EquiX---A Search and Query Language for XML

EquiX is a search language for XML that combines the power of querying with the simplicity of searching. Requirements for such languages are discussed and it is shown that EquiX meets the necessary criteria. Both a graphical abstract syntax and a formal concrete syntax are presented for EquiX queries. In addition, the semantics is defined and an evaluation algorithm is presented. The evaluation algorithm is polynomial under combined complexity. EquiX combines pattern matching, quantification and logical expressions to query both the data and meta-data of XML documents. The result of a query in EquiX is a set of XML documents. A DTD describing the result documents is derived automatically from the query.Comment: technical report of Hebrew University Jerusalem Israe

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

This article reports on the XML retrieval system X2 which has been developed at the University of Munich over the last five years. In a typical session with X2, the user first browses a structural summary of the XML database in order to select interesting elements and keywords occurring in documents. Using this intermediate result, queries combining structure and textual references are composed semiautomatically. After query evaluation, the full set of answers is presented in a visual and structured way. X2 largely exploits the structure found in documents, queries and answers to enable new interactive visualization and exploration techniques that support mixed IR and database-oriented querying, thus bridging the gap between these three views on the data to be retrieved. Another salient characteristic of X2 which distinguishes it from other visual query systems for XML is that it supports various degrees of detailedness in the presentation of answers, as well as techniques for dynamically reordering and grouping retrieved elements once the complete answer set has been computed

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

A visual XML query interface

As XML becomes more and more popular, easy-to-use and powerful XML query languages are in great need. Xing is a visual query and restructuring language for XML documents. The objective of this project is to develop a basic version of Xing, including a user-oriented XML query interface and a simple query translation implementation. The interface design is based on a visual document metaphor and the notion of document patterns and rules. End users do not have to be good at programming or XML syntax to use Xing. In this report, we describe the implementation for the Xing prototype, including GUI design, data structures and algorithms. We also compare the features of Xing with other XML query languages

A graphical XML query language based on ORA-SS

Ph.DDOCTOR OF PHILOSOPH

XTree and XTreeQuery for declarative XML querying

Master'sMASTER OF SCIENC

XGI: A Graphical Interface for XQuery Creation and XML Schema Visualization

XML (Extensible Markup Language) is used in many contexts of modern information technology to facilitate sharing of information between heterogeneous data sources and inter-platform applications. The prevalence of XML implementation in data storage and exchange necessitates a method to adequately query XML data. The World Wide Web Consortium (W3C) is proposing XQuery as the standard querying language for semistructured XML data. XQuery is designed for experienced database programmers, since its syntax and capabilities are analogous to the SQL relational query language. Therefore, the inherent complexity of formulating XQuery statements makes it an intimidating task for anyone, except an expert in the XQuery language, to construct queries. The development of XQuery Graphical Interface (XGI), a visual interface for creating XQuery in a graphical format, is motivated by the need to simplify the query formation for unskilled users and speed up the query construction for expert users. The implementation of XGI is mainly inspired by three existing systems: Query and Reporting Semistructured Data (QURSED), XQuery By Example (XQBE), and XBrain. A review of these systems and many other systems has helped us understand the benefits and drawbacks of various system design approaches, and has assisted us in identifying a set of features for XGI that will successfully reduce the complexity of creating queries in the XQuery language. XGI provides a web interface for users to explore their own XML source data schema, search for specific schema elements, and visually create queries in the XQuery language for the targeted XML data source. A validation of the XGI system has verified its ability to efficiently and accurately create queries for various XML data sources. From the validation, we have recognized some strengths and weaknesses of the XGI system compared to other systems. We also recommend several areas in which XGI can be improved

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches