Selective integration of background knowledge in TCBR systems
|Selective integration of background knowledge in TCBR systems|
|Author(s)||Patelia A., Chakraborti S., Wiratunga N.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Background knowledge, Explicit semantics, Performance Gain, Semantic similarity, System effectiveness, Text classification, Textual content, Web resources, Wikipedia, Case based reasoning, Knowledge engineering, Semantics, World Wide Web)|
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Selective integration of background knowledge in TCBR systems is a 2011 conference paper written in English by Patelia A., Chakraborti S., Wiratunga N. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
This paper explores how background knowledge from freely available web resources can be utilised for Textual Case Based Reasoning. The work reported here extends the existing Explicit Semantic Analysis approach to representation, where textual content is represented using concepts with correspondence to Wikipedia articles. We present approaches to identify Wikipedia pages that are likely to contribute to the effectiveness of text classification tasks. We also study the effect of modelling semantic similarity between concepts (amounting to Wikipedia articles) empirically. We conclude with the observation that integrating background knowledge from resources like Wikipedia into TCBR tasks holds a lot of promise as it can improve system effectiveness even without elaborate manual knowledge engineering. Significant performance gains are obtained using a very small number of features that have very strong correspondence to how humans describe the domain.
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