Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results

The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.

Mappatura degli habitat e degli oggetti antropici della laguna di Venezia

L’utilizzo del Multibeam Echo sounder (MBES) in ambienti di transizione poco profondi, con condizioni ambientali complesse come la laguna di Venezia, è ancora in fase di studio e i dati biologici e sedimentologici inerenti ai canali della laguna di Venezia sono attualmente scarsi e datati in letteratura. Questo studio ha lo scopo di mappare gli habitat e gli oggetti antropici di un canale della laguna di Venezia in un intervallo di profondità tra 0.3 e 20 m (Canale San Felice) analizzando i dati batimetrici e di riflettività (backscatter) acquisiti da ISMAR-Venezia nell’ambito del progetto RITMARE. A tale scopo il fondale del canale San Felice (Venezia) è stato caratterizzato dal punto di vista geomorfologico, sedimentologico e biologico; descrivendo anche l’eventuale presenza di oggetti antropici. L’ecoscandaglio utilizzato è il Kongsberg EM2040 Dual-Compact Multibeam in grado di emettere 800 beam (400 per trasduttore) ad una frequenza massima di 400kHZ e ci ha consentito di ricavare ottimi risultati, nonostante le particolari caratteristiche degli ambienti lagunari. I dati acquisiti sono stati processati tramite il software CARIS Hydrographic information processing system (Hips) & Sips, attraverso cui è possibile applicare le correzioni di marea e velocità del suono e migliorare la qualità dei dati grezzi ricavati da MBES. I dati sono stati quindi convertiti in ESRI Grid, formato compatibile con il software ArcGIS 10.2.1 (2013) che abbiamo impiegato per le interpretazioni e per la produzione delle mappe. Tecniche di ground-truthing, basate su riprese video e prelievi di sedimento (benna Van Veen 7l), sono state utilizzate per validare il backscatter, dimostrandosi molto efficaci e soddisfacenti per poter descrivere i fondali dal punto di vista biologico e del substrato e quindi degli habitat del canale lagunare. Tutte le informazioni raccolte durante questo studio sono state organizzate all’interno di un geodatabase, realizzato per i dati relativi alla laguna di Venezia.