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\title{User-friendly ontology authoring using a controlled language}

\name{Valentin Tablan, Tamara Polajnar, Hamish Cunningham, Kalina Bontcheva} 

\address{Dept of Computer Science, University of Sheffield, Sheffield, UK
               \{valyt, tamara, hamish, kalina\}@dcs.shef.ac.uk}


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\maketitleabstract 

Research work in recent years in the fields of Semantic Web and Knowledge
Management has started producing tools that are useful in a variety of contexts
and scenarios. Many of these tools require a structuring of the information
space most commonly in the form of ontologies. However, 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. This makes it difficult to
process automatically and machines can currently only 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 this paper bridges that gap by defining
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 specific
task. The idea of controlled languages is not new, early controlled languages
can trace their roots to 1970's 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 translated
automatically into a variety of other languages. Further examples are the
Caterpillar Technical English (CTE) which had 70,000 carefully chosen
domain-specific terms or the KANTOO system developed at Carnegie Mellon
University. 

Though controlled languages can restrict the colourfulness of expression, they
can be used to efficiently communicate concrete information. In most cases using
a CL is an exercise in expressing information more consistently and concisely.

The controlled language proposed here is modelled to allow maximum
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 definition of new classes, creation
of hierarchies between classes, definition 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 finished, 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. It comprises some existing GATE
components, i.e. the English tokeniser, part-of-speech tagger, and
morphological analyser, followed by a cascade of finite-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 specific tokens are used
to extract information.

The Controlled Language IE application (CLIE) employs a deterministic approach,
so that each sentence can be parsed in one way only. Allowed sentences are
unambiguous, so each sentence type is translated to one type of statement. If
parsing fails it will result in a warning and no ontological output. In certain
cases where the input is invalid, the system will try a less strict analysis
mode in order to suggest how such repair may be effected.

The use of linguistic analysis allows for small variations in the surface form
used to name objects (for instance the use of plurals where it feels appropriate
from a linguistic point of view) without affecting the capability of the system
of identifying different references for the same entity. For example the
sentence {\tt ``There are animals''} will create a new ontology class with the
name {\tt `Animal'} and the sentence {\tt ``Cat is a type of animal''} will
create a new class with the name {\tt `Cat'} as a subclass of the {\tt `Animal'}
class. The {\tt `Animal'} class is referred to in two different ways: one
capitalised and in plural form and another lower case and singular. There is
also support for listing items so a sentence like {\tt ``There are projects,
work packages and deliverables''} will lead to the creation of three new
classes: {\tt`Project'}, {\tt `Work\_Package'} and {\tt `Deliverable'}. The
names of entities are normalised -- first letters are capitalised, spaces are
replaced with underscores and the head word in the case of noun phrases is shown
un-inflected. If this is undesirable, names can be included in single quotes
which will cause them to be used as they appear in the text.

CLIE can be used in one of two modes - to create a new ontology or to add
information to an existing one. Extending an existing ontology requires that
names of concepts, instance, and properties in the text are first checked
against those already in the ontology and only added if necessary. The domain
and range restrictions of properties are also checked to ensure consistency.


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