Ontology Alignment Architecture for Semantic Sensor Web Integration

Abstract: Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall

Contribución a la alineación de ontologías utilizando lógica difusa

En la actualidad, con el aumento de la cantidad de información disponible en Internet se hace cada vez más necesario crear mecanismos para facilitar la organización el intercambio de información y conocimiento entre las aplicaciones. La Web Semántica está destinada a resolver una de las carencias fundamentales de la Web actual, que es la falta de capacidad de las representaciones para expresar significados. Esta tarea se puede simplificar enormemente aądiendo información semántica y de contexto a las formas actuales de representación del conocimiento, utilizadas en la Web, de modo que los equipos puedan procesar, interpretar y conectar la información presentada en la WWW. Las ontologías se han convertido en un componente crucial dentro de la Web semántica, ya que permiten el diseǫ de exhaustivos y rigurosos esquemas conceptuales para facilitar la comunicación y el intercambio de información entre diferentes sistemas y entidades. Sin embargo, la heterogeneidad en la representación del conocimiento en las ontologías dificulta la interacción entre las aplicaciones que utilizan este conocimiento. Por ello, para compartir información, cuando se utiliza vocabularios heterogéneos se debe poder traducir los datos de un marco ontológico a otro. El proceso de encontrar correspondencias entre ontologías diferentes se conoce como alineación de ontologías. En esta tesis doctoral se propone un método de alineación de ontologías utilizando técnicas de lógica difusa para combinar diversas medidas de similitud entre entidades de ontologías diferentes. Las medidas de similitud propuestas se basan en dos elementos fundamentales de las ontologías: la terminología y la estructura. En cuanto a la terminología se propone una medida de similitud lingüística utilizando varias relaciones léxicas entre los nombres de las entidades, combinada con una medida de similitud semántica que tiene en cuenta la información del contexto de las entidades en las ontologías. En cuanto a la estructura se proponen medidas de similitud que utilizan tanto la estructura relacional como la estructura interna de los conceptos en las ontologías

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

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen