XML represents both content and structure of documents. Taking advantage of the document structure promises to greatly improve the retrieval precision. In this article, we present a retrieval technique that adopts the similarity measure of the vector space model, incorporates the document structure, and supports structured queries. Our query model is based on tree matching as a simple and elegant means to formulate queries without knowing the exact structure of the data. Using this query model we propose a logical document concept by deciding on the document boundaries at query time. We combine structured queries and term-based ranking by extending the term concept to structural terms that include substructures of queries and documents. The notions of term frequency and inverse document frequency are adapted to logical documents and structural terms. We introduce an efficient technique to calculate all necessary term frequencies and inverse document frequencies at query time. By adjusting parameters of the retrieval process we are able to model two contrary approaches: the classical vector space model, and the original tree matching approach.
The use of markup languages like SGML, HTML, or XML for encoding the structure of documents or linguistic data has lead to many databases where entries are adequately described as trees. In this context querying formalisms are interesting that offer the possibility to refer both to textual content and logical structure. We consider models where the structure specified in a query is not only used as a filter, but also for selecting and presenting different parts of the data. If answers are formalized as mappings from query nodes to the database, a simple enumeration of all mappings in the answer set will often suffer from the effect that many answers have common subparts. From a theoretical point of view this may lead to an exponential time complexity of the computation and presentation of all answers. Concentrating on the language of so-called tree queries-a variant and extension of Kilpeläi-nen's Tree Matching formalism-we introduce the notion of a "complete answer aggregate" for a given query. This new data structure offers a compact view of the set of all answers and supports active exploration of the answer space. Since complete answer aggregates use a powerful structure-sharing mechanism their maximal size is of order O͑d ⅐ h ⅐ q͒ where d and q respectively denote the size of the database and the query, and h is the maximal depth of a path of the database. An algorithm is given that computes a complete answer aggregate for a given tree query in time O͑d ⅐ log͑d͒ ⅐ h ⅐ q͒. For the sublanguage of so-called rigid tree queries, as well as for so-called "nonrecursive" databases, an improved bound of O͑d ⅐ log͑d͒ ⅐ q͒ is obtained. The algorithm is based on a specific index structure that supports practical efficiency.
Rooted in electronic publishing, XML is now widely used for modelling and storing structured text documents. Especially in the WWW, retrieval of XML documents is most useful in combination with a relevance-based ranking of the query result. Index structures with ranking support are therefore needed for fast access to relevant parts of large document collections. This paper proposes a classification scheme for both XML ranking models and index structures, allowing to determine which index suits which ranking model. An analysis reveals that ranking parameters related to both the content and structure of the data are poorly supported by most known XML indices. The IR-CADG index, owing to its tight integration of content and structure, supports various XML ranking models in a very efficient retrieval process. Experiments show that it outperforms separate content/structure indexing by more than two orders of magnitude for large corpora of several hundred MB.
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