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 e#cient implementation of XSEarch. The performance of the di#erent techniques as well as the recall and the precision were measured experimentally.

Keyword search is a proven, user-friendly way to query HTML documents in the World Wide Web. We propose keyword search in XML documents, modeled as labeled trees, and describe corresponding efficient algorithms. The proposed keyword search returns

XKeyword provides efficient keyword proximity queries on large XML graph databases. A query is simply a list of keywords and does not require any schema or query language knowledge for its formulation. XKeyword is built on a relational database...

This paper presents a novel engine, coined TopX, for efficient ranked retrieval of XML documents over semistructured but nonschematic data collections. The algorithm follows the paradigm of threshold algorithms for top-k query processing with a focus on inexpensive sequential accesses to index lists and only a few judiciously scheduled random accesses. The difficulties in applying...

The INEX'03 query language proved to be much too complicated for the INEX participants to use well, let alone anyone else. We need something simpler, but not too simple. Something which is basically a hybrid between Boolean IR queries and a stripped down CSS will do the job.

Recently, there has been a great deal of interest in the development of techniques to evaluate path expressions over collections of XML documents. In general, these path expressions contain both structural and keyword components. Several methods have been proposed for processing path expressions over graph/tree-structured XML data. These methods can be classified into two broad classes. The first involves graph traversal where the input query is evaluated by traversing the data graph or some compressed representation. The other class involves information-retrieval style processing using inverted lists. In this framework, structure indexes have been proposed to be used as a substitute for graph traversal. These structure indexes are proven to be very effective when applied to queries that examine the “coarse ” structure of documents. For example, for many

Abstract Whereas traditional databases manage onlydeterministic information, many applications that use databases involve uncertain data.This paper presents a Probabilistic Tree Data Base (ProTDB) to manage probabilistic data,represented in XML. Our approach differs from previous effortsto develop probabilistic relational systems in that we build a probabilistic XML database.This design is driven by application needs that involve data not readily amenable to a rela-tional representation. XML data poses several modeling challenges: due to its structure, dueto the possibility of uncertainty association at multiple granularities, and due to the possi-bility of missing and repeated sub-elements. We present a probabilistic XML model thataddresses all of these challenges. We devise an implementation of XML query operationsusing our probability model, and demonstrate the efficiency of our implementation experi-mentally. We have used ProTDB to manage data fromtwo application areas: protein chemistry data from the bioinformatics domain, and informa-tion extraction data obtained from the web using a natural language analysis system. Wepresent a brief case study of the latter to demonstrate the value of probabilistic XMLdata management.

Keyword search enables web users to easily access XML data without the need to learn a structured query language and to study possibly complex data schemas. Existing work has addressed the problem of selecting qualified data nodes that match keywords and connecting them in a meaningful way, in the spirit of inferring a where

This paper presents the novel SphereSearch Engine that provides unified ranked retrieval on heterogeneous XML and Web data. Its search capabilities include vague structure conditions, text content conditions, and relevance ranking based on IR statistics and statistically quantified ontological relationships.

XML databases often contain documents comprising structured text. Therefore, it is important to integrate "information retrieval style" query evaluation, which is well-suited for natural language text, with standard "database style" query evaluation, which handles structured queries efficiently. Relevance scoring is central to information retrieval. In the case of XML, this operation becomes more complex because the data required for scoring could reside not directly in an element itself but also in its descendant elements.