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      Open Annotations on Multimedia Web Resources

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          Abstract

          Many Web portals allow users to associate additional information with existing multimedia resources such as images, audio, and video. However, these portals are usually closed systems and user-generated annotations are almost always kept locked up and remain inaccessible to the Web of Data. We believe that an important step to take is the integration of multimedia annotations and the Linked Data principles. We present the current state of the Open Annotation Model, explain our design rationale, and describe how the model can represent user annotations on multimedia Web resources. Applying this model in Web portals and devices, which support user annotations, should allow clients to easily publish and consume, thus exchange annotations on multimedia Web resources via common Web standards.

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          Linked Data: Evolving the Web into a Global Data Space

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            Protein annotation as term categorization in the gene ontology using word proximity networks

            Background We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document justifying that annotation. We approached the task utilizing several combinations of two distinct methods: an unsupervised algorithm for expanding words associated with GO nodes, and an annotation methodology which treats annotation as categorization of terms from a protein's document neighborhood into the GO. Results The evaluation results indicate that the method for expanding words associated with GO nodes is quite powerful; we were able to successfully select appropriate evidence text for a given annotation in 38% of Task 2.1 queries by building on this method. The term categorization methodology achieved a precision of 16% for annotation within the correct extended family in Task 2.2, though we show through subsequent analysis that this can be improved with a different parameter setting. Our architecture proved not to be very successful on the evidence text component of the task, in the configuration used to generate the submitted results. Conclusion The initial results show promise for both of the methods we explored, and we are planning to integrate the methods more closely to achieve better results overall.
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              The Amsterdam hypermedia model: adding time and context to the Dexter model

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                Author and article information

                Journal
                28 February 2012
                Article
                1202.6354
                df476abb-4959-42c9-959f-3681f0732e59

                http://creativecommons.org/licenses/publicdomain/

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                Custom metadata
                20 pages
                cs.DL

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