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

Mining XML Documents

XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections

UJM at INEX 2008 XML mining Track

International audienceThis paper reports our experiments carried out for the INEX XML Mining track, consisting in developing categorization (or classification) and clustering methods for XML documents. We represent XML documents as vectors of index terms. For our first participation, the purpose of our experiments is twofold: Firstly, our overall aim is to set up a categorization text only approach that can be used as a baseline for further work which will take into account the structure of the XML documents. Secondly, our goal is to define two criteria based on terms distribution for reducing the size of the index. Results of our baseline are good and using our two criteria, we improve these results while we slightly reduce the index term. The results are slightly worse when we reduce sharply the index of terms

Fault-Based Test of XML Schemas

XML is largely used by most applications to exchange data among different software components. XML documents, in most cases, follow a grammar or schema that describes which elements and data types are expected by the application. These schemas are translated from specifications written in natural language, and consequently, in this process some mistakes are usually made. Because of this, faults can be introduced in the schemas, and incorrect XML documents can be validated, causing a failure in the application. Hence, to test schemas is a fundamental activity to ensure the integrity of the XML data. With the growing number of Web applications and increased use of XML, there is a demand for specific testing approaches and tools to test schemas. To fulfill this demand, this work introduces a fault-based approach for testing XML schemas. This approach is based on a classification of common faults found in schemas. A supporting tool was implemented and used in evaluation studies. The obtained results show the applicability of the fault-based testing in this context and its efficacy in revealing faults

XML with incomplete information

We study models of incomplete information for XML, their computational properties, and query answering. While our approach is motivated by the study of relational incompleteness, incomplete information in XML documents may appear not only as null values but also as missing structural information. Our goal is to provide a classification of incomplete descriptions of XML documents, and separate features- or groups of features- that lead to hard computational problems from those that admit efficient algorithms. Our classification of incomplete information is based on the combination of null values with partial structural descriptions of documents. The key computational problems we consider are consistency of partial descriptions, representability of complete documents by incomplete ones, and query answering. We show how factors such as schema information, the presence of node ids, and missing structural information affect the complexity of these main computational problems, and find robust classes of incomplete XML descriptions tha

Experiments in Clustering Homogeneous XML Documents to Validate an Existing Typology

This paper presents some experiments in clustering homogeneous XMLdocuments to validate an existing classification or more generally anorganisational structure. Our approach integrates techniques for extracting knowledge from documents with unsupervised classification (clustering) of documents. We focus on the feature selection used for representing documents and its impact on the emerging classification. We mix the selection of structured features with fine textual selection based on syntactic characteristics.We illustrate and evaluate this approach with a collection of Inria activity reports for the year 2003. The objective is to cluster projects into larger groups (Themes), based on the keywords or different chapters of these activity reports. We then compare the results of clustering using different feature selections, with the official theme structure used by Inria.Comment: (postprint); This version corrects a couple of errors in authors' names in the bibliograph

Automatic document classification of biological literature

Background: Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results: We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusions: We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept