We describe a semantic wiki system with an underlying controlled natural language grammar implemented in Grammatical Framework (GF). The grammar restricts the wiki content to a well-defined subset of Attempto Controlled English (ACE), and facilitates a precise bidirectional automatic translation between ACE and language fragments of a number of other natural languages, making the wiki content accessible multilingually. Additionally, our approach allows for automatic translation into the Web Ontology Language (OWL), which enables automatic reasoning over the wiki content. The developed wiki environment thus allows users to build, query and view OWL knowledge bases via a user-friendly multilingual natural language interface. As a further feature, the underlying multilingual grammar is integrated into the wiki and can be collaboratively edited to extend the vocabulary of the wiki or even customize its sentence structures. This work demonstrates the combination of the existing technologies of Attempto Controlled English and Grammatical Framework, and is implemented as an extension of the existing semantic wiki engine AceWiki.
Ontology languages (such as RDF, OWL and SKOS) typically support language-specific labels as attachments to ontological entities (such as classes and properties). Although the ontological axioms can thus be presented multilingually, their keywords (e.g. SubClassOf , some , only ) are still in English and their syntactic structure is not customizable. This is clearly insufficient for true ontology verbalization, especially for expressive ontology languages like OWL as argued in @cite_19 , which describes a sophisticated lexical annotation ontology to be attached to the domain ontology as linguistic knowledge. Our work can also be seen as attaching (multilingual) linguistic knowledge to a semantic web ontology. @cite_29 discusses a multilingual CNL-based verbalization of business rules. It is similar to our approach by being implemented in GF but differs by not using OWL as the ontology language.
@cite_19 @cite_29
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With OWL (Web Ontology Language) established as a standard for encoding ontologies on the Semantic Web, interest has begun to focus on the task of verbalising OWL code in controlled English (or other natural language). Current approaches to this task assume that axioms in OWL can be mapped to sentences in English. We examine three potential problems with this approach (concerning logical sophistication, information structure, and size), and show that although these could in theory lead to insuperable difficulties, in practice they seldom arise, because ontology developers use OWL in ways that favour a transparent mapping. This result is evidenced by an analysis of patterns from a corpus of over 600,000 axioms in about 200 ontologies. 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. Abstract: In this paper we motivate why it is crucial to associate linguistic information with ontologies and why more expressive models, beyond the label systems implemented in RDF, OWL and SKOS, are needed to capture the relation between natural language constructs and ontological structures. We argue that in the light of tasks such as ontology-based information extraction (i.e., ontology population) from text, ontology learning from text, knowledge-based question answering and ontology verbalization, currently available models do not suffice as they only allow us to associate literals as labels to ontology elements. Using literals as labels, however, does not allow us to capture additional linguistic structure or information which is definitely needed as we argue. In this paper we thus present a model for linguistic grounding of ontologies called LexInfo. LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. LexInfo has been implemented as an OWL ontology and is freely available together with an API. Our main contribution is the model itself, but even more importantly a clear motivation why more elaborate models for associating linguistic information with ontologies are needed. We also further discuss the implementation of the LexInfo API, different tools that support the creation of LexInfo lexicons as well as some preliminary applications. 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.