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학술지 Pragmatic Correlation Analysis for Probabilistic Ranking over Relational Data
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박재휘, 이상구
Expert Systems with Applications, v.40 no.7, pp.2649-2658
12PC1100, 맞춤형 모바일 지식 서비스를 위한 지식스토어 핵심기술 개발, 김채규
It is widely recognized that effective ranking methods for relational data (e.g., tuples) enable users to overcome the limitations of the traditional Boolean retrieval model and the hardness of structured query writing. To determine the rank of a tuple, term frequency-based methods, such as tf × idf (term frequency × inverse document frequency) schemes, have been commonly adopted in the literature by simply considering a tuple as a single document. However, in many cases, we have noted that tf × idf schemes may not produce effective rankings or specific orderings for relational data with categorical attributes, which is pervasive today. To support fundamental aspects of relational data, we apply the notions of correlation analysis to estimate the extent of relationships between queries and data. This paper proposes a probabilistic ranking model to exploit statistical relationships that exist in relational data of categorical attributes. Given a set of query terms, information on correlative attribute values to the query terms is used to estimate the relevance of the tuple to the query. To quantify the information, we compute the extent of the dependency between correlative attribute values on a Bayesian network. Moreover, we avoid the prohibitive cost of computing insignificant ranking features based on a limited assumption of node independence. Our probabilistic ranking model is domain-independent and leverages only data statistics without any prior knowledge such as user query logs. Experimental results show that our work improves the effectiveness of rankings for real-world datasets and has a reasonable query processing efficiency compared to related work. © 2012 Elsevier B.V. All rights reserved.
Correlation analysis, Probabilistic ranking model, Ranking for structured data
KSP 제안 키워드
Bayesian Network(BN), Boolean retrieval, Correlation Analysis, Fundamental aspects, Inverse Document Frequency, Probabilistic ranking, Query Processing, Ranking Methods, Ranking Model, Real-world, Relational data