Competency Questions (CQs) for an ontology and similar artefacts aim to provide insights into the contents of an ontology and to demarcate its scope. The absence of a controlled natural language, tooling and automation to support the authoring of CQs has hampered their effective use in ontology development and evaluation. The few question templates that exists are based on informal analyses of a small number of CQs and have limited coverage of question types and sentence constructions. We aim to fill this gap by proposing a template-based CNL to author CQs, called CLaRO. For its design, we exploited a new dataset of 234 CQs that had been processed automatically into 106 patterns, which we analysed and used to design a template-based CNL, with an additional CNL model and XML serialisation. The CNL was evaluated with a subset of questions from the original dataset and with two sets of newly sourced CQs. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90 for unseen questions. CLaRO has the potential to facilitate streamlining formalising ontology content requirements and, given that about one third of the competency questions in the test sets turned out to be invalid questions, assist in writing good questions.
This technical report describes the discourse representation structures (DRS) derived from texts written in version 5 of Attempto Controlled English (ACE 5). Among other things, ACE 5 supports modal statements, negation as failure, and sentence subordination. These features require an extended form of discourse representation structures. The discourse representation structure itself uses a reified, or ‘flat’ notation, meaning that its atomic conditions are built from a small number of predefined predicates that take constants standing for words of the ACE text as their arguments. Furthermore, each logical atom gets an index relating it to the sentence of the ACE text from which it was derived. What is here called controlled natural language CNL has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions.  Natural Language Generation (NLG) is concerned with transforming given content input into a natural language out- put, given some communicative goal. Although this input can take various forms and representations, it is the semantic conceptual representations that have always been considered as the "natural" starting ground for NLG. Therefore, it is natural that the Se- mantic Web (SW), with its machine-processable representation of information with explicitly defined semantics, has attracted the interest of NLG practitioners from early on. We attempt to provide an overview of the main paradigms of NLG from SW data, emphasizing how the Semantic Web provides opportunities for the NLG community to improve their state-of-the-art ap- proaches whilst bringing about challenges that need to be addressed before we can speak of a real symbiosis between NLG and the Semantic Web. In this paper we present a formal framework and an experimental software supporting the user in the task of formulating a precise query ‐ which best captures their information needs ‐ even in the case of complete ignorance of the vocabulary of the underlying information system holding the data. Our intelligent interface is driven by means of appropriate automated reasoning techniques over an ontology describing the domain of the data in the information system. We will define what a query is and how it is internally represented, which operations are available to the user in order to modify the query and how contextual feedback is provided about it presenting only relevant pieces of information. We will then describe the elements that constitute the query interface available to the user, providing visual access to the underlying reasoning services and operations for query manipulation. Lastly, we will define a suitable representation in “linear form”, starting from which the query can be more easily expressed in natural language. Author Keywords One of the core challenges for building the semantic web is the creation of ontologies, a process known as ontology authoring. Controlled natural languages (CNLs) propose different frameworks for interfacing and creating ontologies in semantic web systems using restricted natural language. However, in order to engage non-expert users with no background in knowledge engineering, these language interfacing must be reliable, easy to understand and accepted by users. This paper includes the state-of-the-art for CNLs in terms of ontology authoring and the semantic web. In addition, it includes a detailed analysis of user evaluations with respect to each CNL and offers analytic conclusions with respect to the field. SPARKLIS is a Semantic Web tool that helps users explore and query SPARQL endpoints by guiding them in the interactive building of questions and answers, from simple ones to complex ones. It combines the fine-grained guidance of faceted search, most of the expressivity of SPARQL, and the readability of (controlled) natural languages. No knowledge of the vocabulary and schema are required for users. Many SPARQL features are covered: multidimensional queries, union, negation, optional, filters, aggregations, ordering. Queries are verbalized in either English or French, so that no knowledge of SPARQL is ever necessary. All of this is implemented in a portable Web application, SPARKLIS, and has been evaluated on many endpoints and questions. No endpoint-specific configuration is necessary as the data schema is discovered on the fly by the tool. Online since April 2014, thousands of queries have been formed by hundreds of users over more than a hundred endpoints.