text: Three approaches to content-and-structure XML retrieval are analysed in this paper: first by using Zettair, a full-text information retrieval system; second by using eXist, a native XML database, and third by using a hybrid XML retrieval system that uses eXist to produce the final answers from likely relevant articles retrieved by Zettair. INEX 2003 content-and-structure topics can be classified in two categories: the first retrieving full articles as final answers, and the second retrieving more specific elements within articles as final answers. We show that for both topic categories our initial hybrid system improves the retrieval effectiveness of a native XML database. For ranking the final answer elements, we propose and evaluate a novel retrieval model that utilises the structural relationships between the answer elements of a native XML database and retrieves Coherent Retrieval Elements. The final results of our experiments show that when the XML retrieval task focusses on highly relevant elements our hybrid XML retrieval system with the Coherent Retrieval Elements module is 1.8 times more effective than Zettair and 3 times more effective than eXist, and yields an effective content-and-structure XML retrieval.
  @cite_5: XSEarch, a semantic search engine for XML, is presented. XSEarch has a simple query language, suitable for a naive user. It returns semantically related document fragments that satisfy the user's query. Query answers are ranked using extended information-retrieval techniques and are generated in an order similar to the ranking. Advanced indexing techniques were developed to facilitate efficient implementation of XSEarch. The performance of the different techniques as well as the recall and the precision were measured experimentally. These experiments indicate that XSEarch is efficient, scalable and ranks quality results highly.
  @cite_3: We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents.
  @cite_2: This paper proposes a new approach to querying collections of structured textual information such as SGML XML documents. Knowledge about the structure of documents is an additional resource that should be exploited during retrieval since the semantics of the different textual objects can be used to specify an information need much more precisely. However the traditional probabilistic retrieval model lacks the ability to handle structural information. We define a new retrieval function based on the probabilistic model which overcomes this drawback. The presented query language allows the assignment of structural roles to individual terms. The efficient evaluation of queries in this framework requires appropriate index structures. We design text and structure indexes and show how their information is combined during evaluation. The implementation supports additional functionalities such as a table of contents for browsing. First evaluation results show the feasibility of the approach on collections of unstructured documents.
summary: For the purpose of ranking the resulting answers of XML retrieval topics, @cite_2 extend the probabilistic ranking model by incorporating the notion of structural roles'', which can be determined manually from the document schema. However, the term frequencies are measured only for the structural elements belonging to a particular role, without taking into account the entire context where all these elements belong in the document hierarchy. XRank @cite_3 and XSearch @cite_5 furthermore aim at producing effective ranked results for XML queries. XRank generally focuses on hyperlinked XML documents, while XSearch retrieves answers comprising semantically related nodes. However, since the structure of IEEE XML documents in the INEX document collection does not typically meet the above requirements, neither of them (without some modifications) could be used in a straightforward fashion with the CAS retrieval task.