Abstract. Accessing structured data such as that encoded in ontologies and knowledge bases can be done using either syntactically complex formal query languages like SPARQL or complicated form interfaces that require expensive customisation to each particular application domain. This paper presents the QuestIO system – a natural language interface for accessing structured information, that is domain independent and easy to use without training. It aims to bring the simplicity of Google’s search interface to conceptual retrieval by automatically converting short conceptual queries into formal ones, which can then be executed against any semantic repository. QuestIO was developed specifically to be robust with regard to language ambiguities, incomplete or syntactically ill-formed queries, by harnessing the structure of ontologies, fuzzy string matching, and ontologymotivated similarity metrics.

Abstract. At OWLED2007 a task force was formed to work towards a

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. 1

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. 1

Ontology generation and population is a crucial part of knowledge base construction and maintenance that enables us to relate text to ontologies, providing a rich and customised ontology related to the data and domain with which we are concerned. SPRAT combines aspects from traditional named entity recognition, ontology-based information extraction and relation extraction, in order to identify patterns for the extraction of a variety of entity types and relations between them, and to re-engineer them into concepts and instances in an ontology. When augmented with richer knowledge such as WordNet semantic categories and terminological information, the results are greatly improved. SPRAT can either modify an existing ontology or create a new ontology from scratch, which is specific to the corpus of texts processed. Preliminary results are very promising, although more refinement of the patterns is still necessary.

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 editing-CLOnE, 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.

One of the most prominent benefits gained from the emergence of Semantic Web technology is the possibility to access data more efficiently, through the use of ontologies [18]. Querying such data requires using formal languages such as SeRQL [7] or SPARQL [39]. However, the syntax of these formal languages tends to be too

The implementation of usable security is particularly challenging in the growing field of Grid computing, where control is decentralised, systems are heterogeneous, and authorization applies across administrative domains. PERMIS, based on the Role-Based Access Control (RBAC) model, provides a unified infrastructure to address these challenges. Previous research has found that resource owners who do not understand the PERMIS RBAC model have difficulty expressing access control policies. We have addressed this issue by investigating the use of a controlled natural language parser for expressing these policies. In this paper, we describe our experiences in the design, implementation, and evaluation of this parser for the PERMIS Editor. We began by understanding Grid access control needs as expressed by resource owners, through interviews and focus

domain experts

Ontology population is a crucial part of knowledge base construction and maintenance that enables us to relate text to ontologies, providing on the one hand a customised ontology related to the data and domain with which we are concerned, and on the other hand a richer ontology which can be used for a variety