Ontology-based speech act identification in a bilingual dialog system using partial pattern trees
Journal of the American Society for Information Science and Technology
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
A multi-strategy knowledge interoperability framework for heterogeneous learning objects
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Towards integrating real-world spatiotemporal data with social networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Ontology-Based similarity between text documents on manifold
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
GEOSO - a geo-social model: from real-world co-occurrences to social connections
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
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Development of algorithms for automated text categorization in massive text document sets is an important research area of data mining and knowledge discovery. Most of the text-clustering methods were grounded in the term-based measurement of distance or similarity, ignoring the structure of the documents. In this paper, we present a novel method named structured cosine similarity (SCS) that furnishes document clustering with a new way of modeling on document summarization, considering the structure of the documents so as to improve the performance of document clustering in terms of quality, stability, and efficiency. This study was motivated by the problem of clustering speech documents (of no rich document features) attained from the wireless experience oral sharing conducted by mobile workforce of enterprises, fulfilling audio-based knowledge management. In other words, this problem aims to facilitate knowledge acquisition and sharing by speech. The evaluations also show fairly promising results on our method of structured cosine similarity.