The semantics of similarity in geographic information retrieval

Similarity measures have a long tradition in fields such as information retrieval artificial intelligence and cognitive science. Within the last years these measures have been extended and reused to measure semantic similarity; i.e. for comparing meanings rather than syntactic differences. Various measures for spatial applications have been developed but a solid foundation for answering what they measure; how they are best applied in information retrieval; which role contextual information plays; and how similarity values or rankings should be interpreted is still missing. It is therefore difficult to decide which measure should be used for a particular application or to compare results from different similarity theories. Based on a review of existing similarity measures we introduce a framework to specify the semantics of similarity. We discuss similarity-based information retrieval paradigms as well as their implementation in web-based user interfaces for geographic information retrieval to demonstrate the applicability of the framework. Finally we formulate open challenges for similarity research

Semantically-Enabled Sensor Plug & Play for the Sensor Web

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC’s Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research

A Data-Driven Framework for Assisting Geo-Ontology Engineering Using a Discrepancy Index

Geo-ontologies play significant roles in formalizing concepts and relationships in geography as well as in fostering publication, retrieval, reuse, and integration of geographic data within and across domains. The status quo of geo-ontology engineering is that a group of domain experts collaboratively formalize the key concepts and their relationships. On one hand this centralized top-down ontology engineering approach can take into account invaluable expert knowledge and capture our perception of the world correctly in most cases; on the other it might yield biased geo-ontologies and misrepresent some important concepts or the interplay between different concepts due to the fact that such top-down ontology engineering strategy hardly takes into consideration the existing dataset. With an increasing number of Linked Data on the Web, we are able to use such data to assist the traditional geo-ontology engineering process. However, the quality of Linked Data also imposes challenges to this task. This research proposes a framework by modeling the hierarchical structure using a series of data mining algorithms and eventually quantifies the difference between the original ontology and the data mining one with the proposed Discrepancy Index. The Discrepancy Index can help geo-ontology engineers identify as well as quantify potential ontological modeling issues and Linked Data quality issues, thus closing the gap in the dynamic process of geo-ontology engineering

Reasoning-Supported Quality Assurance for Knowledge Bases

The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities

Elaboracao semiautomática de uma ontologia para remédios a partir de textos antigos de medicina e de culinária

Dissertação de mestrado integrado em Informatics EngineeringAs ontologias podem ser descritas como uma estrutura de dados com uma semântica concisa, explícita e central. As ontologias têm sido utilizadas com o intuito de representar um domínio de conhecimento, ou pelo menos parte desse domínio, de forma a que, posteriormente, possam ser efetuadas interrogações sobre si, visando a exploração, de forma equilibrada e sustentada, do conhecimento representado nessa estrutura. Desta forma, facilmente poderá ser descoberto e explorado o conhecimento presente num dado domínio que se pretende representar e descobrir através da utilização deste tipo de estrutura. O processo manual de construção de uma ontologia envolve diversos custos, nos quais se pode destacar, em particular, o custo temporal associado à concepção e implementação de uma ontologia para um grande domínio de conhecimento. É neste contexto que se insere a área da Ontology Learning, na qual são integrados vários métodos de diferentes áreas de machine learning no processo de construção de ontologias, com o intuito de o (semi)automatizar. Dentro das áreas de machine learning poderá ser destacada a área de Natural Language Processing, na qual podemos encontrar um conjunto muito diverso de técnicas que nos permitem identificar e extrair termos/conceitos pertinentes, propriedades e relações a partir de um domínio de conhecimento, representado, por exemplo, na forma de um texto. Dentro das diversas técnicas passíveis de serem aplicadas, pode-se destacar a aplicação de padrões léxico-sintáticos, que visam explorar formalidades linguísticas com o objetivo de retirar pares hiperónimo e hipónimo de maneira a identificar conceitos e relações da ontologia. Esta abordagem foi aplicada num conjunto de textos antigos de culinária, agricultura e medicina dos séculos XVI e XVII, escritos em Português antigo. Após termos criado a ontologia, desenvolvemos um sistema capaz de explorar o conhecimento que ela contém, dando particular atenção à exploração dos diferentes remédios, dos ingredientes que compõem estes remédios, e dos processos de preparação desses mesmos ingredientes. Com este sistema, facultámos uma maneira simples e intuitiva de explorar o conhecimento presente nos diversos medicamentos representados e caraterizados na ontologia desenvolvida.Ontologies can be described as a data structure with concise, explicit, and central semantics. Ontologies have been used in order to represent a knowledge domain, or at least part of that domain, so that later queries can be made over itself, that aim to explore, in a balanced and sustained way, the knowledge represented in this structure. By using this structure, the knowledge that is represented in a certain domain can easily be discovered and explored through methods that can be used to explore this type of structure. The process of manually creating an ontology is filled with costs, in which we can highlight, in particular, the time cost associated with the conception and implementation of an ontology which covers a large domain of knowledge. In light of this issue, the area of Ontology Learning aims to cover these issues, where methods from different areas of machine learning are integrated in the ontology construction process, with the aim of (semi)automating. Within the machine learning areas, the Natural Language Processing area can be highlighted, where a large set of techniques can be applied in order to identify and extract relevant terms/concepts, properties and relationships from a domain of knowledge, represented for example in the form of a text. Among the various techniques that can be applied, one such technique can be through the application of lexical-syntactic patterns, which aim to explore linguistic formalities with the goal of extracting hypernym and hyponym pairs so that the relationships, concepts can be identified. This approach has been applied to a set of classical culinary, agricultural, and medical texts from the XVI and XVII centuries, written in classical Portuguese. After having created the ontology, a system was developed so that the knowledge contained within it could be explored, giving particular attention to the different remedies, the ingredients contained within the remedies, and the preparation process of these ingredients. With this system, an easy and intuitive way to explore the knowledge present in the different medicines represented within the ontology was developed