets out- performed by the traditional text-based method (area under the ROC curve 0.986 vs. 0.938) the combined method significantly outperforms both of these (0.998, p = 0.0001) suggesting that the face-based method indeed carries complimentary information to raw text attributes.".
- 17 hasAuthorList authorList.
- 17 isPartOf proceedings.
- 17 keyword "cross-platform personalization".
- 17 keyword "face recognition".
- 17 keyword "interlink- age of social graphs".