A model and an algebra for semi-structured and full-text queries

The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

Spagnolo Tecnico Semplificato

L’obiettivo della presente dissertazione è quello di creare un nuovo linguaggio controllato, denominato Español Técnico Simplificado (ETS). Basato sulla specifica tecnica del Simplified Technical English (STE), ufficialmente conosciuta come ASD-STE100, lo spagnolo controllato ETS si presenta come un documento metalinguistico in grado di fornire ad un redattore o traduttore tecnico alcune regole specifiche per produrre un documento tecnico. La strategia di implementazione conduce allo studio preliminare di alcuni linguaggi controllati simili all’inglese STE, quali il Français Rationalisé e il Simplified Technical Spanish. Attraverso un approccio caratteristico della linguistica dei corpora, la soluzione proposta fornisce il nuovo linguaggio controllato mediante l’estrazione di informazioni specifiche da un corpus ad-hoc di lingua spagnola appositamente creato ed interrogato. I risultati evidenziano un metodo linguistico (controllato) utile a produrre documentazione tecnica priva di ogni eventuale ambiguità. Il sistema ETS, infatti, si fonda sul concetto della intelligibilità in quanto condizione necessaria da soddisfare nell’ambito della produzione di un testo controllato. E, attraverso la sua macrostruttura, il documento ETS fornisce gli strumenti necessari per rendere il testo controllato univoco. Infatti, tale struttura bipartita suddivide in maniera logica i dettami: una prima parte riguarda e contiene regole sintattiche e stilistiche; una seconda parte riguarda e contiene un dizionario di un numero limitato di lemmi opportunamente selezionati. Il tutto a favore del principio della biunivocità dei segni, in questo caso, della lingua spagnola. Il progetto, nel suo insieme, apre le porte ad un linguaggio nuovo in alternativa a quelli presenti, totalmente creato in accademia, che vale come prototipo a cui far seguire altri progetti di ricerca.

Statistical analysis of the error associated with the simplification of the stratigraphy in geotechnical models

The uncertainties in the determination of the stratigraphic profile of natural soils is one of the main problems in geotechnics, in particular for landslide characterization and modeling. The study deals with a new approach in geotechnical modeling which relays on a stochastic generation of different soil layers distributions, following a boolean logic – the method has been thus called BoSG (Boolean Stochastic Generation). In this way, it is possible to randomize the presence of a specific material interdigitated in a uniform matrix. In the building of a geotechnical model it is generally common to discard some stratigraphic data in order to simplify the model itself, assuming that the significance of the results of the modeling procedure would not be affected. With the proposed technique it is possible to quantify the error associated with this simplification. Moreover, it could be used to determine the most significant zones where eventual further investigations and surveys would be more effective to build the geotechnical model of the slope. The commercial software FLAC was used for the 2D and 3D geotechnical model. The distribution of the materials was randomized through a specifically coded MatLab program that automatically generates text files, each of them representing a specific soil configuration. Besides, a routine was designed to automate the computation of FLAC with the different data files in order to maximize the sample number. The methodology is applied with reference to a simplified slope in 2D, a simplified slope in 3D and an actual landslide, namely the Mortisa mudslide (Cortina d’Ampezzo, BL, Italy). However, it could be extended to numerous different cases, especially for hydrogeological analysis and landslide stability assessment, in different geological and geomorphological contexts.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.