<p>Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations.</p> / Report code: LiU-Tek-Lic-2006:6.

Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations. / <p>Report code: LiU-Tek-Lic-2006:6.</p>

No description available.

Els datasets geogràfics representen la realitat mitjançant un conjunt d'entitats temàtiques que sovint no estan definides d'una manera precisa i que diferents persones poden entendre de diferents formes. La integració d'informació geogràfica provinent de diverses fonts presenta un important repte des del punt de vista semàntic. En aquesta tesi es proposa una solució a aquest problema basada en la definició d'un marc semàntic el nucli del qual és una ontologia que representa els conceptes temàtics en un repositori de datasets, així com les relacions entre aquests conceptes. També es proposa un mètode semi-automàtic per fusionar les ontologies d'aplicació dels datasets en el repositori. El marc semàntic permet també la definició de serveis semàntics, en concret la integració en un nou dataset d'informació temàtica provinent de diverses fonts. Finalment, el marc semàntic i els seus serveis s'utilitzaran en un sistema d'indexació i recuperació d'elements multimèdia geo-referenciats a partir del seu contingut geogràfic temàtic. / Los datasets geográficos representan la realidad mediante un conjunto de entidades temáticas que a menudo no están definidas de una manera precisa y que diferentes sujetos pueden entender de distintas formas. La integración de información geográfica proveniente de diversas fuentes presenta un importante reto desde el punto de vista semántico. En esta tesis se propone una solución a este problema basada en la definición de un marco semántico cuyo núcleo es una ontología que representa los conceptos temáticos en un repositorio de datasets, así como las relaciones entre dichos conceptos. También se propone un método semi-automático para fusionar las ontologías de aplicación de los datasets en el repositorio. El marco semántico permite además la definición de servicios semánticos, en concreto la integración en un nuevo dataset de información temática proveniente de diversas fuentes. Finalmente, el marco semántico y sus servicios se utilizarán en un sistema de indexación y recuperación de elementos multimedia geo-referenciados a partir de su contenido geográfico temático. / Geographic datasets represent reality through a set of thematic entities that are often not precisely defined and that may be understood in different ways by different subjects. Integrating geographic information from diverse datasets presents an important challenge from the semantic point of view. A solution to this problem is proposed in this thesis based on the definition of a semantic framework whose core is an ontology that represents the thematic concepts in a repository of datasets as well as their relations. A semi-automatic method is also proposed to merge the application ontologies of the datasets in the repository. The semantic framework supports the definition of semantic services, particularly the integration of the thematic information from diverse datasets in a new one. Finally, the semantic framework and its services have been used in the context of indexing and retrieving geo-referenced multimedia elements based on their thematic geographic content.