Automatic constructor of domain models without pre-existing corpus

En este proyecto se presenta un constructor automático de modelos de dominios de conocimientos de forma automática sin corpus preexistente para describir semánticamente un contexto. El constructor está basado en técnicas y métodos para la construcción de corpus a partir de fuentes digitales, mediante el desarrollo de librerías de software que automaticen las fases del sistema propuesto. Este proyecto se encuentra en fases de pruebas conceptuales y desarrollo de componentes.This project is about an automatic builder of domain models without pre-existing corpus. The constructor is based on techniques and methods for the construction of corpus from data extracted from digital media, through the use and development of software libraries that automate the phases of the process of building domains. This project is in phases of conceptual testing and component developmentLos desarrollos presentados en este proyecto se llevaron a cabo dentro de la construcción de capacidades de investigación del Centro de Excelencia y Apropiación en Big Data y Data Analytics (CAOBA), liderado por la Pontificia Universidad Javeriana, financiada por el Ministerio de Tecnologías de la Información y Telecomunicaciones de la República de Colombia (MinTIC) (Alianza CAOBA, 2017)

The Hardship That is Internet Deprivation and What it Means for Sentencing: Development of the Internet Sanction and Connectivity for Prisoners

Twenty years ago, the internet was a novel tool. Now it is such an ingrained part of most people’s lives that they experience and exhibit signs of anxiety and stress if they cannot access it. Non-accessibility to the internet can also tangibly set back peoples’ social, educational, financial, and vocational pursuits and interests. In this Article, we argue that the sentencing law needs to be reformed to adapt to the fundamental changes in human behavior caused by the internet. We present three novel and major implications for the sentencing law and practice in the era of the internet. First, we argue that denial of access to the internet should be developed as a discrete sentencing sanction, which can be invoked for relatively minor offenses in much the same way that deprivation of other entitlements or privileges, such as the right to drive a motor vehicle, are currently imposed for certain crimes. Second, we argue that prisoners should have unfettered access to the internet. This would lessen the pain stemming from incarceration in a manner which does not undermine the principal objectives of imprisonment—community protection and infliction of a hardship—while at the same time providing prisoners with the opportunity to develop skills, knowledge, and relationships that will better equip them for a productive life once they are released. Previous arguments that have been made for denying internet access to prisoners are unsound. Technological advances can readily curb supposed risks associated with prisoners using the internet. Finally, if the second recommendation is not adopted, and prisoners continue to be denied access to the internet, there should be an acknowledgement that the burden of imprisonment is greater than is currently acknowledged. The internet is now such an ingrained and important aspect of people’s lives that prohibiting its use is a cause of considerable unpleasantness. This leads to our third proposal: continued denial of the internet to prisoners should result in a recalibration of the pain of imprisonment such that a sentencing reduction should be conferred to prisoners

Semantic Enrichment of Ontology Mappings

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics