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.

Design and implementation of a fft pruning engine for DSA-enabled cognitive radios.

This research work presents the design and implementation of a FFT pruning block, which is an extension to the FFT core for OFDM demodulation, enabling run-time 8 pruning of the FFT algorithm, without any restrictions on the distribution pattern of the active/inactive sub-carriers. The design and implementation of FFT processor core is not the part of this work. The whole design was prototyped on an ALTERA STRATIX V FPGA to evaluate the performance of the pruning engine. Synthesis and simulation results showed that the logic overhead introduced by the pruning block is limited to a 10% of the total resources utilization. Moreover, in presence of a medium-high scattering of the sub-carriers, power and energy consumption of the FFT core were reduced by a 30% factor.

Grassezza e obesità nei giornali italiani: una questione di rappresentazioni

Our contemporary society still sees the fat body as a problematic issue. This refusal originated as a racist control practice and developed as an esthetical and medical problem, resulting in the stigmatization and discrimination of this marginalized social group. Drawing on a corpus of about 157,000 words, the present study aims to shed light on how journalistic language might play a role in reinforcing prejudices towards fat people and, consequently, their stigmatization. The corpus contains 305 articles on fatness and/or obesity that were taken from six Italian newspapers representing different political leanings. The analysis is based on three main research questions: which frames are used to represent fat people in Italian newspapers? Do women get a particular treatment when talked about in relation to fatness/obesity? Do the articles employ any stigmatizing discourse strategies? Results show particular emphasis on the medical aspects of fatness/obesity, in terms of consequences on fat people’s health due to their lifestyle choices, with little to no consideration of societal responsibility around weight stigma. There is also evidence of women being talked about more than men in connection with this topic, especially with regards to their duty to appear in a certain way and their responsibility as mothers. Furthermore, articles display a vast amount of stigmatizing discourses, that go from offensive referential and predicational strategies, to an explicit mockery of fat people. In conclusion, the journalistic discourses on fatness/obesity analyzed in the present study show problematic traits possibly affecting fat people’s quality of life and should be examined more extensively as to establish a generalizing pattern by taking a larger set of data into account.