Folksonomies provide a comfortable way to search and browse the blogosphere. As the tags in the blogosphere are sparse, ambiguous and too general, this paper proposes both a supervised and an unsupervised approach tha...
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ISBN:
(纸本)9781595939913
Folksonomies provide a comfortable way to search and browse the blogosphere. As the tags in the blogosphere are sparse, ambiguous and too general, this paper proposes both a supervised and an unsupervised approach that extract tags from posts using a tag semantic network. We evaluate the two methods on a blog dataset and observe an improvement in F1-measure from 0.23 to 0.50 when compared to the base line system.
We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relate...
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We present a graph-theoretic analysis of the topological structures underlying the collaborative knowledge bases Wikipedia and Wiktionary, which are promising uprising resources in Natural Language processing. We cont...
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We present a graph-theoretic analysis of the topological structures underlying the collaborative knowledge bases Wikipedia and Wiktionary, which are promising uprising resources in Natural Language processing. We contrastively compare them to a conventional linguistic knowledge base, and address the issue of how these Social Web knowledge repositories can be best exploited within the Social-Semantic Web.
knowledge Grid is a platform that enables uniform and effective knowledge sharing and management across the Internet. Based on this platform, this paper proposes a cooperative learning environment KGCL. It supports th...
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Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralque...
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ISBN:
(数字)9783540476986
ISBN:
(纸本)9783540476979
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
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