Looking at the amount of structured contents available on the web in recent years, we can be certain that the needs of structured retrieval systems are getting more prominent. In order to access the structured contents, information requests are formulated in the form of query languages supported by the structured retrieval systems. Nevertheless, information requests at this layer cannot be specified directly by end users. Hence, a common solution is to map information needs obtained from the application layer to the query language of a retrieval system automatically. Although rule-based approach can be used to generate query automatically, it is designed for a single target query language, whereas, there are not one but different types of structured queries with different forms of syntax used in a structured retrieval environment.

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The search that involves structured Web resources like XML data, services is still lagging of its own method and relying on contemporary search systems. This paper presents a method that learns semantics from structured information of these resources. Instead of committing the semantic meaning of resources to strict and formal vocabularies like ontology or data dictionary, we are interested to interpret the meaning based on the natural context of the resources. The semantics are used in search process, i.e. query reasoning and resource selection, to provide better answer in terms of context relevancy and clearer result description