=Paper=
{{Paper
|id=None
|storemode=property
|title=Design of a CNL to Involve Domain Experts in Modelling
|pdfUrl=https://ceur-ws.org/Vol-622/paper13.pdf
|volume=Vol-622
}}
==Design of a CNL to Involve Domain Experts in Modelling==
https://ceur-ws.org/Vol-622/paper13.pdf
Design of a CNL
to Involve Domain Experts in Modeling
Sivlie Spreeuwenberg1 , Jeroen van Grondelle2 , Ronald Heller2 , and
Gartjan Grijzen2
1
LibRT, Amsterdam, Netherlands
silvie@librt.com
2
Be Informed, Apeldoorn, Netherlands
{j.vangrondelle, r.heller, g.grijzen}@beinformed.nl
Abstract. 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 nat-
ural 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 exist-
ing 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 differences.
Keywords: Controlled Natural Language, Business Rules, Specifica-
tions, Knowledge Representation, CNL design and evaluation, SBVR,
RuleSpeak
1 Need for Controlled Natural Languages in Modeling
The adoption of model driven technologies such as Enterprise Decision Man-
agement and Business Process Management is growing. As a result, involving
business users in modeling is more important than ever. Their ability to capture
business knowledge in models correctly is a key factor in the adoption of these
technologies. The main challenge in involving business users in knowledge mod-
eling is the fact that most business users are not trained in formal knowledge
representation techniques. A formal, concise, visual representation can be quite
intimidating to the uninitiated. Consequently they will not be able to verify the
accuracy of the model directly.
Be Informed develops a software suite that is used by complex, knowledge in-
tensive organizations to capture their business knowledge and run model driven
services based on these knowledge models. Knowledge representation in Be In-
formed 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 first 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 Office [1].
A next version [2] of the text generator was used to validate candidate policy
decisions for consistency before they are accepted. In workshops with business
representatives and legal advisors, the policy is defined in the tool that also will
be used to execute this policy. Both a visual graph oriented representation and
the textual representation discussed in this paper were used. It is important to
note that the parties involved here were unfamiliar with formal representation
techniques and would normally express any policy in unrestricted, natural lan-
guage. The expectation that the textual representation was preferred over the
diagrams was confirmed by the participants. An interesting new observation was
that the sentence should be a grammatically correct sentence.
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 classified in one of two categories [3]: those that
improve readability for human readers and those that enable reliable automatic
semantic analysis of the language. The language that we designed has a formal
logical basis. But all too often languages in the second category do not read very
naturally. The challenge for Be Informed was to design a language that can be
easily generated from a conceptual graph and is natural and understandable for
people to read.
Using CNLs to represent ontology’s has been done before, for instance in
Attempto Controlled Language [4] and CLOnE [5]. They both use natural lan-
guage generation (NLG) to create a textual representation and natural language
processing (NLP) to roundtrip the ontology based on the changed text.
The textual syntax definition proposed in this paper is quite similar to the
definition 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 [6]. Editing is not performed by manipulating text
but by performing editing operations at the concept level, with the text being
updated to reflect concept-level changes.
2.1 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.
Fig. 1. Pattern based generation approach
The structure of the textual representation of the formal graph is defined by
pattern sentences. A pattern sentence consists of static text fragments and sub-
ject, object and property placeholders. Fragments and placeholders are grouped
into sentence parts, in order to make certain parts of the sentence optional. Car-
dinality 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 different 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 repre-
sentation. This provides freedom in choosing or updating the pattern sentences,
eventually based on audience-specific preferences, without constraints from NLP
techniques. Methods such as RuleSpeak [7] and the OMG standard SBVR [8]
have rationalized the use of natural language for the business by introducing
syntactic guidelines and best practices. Our latest sentence patterns include
RuleSpeak keywords (must, always) and follow the guideline that a rule should
be expressed by a grammatically complete, correct and readable sentence. These
patterns make sentences easier to read and place a natural and intuitive empha-
sis on the fact that the sentence introduces an obligation (and is not ‘just’ a
potential statement) for business experts with no background in formal logics.
The sentence patterns also provide guidance to direct people into being more
formal.
2.2 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 [2]. The sentence parts are placed between quotation marks, the
mapping to the metamodel is placed between braces.
This pattern sentence encodes for which discount applies: “The discount D
is always an applicable discount if a customer orders the product P” {Discount,
requires, Product} “with option O.” {Discount, requires, Option}.
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 defined as a discount in the discount drop-down list.
Fig. 2. Task centric and word processor style editing
A consequence of the tight connection between the metamodel and the sen-
tence 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 elimi-
nate 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-specific, 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 varia-
tions. 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
efforts. To facilitate this trade-off, 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 [9]) are welcome.
References
1. Heller, R., Teeseling, F. van, Gülpers, M.: A Knowledge Infrastructure for the Dutch
Immigration Office. 7th Extended Semantic Web Conference, Heraklion. LNCS, vol.
6089, pp. 386-390 (2010)
2. Grondelle, J.C. van, Heller, R., Haandel, E. van, Verburg, T.: Involving Business
Users in Formal Modelling using Natural Language Pattern Sentences. Proceedings
of EKAW, Lisbon, LNCS (2010)
3. Clark, P., Harrison, P., Murray, W.R., Thompson, J., [ed] Fuchs, N.E.: Natural-
ness vs. Predictability: A Key Debate in Controlled Languages.Pre-Proceedings of
the Workshop on Controlled Natural Language, Marettimo Island. CEUR, vol. 448
(2009)
4. Kaljurand, K., Fuchs, N.E.: Verbalizing OWL in Attempto Controlled English. In
Proceedings of Third International Workshop on OWL: Experiences and Directions,
Innsbruck, Austria (2007)
5. Funk, A., Tablan, V., Bontcheva, K., Cunningham, H., Davis, B., Handschuh, S.:
CLOnE: Controlled Language for Ontology Editing. Proceedings of the Sixth In-
ternational Semantic Web Conference (ISWC), Busan, Korea, LNCS, vol. 4825, pp.
142-155 (2007)
6. Power, R., Scott, D., Evans, R.: What you see is what you meant: direct knowl-
edge editing with natural language feedback. In Proceedings of the 13th Biennial
European Conference on Artificial Intelligence, pp. 675-681, Brighton, UK (1998)
7. Ross, R.G.: RuleSpeak [Online] http://www.rulespeak.com (2009)
8. Object Management Group: Semantics of Business Vocabulary and Rules [Online]
http://www.omg.org/spec/SBVR/1.0 (2008)
9. Engelbrecht, P., Hart, G., Dolbear, C. [ed.] Fuchs, N.E.: Talking Rabbit: a User
Evaluation of Sentence Production. Pre-Proceedings of the Workshop on Controlled
Natural Language, Marettimo Island. CEUR, vol. 448 (2009)