Ontology Evaluation

Ontology evaluation is the task of measuring the quality of an ontology. It enables us to answer the following main question: How to assess the quality of an ontology for the Web? In this thesis a theoretical framework and several methods breathing life into the framework are presented. The application to the above scenarios is explored, and the theoretical foundations are thoroughly grounded in the practical usage of the emerging Semantic Web

Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople

SPARQL enables users to access and browse knowledge graphs in a precise way. However, using SPARQL requires knowledge that many casual users lack. To counter this, specific tools have been created that enable more casual users to browse and query results. This paper evaluates and compares the most prominent techniques, QueryVOWL, SPARKLIS and the Wikidata Query Service (WQS), through a usability evaluation, using a mixed-method evaluation based on usability metrics and heuristics, containing both quantitative and qualitative data. The findings show that while WQS achieved the best results, usability problems were encountered in all tools. Key aspects for usability, extracted from the evaluation, serve as important contributions for future query builders

Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help

Developing Ontologies withing Decentralized Settings

This chapter addresses two research questions: “How should a well-engineered methodology facilitate the development of ontologies within communities of practice?” and “What methodology should be used?” If ontologies are to be developed by communities then the ontology development life cycle should be better understood within this context. This chapter presents the Melting Point (MP), a proposed new methodology for developing ontologies within decentralised settings. It describes how MP was developed by taking best practices from other methodologies, provides details on recommended steps and recommended processes, and compares MP with alternatives. The methodology presented here is the product of direct first-hand experience and observation of biological communities of practice in which some of the authors have been involved. The Melting Point is a methodology engineered for decentralised communities of practice for which the designers of technology and the users may be the same group. As such, MP provides a potential foundation for the establishment of standard practices for ontology engineering