Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

A structural and quantitative analysis of the webof linked data and its components to perform retrieval data

Esta investigación consiste en un análisis cuantitativo y estructural de la Web of Linked Data con el fin de mejorar la búsqueda de datos en distintas fuentes. Para obtener métricas cuantitativas de la Web of Linked Data, se aplicarán técnicas estadísticas. En el caso del análisis estructural haremos un Análisis de Redes Sociales (ARS). Para tener una idea de la Web of Linked Data para poder hacer un análisis, nos ayudaremos del diagrama de la Linking Open Data (LOD) cloud. Este es un catálogo online de datasets cuya información ha sido publicada usando técnicas de Linked Data. Los datasets son publicados en un lenguaje llamado Resource Description Framework (RDF), el cual crea enlaces entre ellos para que la información pudiera ser reutilizada. El objetivo de obtener un análisis cuantitativo y estructural de la Web of Linked Data es mejorar las búsquedas de datos. Para ese propósito nosotros nos aprovecharemos del uso del lenguaje de marcado Schema.org y del proyecto Linked Open Vocabularies (LOV). Schema.org es un conjunto de etiquetas cuyo objetivo es que los Webmasters pudieran marcar sus propias páginas Web con microdata. El microdata es usado para ayudar a los motores de búsqueda y otras herramientas Web a entender mejor la información que estas contienen. LOV es un catálogo para registrar los vocabularios que usan los datasets de la Web of Linked Data. Su objetivo es proporcionar un acceso sencillo a dichos vocabularios. En la investigación, vamos a desarrollar un estudio para la obtención de datos de la Web of Linked Data usando las fuentes mencionadas anteriormente con técnicas de “ontology matching”. En nuestro caso, primeros vamos a mapear Schema.org con LOV, y después LOV con la Web of Linked Data. Un ARS de LOV también ha sido realizado. El objetivo de dicho análisis es obtener una idea cuantitativa y cualitativa de LOV. Sabiendo esto podemos concluir cosas como: cuales son los vocabularios más usados o si están especializados en algún campo o no. Estos pueden ser usados para filtrar datasets o reutilizar información

DisMatch results for OAEI 2016

DisMatch is an experimental ontology matching system based on the use of corpus based distributional measure for approximating se- mantic relatedness. Through the use of a domain-related corpus, the measure can be applied to a problem focused on the domain of the cor- pus, here being the Disease and Phenotype track. In this paper, we aim to briefly present the proposed approach and the results obtained in the evaluation, as well as some early conclusions regarding the performance of DisMatch.Ministerio de Educación y Ciencia TIN2014-58304-RJunta de Andalucía P11-TIC-7529Junta de Andalucía P12-TIC-151

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications