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

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database

This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments ("General" and "Specific") and two categories of topics ("Broad" and "Narrow"). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call "Coherent Retrieval Elements". The results of our experiments show that -- when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics) -- the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.Comment: Postprint version. The editor version can be accessed through the DO

Term-Specific Eigenvector-Centrality in Multi-Relation Networks

Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim

Content-Aware DataGuides for Indexing Large Collections of XML Documents

XML is well-suited for modelling structured data with textual content. However, most indexing approaches perform structure and content matching independently, combining the retrieved path and keyword occurrences in a third step. This paper shows that retrieval in XML documents can be accelerated significantly by processing text and structure simultaneously during all retrieval phases. To this end, the Content-Aware DataGuide (CADG) enhances the wellknown DataGuide with (1) simultaneous keyword and path matching and (2) a precomputed content/structure join. Extensive experiments prove the CADG to be 50-90% faster than the DataGuide for various sorts of query and document, including difficult cases such as poorly structured queries and recursive document paths. A new query classification scheme identifies precise query characteristics with a predominant influence on the performance of the individual indices. The experiments show that the CADG is applicable to many real-world applications, in particular large collections of heterogeneously structured XML documents

The State-of-the-arts in Focused Search

The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a user’s topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems