Keyword search over relational databases

Abstract

Keyword queries offer a convenient alternative to traditional SQL in querying relational databases with large, often unknown, schemas and instances. The challenge in answering such queries is to discover their intended semantics, construct the SQL queries that describe them and used them to retrieve the respective tuples. Existing approaches typically rely on indices built a-priori on the database content. This seriously limits their applicability if a-priori access to the database content is not possible. Examples include the on-line databases accessed through web interface, or the sources in information integration systems that operate behind wrappers with specific query capabilities. Furthermore, existing literature has not studied to its full extend the inter-dependencies across the ways the different keywords are mapped into the database values and schema elements. In this work, we describe a novel technique for translating keyword queries into SQL based on the Munkres (a.k.a. Hungarian) algorithm. Our approach not only tackles the above two limitations, but it offers significant improvements in the identification of the semantically meaningful SQL queries that describe the intended keyword query semantics. We provide details of the technique implementation and an extensive experimental evaluation.

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

“…Section 4.2 compares the precisions of the term similarity calculation with different parameters to choose the optimal value of . Section 4.3 gives contrast experiments using Metadata [3] and -coupling [11] as the baselines. The performance of these algorithms is evaluated, respectively.…”

“…However, it needs the interaction of humans, so its efficiency is low. In [3], the keyword query is transformed to a SQL statement based on Munkres, which provides possible semantic descriptions. This method is useful for identifying users' query intention.…”

“…In this subsection, we report the performance of the query expansion method ReInterpretQE in comparison with the state-of-the-art approaches, Metadata [3] and -coupling [11]. The performance is measured by three evaluation metrics: precision, recall, and -score.…”

“…Although this method does not require users to be knowledgeable about the underlying structure of databases and structured query language (e.g., SQL), its semantic fuzziness and its expressive power are limited due to the lack of structure [3]. In addition, ordinary users are usually unable to specify exact keywords to describe their query intention, which makes it harder to return adequate results through keyword query [4,5].…”

“…On the other hand, Ganti et al [12] translates keyword queries into SQL based on the materialized mappings. Bergamaschi et al [3] propose a Metadata method to translate keyword queries into SQL based on Munkres algorithm. Though these methods describe users' query intention to a degree, they do not take into account similar words and related items extension.…”