ChImp:Visualizing Ontology Changes and their Impact in Protégé

Today, ontologies are an established part of many applications and research. However, ontologies evolve over time, and ontology editors---engineers and domain experts---need to be aware of the consequences of changes while editing. Ontology editors might not be fully aware of how they are influencing consistency, quality, or the structure of the ontology, possibly causing applications to fail. To support editors and increase their sensitivity towards the consequences of their actions, we conducted a user survey to elicit preferences for representing changes, e.g., with ontology metrics such as number of classes and properties. Based on the survey, we developed ChImp---a Protégé plug-in to display information about the impact of changes in real-time. During editing of the ontology, ChImp lists the applied changes, checks and displays the consistency status, and reports measures describing the effect on the structure of the ontology. Akin to software IDEs and integrated testing approaches, we hope that displaying such metrics will help to improve ontology evolution processes in the long run

Ontology selection for reuse: Will it ever get easier?

Ontologists and knowledge engineers tend to examine different aspects of ontologies when assessing their suitability for reuse. However, most of the evaluation metrics and frameworks introduced in the literature are based on a limited set of internal characteristics of ontologies and dismiss how the community uses and evaluates them. This paper used a survey questionnaire to explore, clarify and also confirm the importance of the set of quality related metrics previously found in the literature and an interview study. According to the 157 responses collected from ontologists and knowledge engineers, the process of ontology selection for reuse depends on different social and community related metrics and metadata. We believe that the findings of this research can contribute to facilitating the process of selecting an ontology for reuse

Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classes

Classifying entities in domain ontologies under upper ontology classes is a recommended task in ontology engineering to facilitate semantic interoperability and modelling consistency. Integrating upper ontologies this way is difficult and, despite emerging automated methods, remains a largely manual task. Little is known about how well experts perform at upper ontology integration. To develop methodological and tool support, we first need to understand how well experts do this task. We designed a study to measure the performance of human experts at manually classifying classes in a general knowledge domain ontology with entities in the Basic Formal Ontology (BFO), an upper ontology used widely in the biomedical domain. We conclude that manually classifying domain entities under upper ontology classes is indeed very difficult to do correctly. Given the importance of the task and the high degree of inconsistent classifications we encountered, we further conclude that it is necessary to improve the methodological framework surrounding the manual integration of domain and upper ontologies

Representing Dataset Quality Metadata using Multi-Dimensional Views

Data quality is commonly defined as fitness for use. The problem of identifying quality of data is faced by many data consumers. Data publishers often do not have the means to identify quality problems in their data. To make the task for both stakeholders easier, we have developed the Dataset Quality Ontology (daQ). daQ is a core vocabulary for representing the results of quality benchmarking of a linked dataset. It represents quality metadata as multi-dimensional and statistical observations using the Data Cube vocabulary. Quality metadata are organised as a self-contained graph, which can, e.g., be embedded into linked open datasets. We discuss the design considerations, give examples for extending daQ by custom quality metrics, and present use cases such as analysing data versions, browsing datasets by quality, and link identification. We finally discuss how data cube visualisation tools enable data publishers and consumers to analyse better the quality of their data.Comment: Preprint of a paper submitted to the forthcoming SEMANTiCS 2014, 4-5 September 2014, Leipzig, German

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Extracting ontologies from software documentation: a semi-automatic method and its evaluation

Rich and generic ontologies about web service functionalities are a prerequisite for performing complex reasoning tasks with web service descriptions. However, their acquisition is timeconsuming and conditioned by the small number of web services available in certain domains. As a solution, we describe a semiautomatic method to extract such ontologies from software documentation, motivated by the observation that web services reflect the functionality of their underlying implementation. Further, we report on fine-tuning the extraction process by using a multi-stage evaluation method