We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semanticbased loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time.
As social tagging applications continuously gain in popularity, it becomes more and more accepted that models and tools for (re-)organizing tags are needed. Some first approaches are already practically implemented. Recently, activities to edit and organize tags have been described as "tag gardening". We discuss different ways to subsequently revise and reedit tags and thus introduce different "gardening activities"; among them models that allow gradually adding semantic structures to folksonomies and/or that combine them with more complex forms of knowledge organization systems. Moreover, power tags are introduced as tag gardening candidates and the personal tag repository TagCare is presented.
The SCOT(Social Semantic Cloud Of Tags) ontology is to semantically represent the structure and semantics of a collection of tags and to represent social networks among users based on the tags.
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 10.1007/978-3-642-22140-8_9.(2011)
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, page 136--149. Berlin/Heidelberg, Springer, (2011)
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, page 136--149. Berlin/Heidelberg, Springer, (2011)
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, page 136--149. Berlin/Heidelberg, Springer, (2011)
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, page 136--149. Berlin/Heidelberg, Springer, (2011)
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