Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.

Editorial reorganization of an alphabetically ordered collection of short stories: analysis of problems and hypothesis of possible solutions

This paper aims to show, analyze and solve the problems related to the translation of the book with meaning-bond alphabetically ordered chapter titles La vita non è in ordine alfabetico, by the Italian writer Andrea Bajani. The procedure is inevitable for a possible translation of the book, and it is necessary to have a preliminary pattern to follow. After translating the whole book, not only is a revision fundamental, but a restructure and reorganization of the collection may be required, hence, what this thesis offers is a scheme to start from, together with an analysis of the possible problems that may arise, and a useful method to find a solution.

Learning from experience: Analysis of the civil litigation regarding building structures on the court of bologna in the years 2011/2012

The present work is aimed to the study and the analysis of the defects detected in the civil structure and that are object of civil litigation in order to create an instruments capable of helping the different actor involved in the building process. It is divided in three main sections. The first part is focused on the collection of the data related to the civil proceeding of the 2012 and the development of in depth analysis of the main aspects regarding the defects on existing buildings. The research center “Osservatorio Claudio Ceccoli” developed a system for the collection of the information coming from the civil proceedings of the Court of Bologna. Statistical analysis are been performed and the results are been shown and discussed in the first chapters.The second part analyzes the main issues emerged during the study of the real cases, related to the activities of the technical consultant. The idea is to create documents, called “focus”, addressed to clarify and codify specific problems in order to develop guidelines that help the technician editing of the technical advice.The third part is centered on the estimation of the methods used for the collection of data. The first results show that these are not efficient. The critical analysis of the database, the result and the experience and throughout, allowed the implementation of the collection system for the data.

Arald: an architecture for name resolution, disambiguation and linking of linked data through semantics

La capacità di estrarre entità da testi, collegarle tra loro ed eliminare possibili ambiguità tra di esse è uno degli obiettivi del Web Semantico. Chiamato anche Web 3.0, esso presenta numerose innovazioni volte ad arricchire il Web con dati strutturati comprensibili sia dagli umani che dai calcolatori. Nel reperimento di questi temini e nella definizione delle entities è di fondamentale importanza la loro univocità. Il nostro orizzonte di lavoro è quello delle università italiane e le entities che vogliamo estrarre, collegare e rendere univoche sono nomi di professori italiani. L’insieme di informazioni di partenza, per sua natura, vede la presenza di ambiguità. Attenendoci il più possibile alla sua semantica, abbiamo studiato questi dati ed abbiamo risolto le collisioni presenti sui nomi dei professori. Arald, la nostra architettura software per il Web Semantico, estrae entità e le collega, ma soprattutto risolve ambiguità e omonimie tra i professori delle università italiane. Per farlo si appoggia alla semantica dei loro lavori accademici e alla rete di coautori desumibile dagli articoli da loro pubblicati, rappresentati tramite un data cluster. In questo docu delle università italiane e le entities che vogliamo estrarre, collegare e rendere univoche sono nomi di professori italiani. Partendo da un insieme di informazioni che, per sua natura, vede la presenza di ambiguità, lo abbiamo studiato attenendoci il più possibile alla sua semantica, ed abbiamo risolto le collisioni che accadevano sui nomi dei professori. Arald, la nostra architettura software per il Web Semantico, estrae entità, le collega, ma soprattutto risolve ambiguità e omonimie tra i professori delle università italiane. Per farlo si appoggia alla semantica dei loro lavori accademici e alla rete di coautori desumibile dagli articoli da loro pubblicati tramite la costruzione di un data cluster.