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Enriching videos with light semantics
This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description
Collaboratively Patching Linked Data
Today's Web of Data is noisy. Linked Data often needs extensive preprocessing
to enable efficient use of heterogeneous resources. While consistent and valid
data provides the key to efficient data processing and aggregation we are
facing two main challenges: (1st) Identification of erroneous facts and
tracking their origins in dynamically connected datasets is a difficult task,
and (2nd) efforts in the curation of deficient facts in Linked Data are
exchanged rather rarely. Since erroneous data often is duplicated and
(re-)distributed by mashup applications it is not only the responsibility of a
few original publishers to keep their data tidy, but progresses to be a mission
for all distributers and consumers of Linked Data too. We present a new
approach to expose and to reuse patches on erroneous data to enhance and to add
quality information to the Web of Data. The feasibility of our approach is
demonstrated by example of a collaborative game that patches statements in
DBpedia data and provides notifications for relevant changes.Comment: 2nd International Workshop on Usage Analysis and the Web of Data
(USEWOD2012) in the 21st International World Wide Web Conference (WWW2012),
Lyon, France, April 17th, 201
A Semantic Web Annotation Tool for a Web-Based Audio Sequencer
Music and sound have a rich semantic structure which is so clear to the composer and the listener, but that remains mostly hidden to computing machinery. Nevertheless, in recent years, the introduction of software tools for music production have enabled new opportunities for migrating this knowledge from humans to machines. A new generation of these tools may exploit sound samples and semantic information coupling for the creation not only of a musical, but also of a "semantic" composition. In this paper we describe an ontology driven content annotation framework for a web-based audio editing tool. In a supervised approach, during the editing process, the graphical web interface allows the user to annotate any part of the composition with concepts from publicly available ontologies. As a test case, we developed a collaborative web-based audio sequencer that provides users with the functionality to remix the audio samples from the Freesound website and subsequently annotate them. The annotation tool can load any ontology and thus gives users the opportunity to augment the work with annotations on the structure of the composition, the musical materials, and the creator's reasoning and intentions. We believe this approach will provide several novel ways to make not only the final audio product, but also the creative process, first class citizens of the Semantic We
Linked Data approach for selection process automation in Systematic Reviews
Background: a systematic review identifies, evaluates and synthesizes the available literature on a given topic using scientific and repeatable methodologies. The significant workload required and the subjectivity bias could affect results. Aim: semi-automate the selection process to reduce the amount of manual work needed and the consequent subjectivity bias. Method: extend and enrich the selection of primary studies using the existing technologies in the field of Linked Data and text mining. We define formally the selection process and we also develop a prototype that implements it. Finally, we conduct a case study that simulates the selection process of a systematic literature published in literature. Results: the process presented in this paper could reduce the work load of 20% with respect to the work load needed in the fully manually selection, with a recall of 100%. Conclusions: the extraction of knowledge from scientific studies through Linked Data and text mining techniques could be used in the selection phase of the systematic review process to reduce the work load and subjectivity bia
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