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.

Bioactivity of extracts from extremophile microalgae Coccomyxa sp. on chloride cells in operculum tissue of Fundulus heteroclitus

Microalgae have been studied because of their great potential as a source of new compounds with important value for biotechnology and to understand their strategies of survival in extreme environments. The microalgae Coccomyxa sp., studied in this thesis, is a poly-extremophile witch was isolated from the acid mine drainage of S. Domingos mine. This environment is characterized by low pH (<3) and high concentration of metals, such as copper and iron. The main purpose of the present work was to evaluate the potential bioactivity in an ex-vivo animal model (Fundulus heteroclitus), and expression on selected genes, of cellular extracts obtained from cultures of Coccomyxa sp. at pH 7 without or with exposure to copper (0.6mM Cu²+). The extracts of Coccomyxa sp. cultured at pH 7 exposed to copper show a great potential to be used as epithelial NKCC inhibitors, revealing their potential use as diuretics, but did not show significant effects on gene expression. Coccomyxa sp. could be a good source of cellular extracts with a great potential to be used in pharmaceutical and biotechnology industries.