Involving domain experts in modeling is important since knowledge needs to be captured in a model and only domain experts can establish whether the models are correct. We have experienced that a natural language based representation of a model helps them to understand the semantics of a model and has advantages over a visual representation. Therefore a controlled natural language (CNL) is designed for our existing semantic reasoning tool based on conceptual graphs (Be Informed). The resulting CNL has a formal logical basis but the goal of the CNL representation is to improve readability for human readers. We report on the challenge to develop a CNL that 1) is easy and intuitively readable for domain experts with no background in formal logics, 2) can be easily generated from the formal representation and 3) can be easily adjusted for other natural languages and cultural preferences. The solution uses patterns to represent the CNL that map to the conceptual graph. The patterns are based on SBVR's RuleSpeak and can be easily adjusted for local di erences.
services based on these knowledge models. Knowledge representation in Be Informed is based on concept graphs. To add semantics, the concepts, relations and properties are typed, using types from a metamodel associated with the graph. The tool represents the knowledge as a network diagram. A visual syntax maps icons, line styles and colors to metamodel types.
A rst version of the textual representation presented in this paper was used
to communicate a risk taxonomy to classify shipments of goods to insurance
underwriters. Although the sentences produced were very basic and consisted of
just the subject and object of a triple with a verb in between encoding for the
relation type, the underwriters immediately spotted constructs that appeared
odd to them. This resulted in an improved recall rate of modeling errors. This
early success has motivated further research at the Dutch Immigration O ce [
This paper reports on the design and implementation of a CNL that helps Be Informed customers to actively participate in modeling knowledge. 2
Design and Implementation of a CNL for Be Informed
Controlled languages are often classi ed in one of two categories [
Using CNLs to represent ontology's has been done before, for instance in
Attempto Controlled Language [
The textual syntax de nition proposed in this paper is quite similar to the
de nition used in CLOnE. Our approach towards editing a model based on a
natural language representation does not use NLP and has more in common
with Conceptual Authoring [
Pattern Based Generation Approach and SBVR's RuleSpeak The mechanism we use is based on pattern sentences that map to a concept graph. The formal model remains the single source at all times. The textual representation is just used as a view on the formal model and editing operations by the user in the view are translated into updates to the underlying formal model.
The structure of the textual representation of the formal graph is de ned by pattern sentences. A pattern sentence consists of static text fragments and subject, object and property placeholders. Fragments and placeholders are grouped into sentence parts, in order to make certain parts of the sentence optional. Cardinality in the (meta) model can be represented using multiple sentences or using enumerated lists of relations of the same type. The sentences are hand-crafted to communicate the semantics of the graph constructs they represent but are re-used for di erent projects.
The advantage of pattern sentences that map to the formal (meta) model
directly is that no NLP or parsing needs to be performed on the textual
representation. This provides freedom in choosing or updating the pattern sentences,
eventually based on audience-speci c preferences, without constraints from NLP
techniques. Methods such as RuleSpeak [
Implementation and Example
The following sentences illustrate the mapping of pattern sentences to a product
model about product purchases and applicable discounts and are taken from a
full example in [
This pattern sentence encodes for which discount applies: \The discount D is always an applicable discount if a customer orders the product P" fDiscount, requires, Productg \with option O." fDiscount, requires, Optiong.
A rule sentence based on this pattern sentence is: \The discount early adopters is always an applicable discount for a customer if the customer orders the product basic telephony with option voip."
Valid rule sentences are created in the editor by choosing relevant pattern sentences and completing the variable parts from a drop-down list. The editor uses the knowledge in the instantiated model and will only present concepts that are de ned as a discount in the discount drop-down list.
A consequence of the tight connection between the metamodel and the sentence patterns is that patterns need to explicitly deal with the plural variation of the rule sentence: \The discount triple play is always an applicable discount for a customer if the customer orders all of the following products: fast adsl digital tv basic telephony."
Adding knowledge in the algorithm on plural, gender and verb may eliminate this redundancy in the sentence patterns but introduces complexity in the mapping of the sentence to an update on the formal model. It will make the algorithm (natural) language-speci c, dependent on the availability of corpora containing language information on large sets of (often specialized) terminology and results in a more complex user interface for the end-user. These drawbacks have withheld us until now from implementing this strategy. 3
Conclusions and Research Directions Because no NLP is used in this approach, Be Informed has a lot of freedom in choosing sentence patterns, but has to explicitly deal with grammatical variations. We are interested in hybrid solutions where NLG creates the variations of patterns.
Furthermore, contextualization of the syntax in projects widens the audience
(e.g. explanation dialogs, brochures and websites), but increases implementation
e orts. To facilitate this trade-o , we need measures that evaluate how well a
CNL grammar is suited for an audience. Research in this area and reports on
user evaluations (like [