A smart web query method for semantic retrieval of web data

摘要

The efficient query and extraction of web data is often difficult, because web data does not conform to any data organization standard. In addition, the development of web search technology is still at a relatively early stage. Search engines provide only primitive data query capabilities, and require a detailed syntactic specification to retrieve relevant data. Furthermore, web data exists in a myriad of formats including PDF documents, images, and sound clips that are difficult to be queried. This research proposes a smart web query (SWQ) method for the semantic retrieval of web data. The SWQ method uses domain semantics represented as context ontologies to specify and formulate appropriate web queries to search. This method also relies on semantic search filters to identify and rank relevant web pages semi-automatically. Unlike traditional ontologies that are structured in a hierarchy, terms and their relationships that pertain to a particular domain are organized with a flexible structure by the context ontologies. An SWQ engine is being developed to test the proposed method. Financial trading (e.g. stocks, bonds, unit trusts) is adapted as an example domain (i.e., context) to test and validate the SWQ method and engine.