Spatial groundings for meaningful symbols

The increasing availability of ontologies raises the need to establish relationships and make inferences across heterogeneous knowledge models. The approach proposed and supported by knowledge representation standards consists in establishing formal symbolic descriptions of a conceptualisation, which, it has been argued, lack grounding and are not expressive enough to allow to identify relations across separate ontologies. Ontology mapping approaches address this issue by exploiting structural or linguistic similarities between symbolic entities, which is costly, error-prone, and in most cases lack cognitive soundness. We argue that knowledge representation paradigms should have a better support for similarity and propose two distinct approaches to achieve it. We first present a representational approach which allows to ground symbolic ontologies by using Conceptual Spaces (CS), allowing for automated computation of similarities between instances across ontologies. An alternative approach is presented, which considers symbolic entities as contextual interpretations of processes in spacetime or Differences. By becoming a process of interpretation, symbols acquire the same status as other processes in the world and can be described (tagged) as well, which allows the bottom-up production of meaning

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 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

Soft ontologies, spatial representations and multi-perspective exploration

Abstract: It is against the dynamically evolving nature of many contemporary media applications to be analysed in terms of conventional rigid ontologies that rely on expertise-based fixed categories and hierarchical structure. Many of these rely on sharing 'folksonomies', personal descriptions of information and objects for one's own retrieval. Such applications involve many feedback mechanisms via the community, and have been shown to have emergent properties of complex dynamic systems. We propose that such dynamically evolving information domains can be more usefully described by means of a soft ontology, a dynamically flexible and inherently spatial metadata approach for ill-defined domains. Our contribution is (1) the elaboration of the so far intuitive concept of soft ontology in a way that supports conceptualizing dynamically evolving domains. Further, our approach proposes (2) a whole new mode of interaction with information domains by means of recurring exploration of an information domain from multiple perspectives in search of more comprehensive understanding of it, i.e. multi-perspective exploration. We demonstrate this concept with an example of collaborative tagging in an educational context

Two-fold Semantic Web service matchmaking – applying ontology mapping for service discovery

Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. Since SWS annotations usually are created by distinct SWS providers, semantic-level mediation, i.e. mediation between concurrent semantic representations, is a key requirement for SWS discovery. Since semantic-level mediation aims at enabling interoperability across heterogeneous semantic representations, it can be perceived as a particular instantiation of the ontology mapping problem. While recent SWS matchmakers usually rely on manual alignments or subscription to a common ontology, we propose a two-fold SWS matchmaking approach, consisting of (a) a general-purpose semantic-level mediator and (b) comparison and matchmaking of SWS capabilities. Our semantic-level mediation approach enables the implicit representation of similarities across distinct SWS by grounding service descriptions in so-called Mediation Spaces (MS). Given a set of SWS and their respective grounding, a SWS matchmaker automatically computes instance similarities across distinct SWS ontologies and matches the request to the most suitable SWS. A prototypical application illustrates our approach

Models and Modelling between Digital and Humanities: A Multidisciplinary Perspective

This Supplement of Historical Social Research stems from the contributions on the topic of modelling presented at the workshop “Thinking in Practice”, held at Wahn Manor House in Cologne on January 19-20, 2017. With Digital Humanities as starting point, practical examples of model building from different disciplines are considered, with the aim of contributing to the dialogue on modelling from several perspectives. Combined with theoretical considerations, this collection illustrates how the process of modelling is one of coming to know, in which the purpose of each modelling activity and the form in which models are expressed has to be taken into consideration in tandem. The modelling processes presented in this volume belong to specific traditions of scholarly and practical thinking as well as to specific contexts of production and use of models. The claim that supported the project workshop was indeed that establishing connections between different traditions of and approaches toward modelling is vital, whether these connections are complementary or intersectional. The workshop proceedings address an underpinning goal of the research project itself, namely that of examining the nature of the epistemological questions in the different traditions and how they relate to the nature of the modelled objects and the models being created. This collection is an attempt to move beyond simple representational views on modelling in order to understand modelling processes as scholarly and cultural phenomena as such