Detecting Good Practices and Pitfalls when Publishing Vocabularies on the Web

The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies bringing their semantic to the data being published. These ontologies should be evaluated at different stages, both during their development and their publication. As important as correctly modelling the intended part of the world to be captured in an ontology, is publishing, sharing and facilitating the (re)use of the obtained model. In this paper, 11 evaluation characteristics, with respect to publish, share and facilitate the reuse, are proposed. In particular, 6 good practices and 5 pitfalls are presented, together with their associated detection methods. In addition, a grid-based rating system is generated. Both contributions, the set of evaluation characteristics and the grid system, could be useful for ontologists in order to reuse existing LD vocabularies or to check the one being built

Strategy for energy measurement and interoperability

garciacastro2014ano abstrac

Strategy for energy measurement and interoperability

garciacastro2014ano abstrac

Strategy for Energy Management System Interoperability

priego2013aThe goal of the Ready4SmartCities project is to support energy data interoperability in the context of SmartCities. It keeps a precise focus on building and urban data. Work package 2 is more specifically concerned with identifying the knowledge and data resources available or needed, that support energy management system interoperability. This deliverable defines the strategy to be used in WP2 for achieving its goal. It is made of two parts: identifying domains and stakeholders specific to the WP2 activity and the methodology used in WP2 and WP3

Citizen Science in Archaeology : Developing a Collaborative Web Service for Archaeological Finds in Finland

Peer reviewe

Strategy for Energy Management System Interoperability

priego2013aThe goal of the Ready4SmartCities project is to support energy data interoperability in the context of SmartCities. It keeps a precise focus on building and urban data. Work package 2 is more specifically concerned with identifying the knowledge and data resources available or needed, that support energy management system interoperability. This deliverable defines the strategy to be used in WP2 for achieving its goal. It is made of two parts: identifying domains and stakeholders specific to the WP2 activity and the methodology used in WP2 and WP3

Pitfalls in Ontologies and TIPS to Prevent Them

Abstract. A growing number of ontologies are already available thanks to development initiatives in many different fields. In such ontology developments, developers must tackle a wide range of difficulties and handicaps, which can result in the appearance of anomalies in the resulting ontologies. Therefore, ontology evaluation plays a key role in ontology development. OOPS! is an on-line tool that automatically detects pitfalls, considered as potential errors or problems-and thus may help ontology developers to improve their ontologies. To gain insight in the existence of pitfalls and to assess whether there are differences among ontologies developed by novices, a random set of already scanned ontologies, and existing well-known ones, data of 406 OWL ontologies were analysed on OOPS!'s 21 pitfalls, of which 24 ontologies were also examined manually on the detected pitfalls. The various analyses performed show only minor differences between the three sets of ontologies, therewith providing a general landscape of pitfalls in ontologies. We also propose guidelines to avoid the inclusion of such common pitfalls in new ontologies, the Typical pItfalls Prevention Scheme (TIPS), so as to increase the baseline quality of OWL ontologies

Verifying a medical protocol with temporal graphs: The case of a nosocomial disease

Objective: Our contribution focuses on the implementation of a formal verification approach for medical protocols with graphical temporal reasoning paths to facilitate the understanding of verification steps. Materials and methods: Formal medical guideline specifications and background knowledge are represented through conceptual graphs, and reasoning is based on graph homomorphism. These materials explain the underlying principles or rationale that guide the functioning of verifications. Results: An illustration of this proposal is made using a medical protocol defining guidelines for the monitoring and prevention of nosocomial infections. Such infections, which are acquired in the hospital, increasemorbidity andmortality and add noticeably to economic burden. An evaluation of the use of the graphical verification found that this method aids in the improvement of both clinical knowledge and the quality of actions made. Discussion: As conceptual graphs, representations based on diagrams can be translated into computational tree logic. However, diagrams are much more natural and explicitly human, emphasizing a theoretical and practical consistency. Conclusion: The proposed approach allows for the visualmodeling of temporal reasoning and a formalization of knowledge that can assist in the diagnosis and treatment of nosocomial infections and some clinical problems. This is the first time that one emphasizes the temporal situation modeling in conceptual graphs. It will also deliver a formal verification method for clinical guideline analyses

Semantic technologies for supporting KDD processes

209 p.Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to solve these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process. This research work aims at supporting data analysts through the different KDD phases towards the achievement of energy efficiency and thermal comfort in tertiary buildings. To do so, the EEPSA (Energy Efficiency Prediction Semantic Assistant) is proposed, which aids data analysts discovering the most relevant variables for the matter at hand, and informs them about relationships among relevant data. This assistant leverages Semantic Technologies such as ontologies, ontology-driven rules and ontology-driven data access. More specifically, the EEPSA ontology is the cornerstone of the assistant. This ontology is developed on top of three ODPs (Ontology Design Patterns) and it is designed so that its customization to address similar problems in different types of buildings can be approached methodically

Forest Observatory: a resource of integrated wildlife data

We propose the Forest Observatory, a linked datastore, to represent knowledge from wildlife data. It is a resource that semantically integrates data silos and presents them in a unified manner. This research focuses on the forest of the Lower Kinabatangan Wildlife Sanctuary (LKWS) in Sabah, Malaysian Borneo. In this region, wildlife research activities generate a variety of Internet of Things (IoT) data. However, due to the heterogeneity and isolation of such data (i.e., data created in different formats and stored in separate locations), extracting meaningful information is deemed time-consuming and labour-intense. One possible solution would be to integrate these data using semantic web technologies. As a result, data entities are transformed into a machine-readable format and can be accessed on a single display. This study created a semantic data model to integrate heterogeneous wildlife data. Our approach developed the Forest Observatory Ontology (FOO) to lay the foundation for the Forest Observatory. FOO modelled the IoT and wildlife concepts, established their relationships, and used these features to link historical datasets. We evaluated FOO’s structure and the Forest Observatory using pitfalls scanners and task-based methods. For the latter, a use case was assigned to the Forest Observatory, querying it before and after reasoning. The results demonstrated that our Forest Observatory provides precise and prompt responses to complex questions about wildlife. We hope our research will aid bioscientists and wildlife researchers in maximising the value of their digital data. The Forest Observatory can be expanded to include new data sources, replicated in various wildlife sanctuaries, and adapted to other domains