Many concept-to-text generation systems require domain-specific linguistic resources to produce high quality texts, but manually constructing these resources can be tedious and costly. Focusing on NaturalOWL, a publicly available state of the art natural language generator for OWL ontologies, we propose methods to extract from the Web sentence plans and natural language names, two of the most important types of domain-specific linguistic resources used by the generator. Experiments show that texts generated using linguistic resources extracted by our methods in a semi-automatic manner, with minimal human involvement, are perceived as being almost as good as texts generated using manually authored linguistic resources, and much better than texts produced by using linguistic resources extracted from the relation and entity identifiers of the ontology.
 This paper presents a survey of research in controlled natural languages that can be used as high-level knowledge representation languages. Over the past 10 years or so, a number of machine-oriented controlled natural languages have emerged that can be used as high-level interface languages to various kinds of knowledge systems. These languages are relevant to the area of computational linguistics since they have two very interesting properties: firstly, they look informal like natural languages and are therefore easier to write and understand by humans than formal languages; secondly, they are precisely defined subsets of natural languages and can be translated automatically (and often deterministically) into a formal target language and then be used for automated reasoning. We present and compare the most mature of these novel languages, show how they can balance the disadvantages of natural languages and formal languages for knowledge representation, and discuss how domain specialists can be supported writing specifications in controlled natural language. SNOMED is a large description logic based terminology for recording in electronic health records. Often, neither the labels nor the description logic definitions are easy for users to understand. Furthermore, information is increasingly being recorded not just using individual SNOMED concepts but also using complex expressions in the description logic ("post-coordinated" concepts). Such post-coordinated expressions are likely to be even more complex than other definitions, and therefore can have no pre-assigned labels. Automatic verbalisation will be useful both for understanding and quality assurance of SNOMED definitions, and for helping users to understand post-coordinated expressions. OntoVerbal is a system that presents a compositional terminology expressed in OWL as natural language. We describe the application of OntoVerbal to SNOMED-CT, whereby SNOMED classes are presented as textual paragraphs through the use of natural language generation technology. This paper gives an overview of ongoing work on a system for the generation of NL descriptions of classes defined in OWL ontologies. We present a general structuring approach for such descriptions. Since OWL ontologies do not by default contain the information necessary for lexicalization, lexical information has to be added to the data via annotations. A rule-based mechanism for automatically deriving these annotations is presented. We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework. In our approach, we break up the end-to-end generation process into a sequence of local decisions, arranged hierarchically and each trained discriminatively. We deployed our system in three different domains---Robocup sportscasting, technical weather forecasts, and common weather forecasts, obtaining results comparable to state-of-the-art domain-specific systems both in terms of BLEU scores and human evaluation. At OWLED2007 a task force was formed to work towards a common Controlled Natural Language Syntax for OWL 1.1. In this paper members of the task force compare three controlled natural languages (CNLs) — Attempto Controlled English (ACE), Ordnance Survey Rabbit (Rabbit), and Sydney OWL Syntax (SOS) — that have been designed to express the logical content of OWL 1.1 ontologies. The common goal of these three languages is to make OWL ontologies accessible to people with no training in formal logics. We briefly introduce these three CNLs and discuss a number of requirements to an OWL-compatible CNL that have emerged from the present work. We then summarise the similarities and differences of the three CNLs and make some preliminary recommendations to an OWL-compatible CNL. 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. We describe a verbalization of the logical content of OWL ontologies — using OWL 1.1 without data-valued properties — in Attempto Controlled English (ACE). Because ACE is a subset of English, the verbalization makes OWL ontologies accessible to people with no training in formal methods. We conclude that OWL can be verbalized in concise and understandable English provided that a certain naming style is adopted for OWL individuals, classes, and properties. We describe the strategy currently pursued for verbalising OWL ontologies by sentences in Controlled Natural Language (i.e., combining generic rules for realising logical patterns with ontology-specific lexicons for realising atomic terms for individuals, classes, and properties) and argue that its success depends on assumptions about the complexity of terms and axioms in the ontology. We then show, through analysis of a corpus of ontologies, that although these assumptions could in principle be violated, they are overwhelmingly respected in practice by ontology developers. Abstract : Recently, there has been interest in marking up digital images with annotations describing the content of the images using Web-based ontologies encoded in the W3C's Web Ontology Language (OWL). The annotations are subsequently exploited to improve the user experience of large collections of images, whether by enhanced search or by a structured browsing experience. In the latter case, the complexity and unfamiliarity of logic-based ontology languages may do more to impede, than aid, the user. To alleviate this problem, the authors propose using automatic generation of natural language (NL) paraphrases of OWL statements to assist browsing image content. In this paper, they provide an overview of their NL generation approach and an empirical evaluation of the use of their paraphrases for image browsing. This paper describes a proposed new syntax that can be used to write and read OWL ontologies in Controlled Natural Language (CNL): a well-defined subset of the English language. Following the lead of Manchester OWL Syntax in making OWL more accessible for non-logicians, and building on the previous success of Schwitter’s PENG (Processable English), the proposed Sydney OWL Syntax enables two-way translation and generation of grammatically correct full English sentences to and from OWL 1.1 functional syntax. Used in conjunction with OWL tools, it is designed to facilitate ontology construction and editing by enabling authors to write an OWL ontology in a defined subset of English. It also improves readability and understanding of OWL statements or whole ontologies, by enabling them to be read as English sentences. It is hoped that by providing the option of an intuitive, easy to use English syntax which requires no specialized knowledge, the broader community will be far more likely to develop and benefit from Semantic Web applications. This paper is a discussion paper covering the scope, design, and examples of Sydney OWL Syntax in use, and the authors invite feedback on all aspects of the proposal via email to krr.sydneysyntax@cse.unsw.edu.au. Working drafts of the full specification are available at http: www.ics.mq.edu.au rolfs sos.