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 Reasoning for the Semantic Web -Use Cases and Technological Challenges

The goal of semantic web research is to turn the World-Wide Web into a Web of Data that can be processed automatically to a much larger extend than possible with traditional web technology. Important features of the solution currently being developed is the ability to link data from from different sources and to provide formal definitions of the intended meaning of the terminology used in different sources as a basis for deriving implicit information and for conflict detection. Both requires the ability to reason about the definition of terms. With the development of OWL as the standard language for representing terminological knowledge, reasoning in description logics has been determined as the major technique for performing this reasoning So far little attention has been paid to the problem of representing and reasoning about space and time on the semantic web. In particular, existing semantic web languages are not well suited for representing these aspects as they require to operate over metric spaces that behave fundamentally different from the abstract interpretation domains description logics are based on. Nevertheless, there is a strong need to integrate reasoning about space and time into existing semantic web technologies especially because more and more data available on the web has a references to space and time. Images taken by digital cameras are a good example of such data as they come with a time stamp and geographic coordinates. In this paper, we concentrate on spatial aspects and discuss different use case for reasoning about spatial aspects on the (semantic) web and possible technological solutions for these use cases. Based on these discussions we conclude that the actual open problem is not existing technologies for terminological or spatial reasoning, but the lack of an established mechanism for combining the two. The Case for Spatial Queries One of the most central functionality that should be supported by semantic web technology is query answering over web data. The primary language for this purpose i

Bridging between sensor measurements and symbolic ontologies through conceptual spaces

The increasing availability of sensor data through a variety of sensor-driven devices raises the need to exploit the data observed by sensors with the help of formally specified knowledge representations, such as the ones provided by the Semantic Web. In order to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the potential infinite variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an ontology for CS which allows to refine symbolic concepts as CS and to ground instances to so-called prototypical members described by vectors. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of prototypical members, the most similar instance can be identified. In that, we provide a means to bridge between the real-world as observed by sensors and symbolic representations. We also propose an initial implementation utilizing our approach for measurement-based Semantic Web Service discovery

Blending the physical and the digital through conceptual spaces

The rise of the Internet facilitates an ever increasing growth of virtual, i.e. digital spaces which co-exist with the physical environment, i.e. the physical space. In that, the question arises, how physical and digital space can interact synchronously. While sensors provide a means to continuously observe the physical space, several issues arise with respect to mapping sensor data streams to digital spaces, for instance, structured linked data, formally represented through symbolic Semantic Web (SW) standards such as OWL or RDF. The challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the vast variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an approach which allows to refine symbolic concepts as CS and to ground ontology instances to so-called prototypical members which are vectors in the CS. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of CS members, the most similar instance can be identified. In that, we provide a means to bridge between the physical space, as observed by sensors, and the digital space made up of symbolic representations

Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches

Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches

Semantic coordination through programmable Tuple spaces

Two of the main features of today complex software systems like pervasive computing systems and Internet-based applications are distribution and openness. Distribution revolves around three orthogonal dimensions: (i) distribution of control|systems are characterised by several independent computational entities and devices, each representing an autonomous and proactive locus of control; (ii) spatial distribution|entities and devices are physically distributed and connected in a global (such as the Internet) or local network; and (iii) temporal distribution|interacting system components come and go over time, and are not required to be available for interaction at the same time. Openness deals with the heterogeneity and dynamism of system components: complex computational systems are open to the integration of diverse components, heterogeneous in terms of architecture and technology, and are dynamic since they allow components to be updated, added, or removed while the system is running. The engineering of open and distributed computational systems mandates for the adoption of a software infrastructure whose underlying model and technology could provide the required level of uncoupling among system components. This is the main motivation behind current research trends in the area of coordination middleware to exploit tuple-based coordination models in the engineering of complex software systems, since they intrinsically provide coordinated components with communication uncoupling and further details in the references therein. An additional daunting challenge for tuple-based models comes from knowledge-intensive application scenarios, namely, scenarios where most of the activities are based on knowledge in some form|and where knowledge becomes the prominent means by which systems get coordinated. Handling knowledge in tuple-based systems induces problems in terms of syntax - e.g., two tuples containing the same data may not match due to differences in the tuple structure - and (mostly) of semantics|e.g., two tuples representing the same information may not match based on a dierent syntax adopted. Till now, the problem has been faced by exploiting tuple-based coordination within a middleware for knowledge intensive environments: e.g., experiments with tuple-based coordination within a Semantic Web middleware (surveys analogous approaches). However, they appear to be designed to tackle the design of coordination for specic application contexts like Semantic Web and Semantic Web Services, and they result in a rather involved extension of the tuple space model. The main goal of this thesis was to conceive a more general approach to semantic coordination. In particular, it was developed the model and technology of semantic tuple centres. It is adopted the tuple centre model as main coordination abstraction to manage system interactions. A tuple centre can be seen as a programmable tuple space, i.e. an extension of a Linda tuple space, where the behaviour of the tuple space can be programmed so as to react to interaction events. By encapsulating coordination laws within coordination media, tuple centres promote coordination uncoupling among coordinated components. Then, the tuple centre model was semantically enriched: a main design choice in this work was to try not to completely redesign the existing syntactic tuple space model, but rather provide a smooth extension that { although supporting semantic reasoning { keep the simplicity of tuple and tuple matching as easier as possible. By encapsulating the semantic representation of the domain of discourse within coordination media, semantic tuple centres promote semantic uncoupling among coordinated components. The main contributions of the thesis are: (i) the design of the semantic tuple centre model; (ii) the implementation and evaluation of the model based on an existent coordination infrastructure; (iii) a view of the application scenarios in which semantic tuple centres seem to be suitable as coordination media