DETERMINISTIC ONTOLOGY AUTHORING USING CONTROLLED LANGUAGE IE in .NET framework

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3.6. DETERMINISTIC ONTOLOGY AUTHORING USING CONTROLLED LANGUAGE IE
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Formalising knowledge in ontologies is a high initial barrier to entry for small organisations and individuals wishing to make data available to semantic knowledge technology, due to the complexity of the standards involved and the high level of experience and engineering skills required by existing ontology authoring environments. Human language, the most natural method of communication for people, has very complex structures and a large degree of ambiguity. As already discussed in earlier sections, this makes it dif cult to process automatically and machines can currently extract a limited amount of the information therein. On the other side of the coin, formal data that is rigidly structured is easily processed by machines but hard and unnatural for people to use. The approach proposed by us bridges that gap by de ning a controlled language which, while restricted, still feels natural to people and at the same time is simple enough and unambiguous for the machines to process. A controlled language is a subset of a natural language which is generally designed to be less ambiguous than the complete language and to include only certain vocabulary terms and grammar rules which are relevant for a speci c task. The idea of controlled languages is not new, early controlled languages can trace their roots to 1970s Caterpillar Fundamental English (CFE). The aim there was to restrict the complexity of the language used (CFE only had 850 terms) so that the text is unambiguous enough that it can reliably be translated automatically into a variety of other languages. Further examples are the Caterpillar Technical English (CTE) which had 70 000 carefully chosen domain-speci c terms or the KANTOO system developed at Carnegie Mellon University. Though controlled languages can restrict the colourfulness of expression, they can be used to ef ciently communicate concrete information. In most cases using a CL is an exercise in expressing information more consistently and concisely. In order to facilitate knowledge acquisition and maintenance, we de ned a controlled language CLIE CL, modelled to allow maximum
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expressibility within the smallest set of syntactic structures. The limited number of allowed syntactic sentence structures makes the language easier to learn, much easier to use than OWL, RDF, or SQL for instance. While the syntactic structure of the sentences is constrained, the vocabulary permitted is unrestricted: apart from a small number of key phrases that are used to mark phenomena of interest, any terms can be used freely. This allows for the ontologies created to be open-domain. The types of actions that are possible are de nition of new classes, creation of hierarchies between classes, de nition of object and data-type properties, creation of instances and setting of property values for instances. The greatest advantage of this approach is that it requires essentially no training; there are no complicated user interfaces to be learnt, there are no complex formalisms to be understood. The user can simply start from a simple example which shows all the types of utterances accepted by the system and continue the ontology authoring work by re-using and modifying those examples provided. After the editing is nished, the resulting ontology can then be previewed using a simple ontology viewer implemented for this scope. Once the output has been validated, the ontology can be saved into a variety of formats including RDF(S) and OWL variants. The language analysis is carried out by an Information Extraction application based on the GATE language processing framework (Cunningham et al., 2002). It comprises some existing GATE components, that is the English tokeniser, part-of-speech tagger and morphological analyser, followed by a cascade of nite-state transducers, based on GATE s JAPE pattern matching language. The role of the transducers is to search for patterns over annotations looking for constructs conforming with the controlled language. In successfully parsed sentences speci c tokens are used to extract information. The tokeniser separates input into tokens. It identi es words within a sentence and separates them from punctuation. For example in the sentence:
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