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

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

Expériences de classification d'une collection de documents XML de structure homogène

Cette version corrige des erreurs dans le nom de 2 auteurs cites dans la bibliographie.National audienceThis 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. ––– Cet article présente différentes expériences de classification de documents XML de structure homogène, en vue d'expliquer et de valider une présentation organisationnelle pré-existante. Le problème concerne le choix des éléments et mots utilisés pour la classification et son impact sur la typologie induite. Pour cela nous combinons une sélection structurelle basée sur la nature des éléments XML et une sélection linguistique basée sur un typage syntaxique des mots. Nous illustrons ces principes sur la collection des rapports d'activité 2003 des équipes de recherche de l'Inria en cherchant des groupements d'équipes (Thèmes) à partir du contenu de différentes parties de ces rapports. Nous comparons nos premiers résultats avec les thèmes de recherche officiels de l'Inria

Classification de documents XML à partir d'une représentation linéaire des arbres de ces documents

www.cepadues.comIn this work, we propose a new clustering document representation for semi-structured documents collections. Our approach consists on a representation of XML documents based on their sub-paths, defined according to some criteria (length, root beginning, leaf ending) using the structure only or both the structure and the content. By considering those sub-paths as words, we can use standard methods for vocabulary reduction, and simple clustering methods such as K-means that scale up well. We actually use an implementation of the clustering algorithm known as \textit{dynamic clouds} that can work with distinct groups of independent variables. This is necessary in our model since embedded sub-paths are not independent. For validation and evaluation of our method, two collections are used: the INEX corpus and the INRIA activity reports, and a set of metrics well-known in Information Retrieval

DE LA MODELISATION A L'EXPLOITATION DES DOCUMENTS A STRUCTURES MULTIPLES

With the recent development of new information and communication technologies, the paper documents are transformed to digital documents. Furthermore, it considers that the document is no longer seen as a whole, or as a monolithic bloc, but as organized entities. Exploiting these documents amount to identify and locate these entities. These entities are connected by relationships to give a "form" to document. Several types of relationships may occur, so that several "forms" of a document emerge. These different materializations of the same document are related to different uses of the same document and are essential for optimal management and shared of holdings. The work presented in this thesis aims to address the challenges of representing different materializations of a document through its representation of entities and their relationships. If those materializations are translated through structures, the issues are related to the representation of multistructured documents. Our work focuses mainly on the modeling, integration and exploitation of multistructured documents: (1) Proposal of multistructured document model. This model incorporates two levels of description: a specific level to describe each document through entities that compose and a generic level to identify document kinds through the grouping of similar structures. (2) Proposal of techniques for extracting structure (implicit or explicit) of a document (the specific level) and classification of this structure with respect to common structures (the generic level). The classification algorithm proposed includes a calculation of distance called "structural" (comparison of trees and graphs). This classification is associated with a process of verification of the "cohesion" of classes and possible reorganization of disrupted classes. (3) Proposal of document exploitation technical from their structures and their contents: (a) a document search that can reproduce documentary granules through criteria based on research of structures and / or content, (b) a multidimensional analysis that is to analyze and visualize the documentary information across multiple dimensions (of structures and / or content). In order to validate our proposals, we have developed a tool for integration and analysis of multistructured documents, called MDOCREP (Multistructured Document Repository). This tool provides on the one hand, the extraction and classification of document structures, and on the other hand, the querying and the multidimensional analysis of documents from their different structures.Avec l'évolution des nouvelles technologies de l'information et de la communication, les documents papier ont laissé la place aux documents numériques. On considère de plus que le document n'est plus vu comme un tout, ni comme un bloc monolithique, mais comme un ensemble organisé d'entités. Exploiter ces documents revient à identifier et retrouver ces entités. Ces dernières sont reliées par des relations permettant de donner une « forme » au document. Plusieurs types de relations peuvent apparaître, de sorte à ce que plusieurs « formes » d'un même document émergent. Ces différentes matérialisations d'un même document sont liées à des usages différents d'un même document et sont primordiales pour une gestion optimale et partagée des fonds documentaires. Les travaux présentés dans cette thèse visent à faire face aux défis de représentation des différentes matérialisations d'un document au travers de la représentation de ses entités et de leurs relations. Si ces matérialisations sont traduites par des structures, les enjeux concernent la représentation des documents à structures multiples. Nos travaux portent essentiellement sur la modélisation, l'intégration et l'exploitation des documents à structures multiples : (1) Proposition d'un modèle de documents multistructurés. Ce modèle intègre deux niveaux de description : un niveau spécifique permettant de décrire chaque document au travers des entités qui le composent et un niveau générique permettant de définir des typologies de documents au travers du regroupement de structures similaires. (2) Proposition des techniques d'extraction de structure (implicite ou explicite) d'un document (niveau spécifique) et de classification de cette structure par rapport à des structures communes (niveau générique). L'algorithme de classification proposé intègre un calcul d'une distance dite « structurelle » (comparaison d'arbres et de graphes). Cette démarche de classification est associée à une démarche de vérification de la « cohésion » des classes et de réorganisation éventuelle des classes perturbées. (3) Proposition de techniques d'exploitation des documents à partir de leurs structures et de leur contenu : (a) une recherche de documents qui permet de restituer des granules documentaires selon des critères de recherches basés sur la ou les structures et/ou le contenu ; (b) une analyse multidimensionnelle qui consiste à analyser et visualiser les informations documentaires selon plusieurs dimensions (de structures et/ou de contenu). Pour valider nos propositions, nous avons développé un outil d'aide à l'intégration et à l'analyse de documents à structures multiples, intitulé MDOCREP (Multistructured DOCument REPository). Cet outil assure d'une part, l'extraction et la classification des structures de documents, et d'autre part, l'interrogation et la restitution multidimensionnelle des documents à partir de leurs différentes structures