Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) elements, displaying controlled natural language (CNL) explanations of OWL axioms corresponding to the selected visual notation elements. Thus, the domain experts will benefit from both the high-level overview provided by the graphical notation and the detailed textual explanations of particular elements in the diagram.
In the field of textual ontology verbalizations there has been some exploration of how to make verbalizations more convenient for users. One approach that has been tried is grouping verbalizations by entities. It produces a kind of a dictionary, where records are entities (class, property, individual), and every record contains verbalizations of axioms that refer to this entity. The resulting document is significantly larger than a plain, non-grouped verbalization because many axioms may refer to multiple entities and thus will be repeated in each entity. Nevertheless, the grouped presentation was preferred by users @cite_15 . Our approach can be considered a generalization of this approach, where a dictionary is replaced by an ontology visualization that serves as a map of the ontology.
@cite_15
1504748971 2133999934 1538808533 45390577
It has frequently been observed that domain experts are not necessarily ontology experts, and that the production of ontologies would be aided if they could read and edit axioms in natural language. The SWAT Tools Verbaliser is available, via a web interface, for verbalising OWL ontologies as texts in a controlled fragment of English. Taking as input any OWL ontology, the verbaliser creates a lexicon containing entries for all the entities in the input, and uses it to generate an English sentence corresponding to each logical axiom. These sentences are then organised into a document structure similar to that of an encyclopaedia, with an entry providing a definition, typology and examples for each entity. The output is either organised, easily-navigable English text encoded in XML, or a copy of the input OWL in which each entity is annotated with its description entry. The generated texts have been evaluated in a number of ways which are briefly presented here. The SWAT Tools ontology verbaliser generates a hierarchically organised hypertext designed for easy comprehension and navigation. The document structure, inspired by encyclopedias and glossaries, is organised at a number of levels. At the top level, a heading is generated for every concept in the ontology; at the next level, each entry is subdivided into logically-based headings like 'Definition' and 'Examples'; at the next, sentences are aggregated when they have parts in common; at the lowest level, phrases are hyperlinked to concept headings. One consequence of this organisation is that some statements are repeated because they are relevant to more than one entry; this means that the text is longer than one in which statements are simply listed. This trade-off between organisation and brevity is investigated in a user study. Verbalization is the process of writing the semantics captured in axioms into natural language sentences, which enables domain experts (who are not trained to understand technical formal languages) to be able to participate in the modeling and validation processes of their domain knowledge. We present a novel approach to support multilingual verbalization of logical theories, axiomatizations, and other specifications such as business rules. This engineering solution is demonstrated with the Object Role Modeling language and the ontology engineering tool DogmaModeler, although its underlying principles can be reused with other conceptual models and formal languages, such as Description Logics, to improve its understandability and usability by the domain expert. Our engineering solution for multilingual verbalization is characterized by its flexibility, extensibility and maintainability of the verbalization templates, which allow for easy augmentation with other languages than the 10 currently supported. This paper presents an approach to multilingual ontology verbalisation of controlled language based on the Grammatical Framework (GF) and the lemon model. It addresses specific challenges that arise when classes are used to create a consensus-based conceptual framework, in which many parties individually contribute instances. The approach is presented alongside a concrete case, in which ontologies are used to capture business processes by linguistically untrained stakeholders across business disciplines. GF is used to create multilingual grammars that enable transparent multilingual verbalisation. Capturing the instance labels in lemon lexicons reduces the need for GF engineering to the class level: The lemon lexicons with the labels of the instances are converted into GF grammars based on a mapping described in this paper. The grammars are modularised in accordance with the ontology modularisation and can deal with the different styles of label choosing that occur in practice.