Building an ontology catalogue for smart cities

Apart from providing semantics and reasoning power to data, ontologies enable and facilitate interoperability across heterogeneous systems or environments. A good practice when developing ontologies is to reuse as much knowledge as possible in order to increase interoperability by reducing heterogeneity across models and to reduce development effort. Ontology registries, indexes and catalogues facilitate the task of finding, exploring and reusing ontologies by collecting them from different sources. This paper presents an ontology catalogue for the smart cities and related domains. This catalogue is based on curated metadata and incorporates ontology evaluation features. Such catalogue represents the first approach within this community and it would be highly useful for new ontology developments or for describing and annotating existing ontologies

Using Provenance for Quality Assessment and Repair in Linked Open Data

As the number of data sources publishing their data on the Web of Data is growing, we are experiencing an immense growth of the Linked Open Data cloud. The lack of control on the published sources, which could be untrustworthy or unreliable, along with their dynamic nature that often invalidates links and causes conflicts or other discrepancies, could lead to poor quality data. In order to judge data quality, a number of quality indicators have been proposed, coupled with quality metrics that quantify the “quality level” of a dataset. In addition to the above, some approaches address how to improve the quality of the datasets through a repair process that focuses on how to correct invalidities caused by constraint violations by either removing or adding triples. In this paper we argue that provenance is a critical factor that should be taken into account during repairs to ensure that the most reliable data is kept. Based on this idea, we propose quality metrics that take into account provenance and evaluate their applicability as repair guidelines in a particular data fusion setting

Knowledge Driven Intelligent Survey Systems for Linguists

This work was supported the EU Marie Curie K-Drive project (286348).Postprin

Software Testing Techniques Revisited for OWL Ontologies

Ontologies are an essential component of semantic knowledge bases and applications, and nowadays they are used in a plethora of domains. Despite the maturity of ontology languages, support tools and engineering techniques, the testing and validation of ontologies is a field which still lacks consolidated approaches and tools. This paper attempts at partly bridging that gap, taking a first step towards the extension of some traditional software testing techniques to ontologies expressed in a widely-used format. Mutation testing and coverage testing, revisited in the light of the peculiar features of the ontology language and structure, can can assist in designing better test suites to validate them, and overall help in the engineering and refinement of ontologies and software based on them

Linked Data Rights 2.0 - Extension of ODRL for Licensing Linked Data

<p>Presented in Leipzig, 2014, at the Vocarnival Workshop within 10th Int. Conf. on Semantic Systems (SEMANTiCS)</p

Ontology evaluation approaches : a case study from agriculture domain

The quality of an ontology very much depends on its validity. Therefore, ontology validation and evaluation is very important task. However, according to the current literature, there is no agreed method or approach to evaluate an ontology. The choice of a suitable approach very much depends on the purpose of validation or evaluation, the application in which the ontology is to be used, and on what aspect of the ontology we are trying to validate or evaluate. We have developed large user centered ontology to represent agricultural information and relevant knowledge in user context for Sri Lankan farmers. In this paper, we described the validation and evaluation procedures we applied to verify the content and examine the applicability of the developed ontology. We obtained expert suggestions and assessments for the criteria used to develop the ontology as well as to obtain user feedback especially from the farmers to measure the ontological commitment. Delphi Method, Modified Delphi Method and OOPS! Web-based tool were used to validate the ontology in terms of accuracy and quality. The implemented ontology is evaluated internally and externally to identify the deficiencies of the artifact in use. An online knowledge base with a SPARQL endpoint was created to share and reuse the domain knowledge. It was also made use of for the evaluation process. A mobile-based application is developed to check user satisfaction on the knowledge provided by the ontology. Since there is no single best or preferred method for ontology evaluation we reviewed various approaches used to evaluate the ontology and finally identified classification for ontology evaluation approaches based on our work