An Ontology Based Methodology for Satellite Data Semantic Interoperability

Satellites and ocean based observing system consists of various sensors and configurations. These observing systems transmit data in heterogeneous file formats and heterogeneous vocabulary from various data centers. These data centers maintain a centralized data management system that disseminates the observations to various research communities. Currently, different data naming conventions are being used by existing observing systems, thus leading to semantic heterogeneity. In this work, sensor data interoperability and semantics of the data are being addressed through ontologies. The present work provides an effective technical solution to address semantic heterogeneity through semantic technologies. These technologies provide interoperability, capability to build knowledge base, and framework for semantic information retrieval by developing an effective concept vocabulary through domain ontologies. The paper aims at a new methodology to interlink the multidisciplinary and heterogeneous sensor data products. A four phase methodology has been implemented to address satellite data semantic interoperability. The paper concludes with the evaluation of the methodology by linking and interfacing multiple ontologies to arrive at ontology vocabulary for sensor observations. Data from Indian Meteorological satellite INSAT-3D satellite have been used as a typical example to illustrate the concepts. This work on similar lines can also be extended to other sensor observations

An Ontology and Knowledge Graph Infrastructure for Digital Library Knowledge Representation

New technologies for storing and handling knowledge provide unprecedented opportunities for enhanced fruition of ditigal libraries and archives. Going beyond document retrieval based on lexical content or metadata, using the context of documents, and/or of their content, may provide very new ways to put them in perspective and grasp a deeper understanding thereof, also for non-technical users. Several components are needed to support this new perspective: suitable ontological resources to describe such variated knowledge, collaborative tools to collect the precious knowledge scattered across many scholars and practitioners spread all over the world, and to store it in a knowledge base, fruition tools to make the collected knowledge available to all interested stakeholders (scholars, researchers, but also common people). This paper proposes the GraphBRAIN environment as a possible infrastructure. It is a general-purpose tool that allows its users to design and populate knowledge graphs, to collaboratively enrich them, and to exploit advanced fruition tools, consultation and analysis tools. Its functionality may also be provided as a set of Web services to end-user applications. An initial version of the ontology and knowledge graph for digital libraries and archives are also presented and discussed in the paper

Towards a user-friendly solution for collaboratively managing a developed ontology

Ontologies are getting popular for knowledge representation because it is capable of representing the semantics of the knowledge. However, with the evolution of the knowledge, maintaining and support evolution of a developed ontology becomes a complex task. We can get help of domain experts to maintain the ontology as a solution. But, that approach has another problem which is often domain experts do not know about ontology concepts, languages and tools. Also, if we try to accomplish ontology maintenance by the help of domain experts, there should be a technique to maintain ontology collaboratively. In a collaborative ontology development environment, when one user modifying the ontology, other users should also aware of that modification. In order to achieve this awareness, keeping a history of modifications is required. Furthermore, one user’s modifications may conflict with others modifications; therefore, the ontology development system should support that kind of situations too. This study mainly concerns how to maintain the structure of a developed ontology collaboratively. This study follows synchronous collaborative technique by keeping ontology in a central server. Collaborative partners are able to modify and maintain the ontology through user-friendly web-based interfaces. Since the ontology keeps in central place every user knows what modifications happen to the ontology in real time. Also modifications are recorded in a relational database and users are allowed to access those change history when it needed. Versions of the ontology are generated based on modification types. If the modification affects backward compatibility then a new version is created and if not current version is updated. To distinguish different versions, semantic versioning standard is used. The implemented system is validated individually and evaluated by the help of a user group. Validation and evaluation results prove that system is performing as expected

Analysing the trade-off between computational performance and representation richness in ontology-based systems

© Springer Nature Switzerland AG 2019. As the result of the intense research activity of the past decade, Semantic Web technology has achieved a notable popularity and maturity. This technology is leading the evolution of the Web via inter-operability by providing structured metadata. Because of the adoption of rich data models on a large scale to support the representation of complex relationships among concepts and automatic reasoning, the computational performance of ontology-based systems can significantly vary. In the evaluation of such a performance, a number of critical factors should be considered. Within this paper, we provide an empirical framework that yields an extensive analysis of the computational performance of ontology-based systems. The analysis can be seen as a decision tool in managing the constraints of representational requirements versus reasoning performance. Our approach adopts synthetic ontologies characterised by an increasing level of complexity up to OWL 2 DL. The benefits and the limitations of this approach are discussed in the paper