Finding top-k semantically related terms from relational keyword search

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014, pp. 505 - 511
Issue Date:
2014
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Due to the insufficient knowledge of users about the database schema and content, most of them cannot easy to find appropriate keywords to express their query intentions. This paper proposes a novel approach, which can provide a list of keywords that semantically related to the set of given query keywords by analyzing the correlations between terms in database and query keywords. The suggestion would broaden the knowledge of users and help them to formulate more efficient keyword queries. To capture the correlations between terms in database and query keywords, a coupling relationship measuring method is proposed to model both the term intra- and intercouplings, which can reveal the explicit and implicit relationships between terms. For a given keyword query, based on the coupling relationships between terms, an order of terms in database is created for each query keyword and then the threshold algorithm (TA) is to expeditiously generate top-k ranked semantically related terms. The experiments demonstrate that our term coupling relationship measuring method can efficiently capture the semantic correlations between terms. The performance of top-k related term selection algorithm is also demonstrated.
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