RoundTrip Ontology Authoring

Abstract

Abstract. Controlled Language (CL) for Ontology Editing tools offer an attractive alternative to naive users, wishing to create Ontologies. However despite the benefits of CLs, users are still required to learn the correct syntactic structures in order to use the Controlled Language properly. Referring to CL vocubulary/syntax rules can be time consuming, annoying and in certain cases may prevent uptake of the tool. This paper extends work based on a controlled language for ontology editingCLOnE, which uses standard NLP tools, for processing the language and manipulating an ontology. The reversal of this process involves text generation of the CLOnE language from an existing ontology using Natural Language Generation (NLG), specifically template based/shallow NLG. The text generator and the CLOnE authoring process both combine to form a RoundTrip Ontology Authoring environment: one can start with an existing imported ontology or one originally produced using CLOnE, (re)produce the Controlled Language using the NL generator, modify or edit the text as required and subsequently parse the text back into the ontology using the CLOnE environment. Building on previous methodology we undertook an evaluation, comparing the Round Trip Ontology Authoring process with a well-known ontology editor; Where previous work required a CL reference manual with several examples in order to use the controlled language, the substitution of NLG reduces this learning curve for users, while simultaneously improving upon existing results for basic Ontology editing tasks.

Controlled Natural Language for Semantic Annotation

“…Furthermore, our annotator has already been implemented and wrapped as a plugin for a semantic note taking tool. Finally, we intend to complete the integration with Nepomuk KDE and evaluate the user-friendliness of our annotator based on the previously successful empirical methods employed in CLOnE [1].…”

“…A natural overlap exists between tools used for both ontology creation and semantic annotation, for instance the CLIE technology permits ontology creation and population by mapping both concept definitions and instances of concepts to a ontological representation using CLOnE -Controlled Language for Ontology Editing [1]. However, there is a subtle difference between the process of ontology creation and population and that of semantic annotation.…”

SPARTIQULATION: Verbalizing SPARQL Queries

“…This training set is derived from a corpus of SPARQL queries consisting of datasets from the QALD-1 challenge 4 and the ILD2012 challenge. 5 The full dataset contains 263…”

LexInfo: A declarative model for the lexicon-ontology interface

“…19 We used a dataset that was a subset of the infobox data extracted from Wikipedia using the Strict Infobox Ontology for DBPedia 3.4 [1], reduced to the information relevant for companies. We have chosen this type of data because it is structured and measure units are normalized, which allows for more sophisticated querying.…”

“…• When verbalizing an ontology, i.e., generating natural language text for easier human consumption (as in [19], [9]), richer models capturing how concepts and relations can be realized linguistically will be needed.…”