Content and structure in indexing and ranking XML

Abstract

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.

Visual exploration and retrieval of XML document collections with the generic system X2

“…As indicated in the upper left corner of the sec slot in Figure 11, the system retrieves 736 sections, each with a relevance score computed according to one of the implemented ranking models, currently either XPRES [27] or s-term [22]. (For a comparison of these and other models, see [26]. )…”

“…Dedicated ranking schemes for structured document retrieval currently attract much attention in IR research [21,22,8,27,24,26]. X 2 is a system that is to a large extent independent of the ranking mechanism used, hence the research papers mentioned above are complementary to the issues discussed in the paper at hand.…”

Pattern based processing of XPath queries

“…Suitable indexing structures together with query processing strategies can significantly improve the performance of this matching operation. There are a number of index-based query processing strategies utilised by XML databases [1,2,17,16,22,23].…”

“…The queries in this section are largely taken from previous work [1,2,17,16,22,23]. Each query was executed five times and the times recorded in milliseconds were then averaged and displayed in table 4.2.…”

XML Node Semantic Weight Model Based on VSM

“…Reordering results come from XML information retrieval system is to expand the traditional IR sort approach to XML model, the main method is based on the term frequency and ontology similarity scoring [4], vector space scoring modeling [5], probability scoring modeling [6], hyperlinks and elements nested structure of ElemRank [7], standardization merger sort [8], etc. However, the existing XML sorting mechanism (that is how to calculate semantic relevance on XML elements, how to calculate match score of query results on keywords and path semantic) is limited.…”