Implementing Background Net with Knowware System for Personalized Keyword Support

Implementing Background Net with Knowware System for Personalized Keyword Support

Yuan Chen (Macau University of Science and Technology, China), Liya Ding (Macau University of Science and Technology, China), Sio-Long Lo (Macau University of Science and Technology, China) and Dickson K.W. Chiu (Dickson Computer Systems, Hong Kong)
DOI: 10.4018/jssoe.2011010105
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This article proposes a novel approach that combines user’s instant requirement described in keywords with her or his long-term knowledge background to better serve article selection based on personal preference. The knowledge background is represented as a weighted undirected graph called background net that captures the contextual association of words that appear in the articles recommended by the user through incremental learning. With a background net of user constructed, a keyword from the user is personalized to a fuzzy set that represents contextual association of the given keyword to other words involved in the user’s background net. An article evaluation with personal preference can be achieved by evaluating similarity between personalized keyword set based on user’s background net and a candidate article. The proposed approach makes it possible to construct a search engine optimizer running on the top of search engines to adjust search results, and offer the potential to be integrated with existing search engine techniques to achieve better performance. The target system of personalized article selection can be automatically constructed using Knowware System which is a development tool of KBS for convenient modeling and component reuse.
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Personal Knowledge Background For Article Selection

A classical approach is to use the cosine similarity by frequency of keywords for article selection. However, this is often insufficient, as it may be too rough in decision. One improvement is the Graph Model (Wu et al., 2009) that considers both the frequency and position property of keywords in articles to obtain a more accurate result by calculating the similarity of two articles. Thus, this method can also used for detecting fraudulent or plagiarized articles, which can be hardly done by only using keyword frequency. There are also other methods proposed to improve the quality of article selection, such as the use of ontology in text classification (Yang et al., 2009), interest mode (Zhao et al., 2011; Teevan et al., 2005) and semantic-based methods (Lv & Liu, 2005).

However, those methods are not dealing with text documents with personal preferences. A word may often have different meanings to different people and thus it is reasonable to obtain different results with the same keywords given when personalization is considered. In this sense, a keyword is not purely a symbol but a symbolized representation of a concept that can be personal in terms of its meaning.

As a research topic, personalized article selection has attracted a lot of attention for its possible applications in Search Engine Optimization (SEO), such as Brin et al. (1999) and Leubner and Kießling (2002). However, most of them capture personal preferences only in a limited space by a rather inflexible set of features represented by keywords, and unable to represent a broad range of user long-term interest and knowledge with incremental learning.

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