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.

Hypergraph Mining for Social Networks

Nowadays, more and more data is collected in large amounts, such that the need of studying it both efficiently and profitably is arising; we want to acheive new and significant informations that weren't known before the analysis. At this time many graph mining algorithms have been developed, but an algebra that could systematically define how to generalize such operations is missing. In order to propel the development of a such automatic analysis of an algebra, We propose for the first time (to the best of my knowledge) some primitive operators that may be the prelude to the systematical definition of a hypergraph algebra in this regard.

Geomorfologia del giacimento di orso delle caverne nella grotta di Govjestica, Valle di Praca, Bosnia-Ercegovina

In 2011 the GSB/USB caving group of Bologna has discovered, in the southern fossil branches of Govjestica cave (Valle di Praça, Bosnia) a fossil deposit of vertebrates containing bones of Ursus spelaeus, Capra ibex, Cricetulus migratorius and Microtus. On the basis of the U/Th ages of the bones, teeth and carbonate flowstone covering the fossils (60 ka), datings carried out in the laboratories of U-Series at Bologna, and on the disposition of the bones, a past connection between Govjestica and the nearby Banja Stjena cave is hypothesised. The closure of this passage has occurred suddenly through a collapse that has forced the last cave bears awakened from their winter sleep to stay blocked in Govjestica, and die. The connecting passage has later been covered with calcite flowstones and is no longer visible. This hypothesis is sustained by the rather scarce number of skeletons of cave bears found in Govjestica (a dozen of skulls against the often large amounts of cave bears found in similar caves): Govjestica cave, and especially the Room of the Bones in its southern part, has been used by cave bears only for a couple of centuries before these parts became inaccessible. Furthermore, the entrance of Banja Stjena cave was probably located close to or at the level of the Praça river, that has excavated its thalweg for around 20 metres in the last 60 ka.

A Collaborative Mobile Crowdsensing System for Smart Cities

Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System.