4,769 research outputs found
Winnowing ontologies based on application use
The requirements of specific applications and services are often over estimated when ontologies are reused or built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services,and investigates the possibility of relying on this usage information to winnow that ontology
Git4Voc: Git-based Versioning for Collaborative Vocabulary Development
Collaborative vocabulary development in the context of data integration is
the process of finding consensus between the experts of the different systems
and domains. The complexity of this process is increased with the number of
involved people, the variety of the systems to be integrated and the dynamics
of their domain. In this paper we advocate that the realization of a powerful
version control system is the heart of the problem. Driven by this idea and the
success of Git in the context of software development, we investigate the
applicability of Git for collaborative vocabulary development. Even though
vocabulary development and software development have much more similarities
than differences there are still important differences. These need to be
considered within the development of a successful versioning and collaboration
system for vocabulary development. Therefore, this paper starts by presenting
the challenges we were faced with during the creation of vocabularies
collaboratively and discusses its distinction to software development. Based on
these insights we propose Git4Voc which comprises guidelines how Git can be
adopted to vocabulary development. Finally, we demonstrate how Git hooks can be
implemented to go beyond the plain functionality of Git by realizing
vocabulary-specific features like syntactic validation and semantic diffs
Facets, Tiers and Gems: Ontology Patterns for Hypernormalisation
There are many methodologies and techniques for easing the task of ontology
building. Here we describe the intersection of two of these: ontology
normalisation and fully programmatic ontology development. The first of these
describes a standardized organisation for an ontology, with singly inherited
self-standing entities, and a number of small taxonomies of refining entities.
The former are described and defined in terms of the latter and used to manage
the polyhierarchy of the self-standing entities. Fully programmatic development
is a technique where an ontology is developed using a domain-specific language
within a programming language, meaning that as well defining ontological
entities, it is possible to add arbitrary patterns or new syntax within the
same environment. We describe how new patterns can be used to enable a new
style of ontology development that we call hypernormalisation
Towards Context Driven Modularization of Large Biomedical Ontologies
Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently occurring concepts in the domain corpus define the application context and can therefore potentially yield the relevant ontology modules. We illustrate our approach on an example case that involves a large ontology on human anatomy and report on our first manual experiments
Semantic model-driven development of web service architectures.
Building service-based architectures has become a major area of interest since the advent of Web services. Modelling these architectures is a central activity. Model-driven development is a recent approach to developing software systems based on the idea of making models the central artefacts for design representation, analysis, and code generation.
We propose an ontology-based engineering methodology for semantic model-driven composition and transformation of Web service architectures. Ontology technology as a logic-based knowledge representation and reasoning framework can provide answers to the needs of sharable and reusable semantic models and descriptions needed for service engineering. Based on modelling, composition and code generation techniques for service architectures, our approach provides a methodological framework for ontology-based semantic service architecture
Semantic-driven matchmaking of web services using case-based reasoning
With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services
Ontology-based collaborative framework for disaster recovery scenarios
This paper aims at designing of adaptive framework for supporting
collaborative work of different actors in public safety and disaster recovery
missions. In such scenarios, firemen and robots interact to each other to reach
a common goal; firemen team is equipped with smart devices and robots team is
supplied with communication technologies, and should carry on specific tasks.
Here, reliable connection is mandatory to ensure the interaction between
actors. But wireless access network and communication resources are vulnerable
in the event of a sudden unexpected change in the environment. Also, the
continuous change in the mission requirements such as inclusion/exclusion of
new actor, changing the actor's priority and the limitations of smart devices
need to be monitored. To perform dynamically in such case, the presented
framework is based on a generic multi-level modeling approach that ensures
adaptation handled by semantic modeling. Automated self-configuration is driven
by rule-based reconfiguration policies through ontology
EAGLEâA Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatioâtemporal correlations. Most semantic approaches do not have spatioâtemporal support. Some of them have attempted to provide full spatioâtemporal support, but have poor performance for complex spatioâtemporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatioâtemporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatioâtemporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
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