Representing time and space for the semantic web

Representation of temporal and spatial information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as "before" or "overlaps") since precise dates or coordinates are not always available. This work proposes several temporal representations for time points and intervals and spatial topological representations in ontologies by means of OWL properties and reasoning rules in SWRL. All representations are fully compliant with existing Semantic Web standards and W3C recommendations. Although qualitative representations for temporal interval and point relations and spatial topological relations exist, this is the first work proposing representations combining qualitative and quantitative information for the Semantic Web. In addition to this, several existing and proposed approaches are compared using different reasoners and experimental results are presented in detail. The proposed approach is applied to topological relations (RCC5 and RCC8) supporting both qualitative and quantitative (i.e., using coordinates) spatial relations. Experimental results illustrate that reasoning performance differs greatly between different representations and reasoners. To the best of our knowledge, this is the first such experimental evaluation of both qualitative and quantitative Semantic Web temporal and spatial representations. In addition to the above, querying performance using SPARQL is evaluated. Evaluation results demonstrate that extracting qualitative relations from quantitative representations using reasoning rules and querying qualitative relations instead of directly querying quantitative representations increases performance at query time

Spatial ontologies for detecting abnormal maritime behaviour

International audienceThe upsurge in piracy and the impact of recent environmental disasters have highlighted the need to improve maritime surveillance. Governmental and private initiatives have developed monitoring systems with improved acquisition and analysis capabilities. These systems rely on one major component, namely the detection of abnormal ship behaviour. This implies a detailed formalisation of expert knowledge. However, the quantity of data, the complexity of situations, the failure to take into account their spatial characteristics and the potential for the same scenario to be interpreted in different ways have proved to be significant problems. We therefore propose a new prototype for the analysis of abnormal ship behaviour. The system is based on a spatial ontology associated with a geographical inference engine. It automatically identifies suspicious vessels and associates them with probable behaviours defined by operational staff

Engineering Temporal and Spatial Aspects in OWL using Patterns

WWW is a huge, open, heterogeneous system, however its contents data is mainly human oriented. The Semantic Web needs to assure that data is readable and “understandable” to intelligent software agents, though the use of explicit and formal semantics. Ontologies constitute a privileged artifact for capturing the semantic of the WWW data. Temporal and spatial dimensions are transversal to the generality of knowledge domains and therefore are fundamental for the reasoning process of software agents. Representing temporal/spatial evolution of concepts and their relations in OWL (W3C standard for ontologies) it is not straightforward. Although proposed several strategies to tackle this problem but there is still no formal and standard approach. This work main goal consists of development of methods/tools to support the engineering of temporal and spatial aspects in intelligent systems through the use of OWL ontologies. An existing method for ontology engineering, Fonte was used as framework for the development of this work. As main contributions of this work Fonte was re-engineered in order to: i) support the spatial dimension; ii) work with OWL Ontologies; iii) and support the application of Ontology Design Patterns. Finally, the capabilities of the proposed approach were demonstrated by engineering time and space in a demo ontology about football.A World WideWeb (WWW) é uma rede de dados enorme, aberta, muito rica, heterogénea e não controlada. Contudo, os dados existentes na rede são principalmente orientados ao consumo humano. A Semantic Web, de acordo com a perspectiva de Berners-Lee, deve fornecer condições para que a informação publicada seja lida e interpretada/compreendida por máquinas (agentes), através do enriquecimento semântico formal e explícito. As ontologias são a especificação formal de uma conceptualização partilhada e como tal constituem um artefacto privilegiado para capturar a semântica de um modelo. O formato standard proposto pela W3C (World Wide Web Consortium) para a representação de ontologias no contexto da WWW é o OWL (Web Ontology Language). As dimensões temporal e espacial são transversais à generalidade dos domínios. No processo de entendimento e raciocínio por agentes é crucial a consideração das dimensões temporal e espacial, em particular em tarefas como a análise de narrativas, contextualização, processamento de língua natural ou planeamento. Por exemplo, uma pessoa pode desempenhar vários papéis numa organização no decorrer do tempo; um objecto passa por diversas fases durante o processo de fabrico; ou o planeamento de uma viagem à Europa deve obedecer a diversas restrições temporais e espaciais. Apesar de os humanos demonstrarem uma capacidade inata para lidar com o tempo e o espaço, os agentes inteligentes de software precisam de especificações formais. Contudo, apesar da vasta investigação que tem sido levada a cabo no domínio da engenharia temporal/espacial esta é ainda uma tarefa complexa, trabalhosa e sujeita a erros, visto que é necessário ter conhecimento específico sobre o domínio a modelar e também sobre as teorias que modelam/capturam o tempo e o espaço. Integrar as dimensões temporal e espacial em sistemas inteligentes é uma tarefa complexa e propensa a erros, principalmente porque: 1. muitas vezes o Engenheiro de Conhecimento tem uma percepção intuitiva e informal do tempo e do espaço, enquanto os modelos existentes são formais e complexos, resultando em sistemas nos quais não é possível explorar adequadamente estas dimensões; 2. as dimensões extra, resultantes das componentes temporal e espacial, tornam a ontologia mais complexa, aumentando a dificuldade do processo de verificação e a garantia da completude e consistência do sistema; 3. diferentes intervenientes têm diferentes percepções do tempo e do espaço. Em particular, representar e raciocinar sobre a evolução temporal de conceitos e suas relações considerando ontologias em OWL enfrenta problemas adicionais. A linguagem OWL baseia-se na utilização de relações binárias, o que lhe confere enormes vantagens no processamento automático mas que impõe limitações ao nível da expressividade, tornando complexo representar relações que envolvam mais do que dois argumentos (como por exemplo a caracterização temporal ou espacial de relações). A comunidade científica tem estudado várias formas para fazer face a este problema, nomeadamente: 1. extensões da Lógica Descritiva (DL) com operadores temporais e espaciais; 2. extensões do esquema formal do OWL; 3. aplicação de técnicas de gestão de versões permitindo registar o histórico da evolução da ontologia; 4. ou ainda a criação de esquemas mais complexos para a representação da informação como a criação de conceitos auxiliares para simular a existência de relações n-árias. O principal objectivo deste trabalho consistiu no desenvolvimento de métodos e ferramentas capazes de suportar a engenharia de aspectos temporais e espaciais em sistemas inteligentes através da utilização de ontologias codificadas na linguagem OWL. Uma metodologia de engenharia de ontologias existente chamada Fonte foi utilizada como framework no desenvolvimento deste trabalho. Este método foi aplicado com sucesso na engenharia de aspectos temporais em sistemas inteligentes utilizando ontologias no formato F-Logic. O Fonte utiliza uma abordagem de dividir-para-conquistar de forma que a modelação de domínios complexos pode ser realizada através da composição de diferentes ontologias que definem as diferentes categorias de conhecimento envolvidas no domínio. O Fonte foi utilizado na engenharia dos aspectos temporais em ontologias. Como resultado deste trabalho foi realizada a reengenharia do método Fonte de forma a suportar também a dimensão espacial e a aplicação semiautomática de padrões de desenvolvimento de ontologias (PDO). Em particular este trabalho consistiu no desenvolvimento de: 1. uma linguagem de regras que permite a implementação de PDO e a sua aplicação através da metodologia Fonte 2. mecanismos de verificação que garantem a consistência da ontologia de domínio durante o processo de engenharia; 3. mecanismos de criação automática de propostas baseados em algoritmos de pesquisa semântica e estrutural; 4. ferramenta gráfica de suporte ao método Fonte. As capacidades da metodologia e ferramentas propostas e desenvolvidas foram demonstradas através da engenharia temporal e espacial de uma ontologia do domínio do futebol

SOWL QL: Querying Spatio - Temporal Ontologies in OWL

We introduce SOWL QL, a query language for spatio-temporal information in ontologies. Buildingupon SOWL (Spatio-Temporal OWL), an ontology for handling spatio-temporal information in OWL, SOWL QL supports querying over qualitative spatio-temporal information (expressed using natural language expressions such as “before”, “after”, “north of”, “south of”) rather than merely quantitative information (exact dates, times, locations). SOWL QL extends SPARQL with a powerful set of temporal and spatial operators, including temporal Allen topological, spatial directional and topological operations or combinations of the above. SOWL QL maintains simplicity of expression and also, upward and downward compatibility with SPARQL. Query translation in SOWL QL yields SPARQL queries implying that, querying spatio-temporal ontologies using SPARQL is still feasible but suffers from several drawbacks the most important of them being that, queries in SPARQL become particularly complicated and users must be familiar with the underlying spatio-temporal representation (the “N-ary relations” or the “4D-fluents” approach in this work). Finally, querying in SOWL QL is supported by the SOWL reasoner which is not part of the standard SPARQL translation. The run-time performance of SOWL QL has been assessed experimentally in a real data setting. A critical analysis of its performance is also presented

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 semantic web rule language for geospatial domains

Retrieval of geographically-referenced information on the Internet is now a common activity. The web is increasingly being seen as a medium for the storage and exchange of geographic data sets in the form of maps. The geospatial-semantic web (GeoWeb) is being developed to address the need for access to current and accurate geo-information. The potential applications of the GeoWeb are numerous, ranging from specialised application domains for storing and analysing geo-information to more common applications by casual users for querying and visualising geo-data, e.g. finding locations of services, descriptions of routes, etc. Ontologies are at the heart of W3C's semantic web initiative to provide the necessary machine understanding to the sheer volumes of information contained on the internet. For the GeoWeb to succeed the development of ontologies for the geographic domain are crucial. Semantic web technologies to represent ontologies have been developed and standardised. OWL, the Web Ontology Language, is the most expressive of these enabling a rich form of reasoning, thanks to its formal description logic underpinnings. Building geo-ontologies involves a continuous process of update to the originally modelled data to reflect change over time as well as to allow for ontology expansion by integrating new data sets, possibly from different sources. One of the main challenges in this process is finding means of ensuring the integrity of the geo-ontology and maintaining its consistency upon further evolution. Representing and reasoning with geographic ontologies in OWL is limited. Firstly, OWL is not an integrity checking language due to it's non-unique name and open world assumptions. Secondly, it can not represent spatial datatypes, can not compute information using spatial operators and does not have any form of spatial index. Finally, OWL does not support complex property composition needed to represent qualitative spatial reasoning over spatial concepts. To address OWL's representational inefficiencies, new ontology languages have been proposed based on the intersection or union of OWL (in particular the DL family corresponding to OWL) with logic programs (rule languages). In this work, a new Semantic Web Spatial Rule Language (SWSRL) is proposed, based on the syntactic core of the Description Logic Programs paradigm (DLP), and the semantics of a Logic Program. The language is built to support the expression of geospatial ontological axioms and geospatial integrity and deduction rules. A hybrid framework to integrate both qualitative symbolic information in SWSRL with quantitative, geometric information using spatial datatypes in a spatial database is proposed. Two notable features of SWSRL are 1) the language is based on a prioritised de fault logic that allows the expression of default integrity rules and their exceptions and 2) the implementation of the language uses an interleaved mode of inference for on the fly computation (either qualitative or quantitative) deduction of spatial relations. SWSRL supports an OGC complaint spatial syntax, and a standardised definition of rule meta data. Both features aid the construction, description, identification and categorisation of designed and implemented rules within large rule sets. The language and the developed engine are evaluated using synthetic as well as real data sets in the context of developing geographic ontologies for geographic information retrieval on the Semantic Web. Empirical experiments are also presented to test the scalability and applicability of the developed framework

Automatic Geospatial Data Conflation Using Semantic Web Technologies

Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes

Contributions for the exploitation of Semantic Technologies in Industry 4.0

120 p.En este trabajo de investigación se promueve la utilización de las tecnologías semánticas, en el entorno de la Industria 4.0, a través de tres contribuciones enfocadas en temas correspondientes a la fabricación inteligente: las descripciones enriquecidas de componentes, la visualización y el análisis de los datos, y la implementación de la Industria 4.0 en PyMEs.La primera contribución es una ontología llamada ExtruOnt, la cual contiene descripciones semánticas de un tipo de máquina de fabricación (la extrusora). En esta ontología se describen los componentes, sus conexiones espaciales, sus características, sus representaciones en tres dimensiones y, finalmente, los sensores utilizados para capturar los datos. La segunda contribución corresponde a un sistema de consulta visual en el cual se utiliza la ontología ExtruOnt y una representación en 2D de la extrusora para facilitar a los expertos de dominio la visualización y la extracción de conocimiento sobre el proceso de fabricación de una manera rápida y sencilla. La tercera contribución consiste en una metodología para la implementación de la Industria 4.0 en PyMEs, orientada al ciclo de vida del cliente y potenciada por el uso de tecnologías Semánticas y tecnologías de renderizado 3D.Las contribuciones han sido desarrolladas, aplicadas y validadas bajo un escenario de fabricación real