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.

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Abstract. We present a novel method for translating keyword queries over relational databases into SQL queries with the same intended semantic meaning. In contrast to the majority of the existing keyword-based techniques, our approach does not require any a-priori knowledge of the data instance. It follows a probabilistic approach based on a Hidden Markov Model for computing the top-K best mappings of the query keywords into the database terms, i.e., tables, attributes and values. The mappings are then used to generate the SQL queries that are executed to produce the answer to the keyword query. The method has been implemented into a system called KEYRY (from KEYword to queRY).

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A marketplace is the place in which the demand and supply of buyers and vendors participating in a business process may meet. Therefore, electronic marketplaces are virtual communities in which buyers may meet proposals of several suppliers and make the best choice. In the electronic commerce world, the comparison between different products is blocked due to the lack of standards (on the contrary, the proliferation of standards) describing and classifying them. Therefore, the need for B2B and B2C marketplaces is to reclassify products and goods according to different standardization models. This paper aims to face this problem by suggesting the use of a semi-automatic methodology, supported by a tool (SI-Designer), to define the mapping among different e-commerce product classification standards. This methodology was developed for the MOMIS system within the Intelligent Integration of Information research area. We describe our extension to the methodology that makes it applyable in general to product classification standard, by selecting a fragment of ECCMA/UNSPSC and ecl@ss standard.