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Heterogeneous data, different definitions and incompatible models are a huge problem in many domains, with no exception for the field of energy systems analysis. Hence, it is hard to re-use results, compare model results or couple models at all. Ontologies provide a precisely defined vocabulary to build a common and shared conceptualisation of the energy domain. Here, we present the Open Energy Ontology (OEO) developed for the domain of energy systems analysis. Using the OEO provides several benefits for the community. First, it enables consistent annotation of large amounts of data from various research projects. One example is the Open Energy Platform (OEP). Adding such annotations makes data semantically searchable, exchangeable, re-usable and interoperable. Second, computational model coupling becomes much easier. The advantages of using an ontology such as the OEO are demonstrated with three use cases: data representation, data annotation and interface homogenisation. We also describe how the ontology can be used for linked open data (LOD).

As an important tool for the semantic web, knowledge management, and knowledge sharing, ontology has grown significantly in the last three decades. The technical requirements have grown and cross-disciplinary ontologies have become popular, ontologies become increasingly complex. It is almost impossible for a single user or an organization to complete a large ontology alone. In order to edit the large ontologies efficiently, the ontology editors should be able to collaborate, share, edit, and debug remotely. However, after market research and analysis, there is not an ontology editor that meets our needs. The existing ontology editors are divided into two categories. One is the desktop version, which tends to be fully functional, such as editing, reasoning and debugging. The open source desktop ontology editor is very extensible and provides a variety of available plugins for users. Another type of ontology editor is web-based. Those editors provide a remote collaborative editing mode and allow different users to share and edit ontologies. However, the web-based editor is relatively simple and does not have reasoning or debugging features. After analyzing their advantages and disadvantages, we decided to extend the web-based collaborative ontology editor WebProtégé to implement a debugging function. Ontology debugging is a popular topic in ontology research. In this thesis we use interactive debugging, which is a simple and user-friendly method. It facilitates the reduction of debugging complexity by interactively finding errors in the ontology. Since WebProtégé is a web-based application without debugging and reasoning functionalities, the goal of our project is to implement a debugging plugin for the ontology evaluation. The extension consists of two parts, the client-side and the server-side. The client-side interacts with the user and sends the user's requests to the server-side. The server side accepts requests from the client, applies the interactive debugging method to perform consistency checking of ontologies, finds diagnosis sets, and returns the results to the client. Thus, the user can efficiently use this extension to debug remotely.In addition, the reasoning efficiency of different reasoners for an ontology varies significantly due to different algorithms. It is a challenge to select the best reasoner for an ontology. Therefore, in the last part of the thesis, we show how to use machine learning algorithms to predict the best reasoner for a given ontology. We try to use linear regression to predict the run-time and then use support vector machines to indicate the fastest reasoner for ontology consistency checking. The experiments show that the linear regression model does not fit the data well, while the support vector machine model achieves 65\% accuracy of the results for the triple classification task. We conclude that a collaborative ontology debugger can help multiple users to edit and debug shared ontologies accurately and efficiently., Boda Wen, Masterarbeit Universität Klagenfurt 2021

This paper proposes a framework of the negotiation process for solving divergences in the collaborative ontology development. Such framework is obtained through the use of philosophical principles deriving from the theories of essence, identity, unity and dependence (preconized by the OntoClean methodology) as to justify part of the argumentation used in the negotiation process among the participants, besides helping reach a consensus and reduce the conceptual gap among models. The evaluation of the experiments conducted with the use of the proposed method suggests the feasibility and implementability of our approach in practice.

Proceedings of the Third Canadian Semantic Web Symposium, We describe our ongoing work on using social media as a platform for citizen science. Building on our previous work of facilitating citizen science observations, and using RDF to integrate them with existing biodiversity knowledge, we are currently building Facebook Apps that will enable the reporting of observations, as well as the browsing and tagging of existing observations. The tagging capability serves two main purposes. First, it permits (and, we hope, encourages) multi-stage crowdsourcing for image identification. Second, it serves as a driver of ontology evolution, and permits experiments on potential working relationships between expert-engineered ontologies, and tagbased folksonomies.