=Paper=
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|title=From Natural Language Requirements to a Conceptual Model
|pdfUrl=https://ceur-ws.org/Vol-646/EMDT2010paper3.pdf
|volume=Vol-646
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==From Natural Language Requirements to a Conceptual Model==
https://ceur-ws.org/Vol-646/EMDT2010paper3.pdf
FROM NATURAL LANGUAGE REQUIREMENTS TO A CONCEPTUAL MODEL
Christian Kop, Günther Fliedl, Heinrich C. Mayr
Alpen-Adria Universität Klagenfurt
Applied Informatics/Application Engineering
Universitätsstasse 65 – 67, 9020 Klagenfurt
(chris | guenther | heinrich)@ifit.uni-klu.ac.at
ABSTRACT The approach provides instruments for the
representation of intermediate results and the
In literature it is described in great detail how class traceability between intermediate results and the
diagrams and ER diagrams or UML class diagrams original sentences. It supports automated mapping
are derived from natural language sentences. It is from natural language requirements to interlingua
normally assumed, that there is a direct specifications and automated mapping from the
correspondence between natural language elements interlingua representation to the conceptual models.
(e.g., words) and conceptual model elements. We do
not strictly follow this assumption because of the The linguistic processing step focuses on the transfer
complexity of natural language with its ambiguities of written textual requirements to an interlingua, the
and ellipsis. Hence in this paper a stepwise generation so called Pre-design Model. The “Klagenfurt
of a conceptual model out of natural language Conceptual Pre-design Model (KCPM)” [6] provides
requirements sentences is proposed. According to the a glossary and a graphical representation and it is used
ideas of MDA we assume that automatic as a basis for the mapping to the conceptual model
transformation steps from the source model (in our (e.g., UML). We propose that the basic notions
case natural language) to the target conceptual model introduced in this interlingua should correspond to
(e.g., UML class diagram) make sense. In addition to hypothetical basic linguistic categories like nouns,
that we suggest that the designer should play an verbs, etc. Thus, the goal of the whole process which
important part during transformation. It is furthermore is called NIBA (“Natürlichsprachliche
proposed to introduce an interlingua which helps to Informationsbedarfsanalyse”) is to automate the
detect defects and provides traceability between process of producing pre-design models by extracting
sentences and the model elements. their entries from the end-user’s natural language
requirements statements.
Index Terms – natural language processing,
interlingua, conceptual modeling, defect detection To enhance the mapping process a specific framework
for annotating natural language descriptions on
1. INTRODUCTION different layers was developed.
In most cases the requirements are presented on two The paper is structured as follows. In the next section
levels: the level of end user needs and the level of the related work is described. The linguistic
developers or requirements engineers models. End processing step is introduced in Section 3. Section 4
user requirements usually are expressed via natural explains the interpretation step. Section 5 focuses on
language; requirements handled by engineers are the interlingua and their possibilities. Section 6 gives
usually expressed through formal, conceptual models. an overview of the mapping to the conceptual model.
In many cases this diverging way of representing The paper is summarized in Section 7.
knowledge is the main reason for misunderstandings
between users and engineers concerning initial 2. RELATED WORK
requirements. The discrepancy disables the possibility
of validating requirements, which is an important step The interpretation of natural language has a long
in the process of requirements engineering. tradition. In earlier approaches heuristics were
proposed. Some of these approaches were described
To handle such problems we proposed an intermediate in [3] [1] [8] [7]. Chen presented 11 rules to generate
level for requirements representation, an interlingua conceptual model elements (entity types and
connecting the natural language level of the end user relationship types) from structured sentence. Excerpts
and conceptual model level produced by engineers. of these rules can be found in the next listing [3].
(Rule 1) A common noun in English Extraction of derivational morphological
corresponds to an entity type. information.
(Rule 2) A transitive verb in English The identification of multi-words units and
corresponds to a relationship type in an ER idiomatic expression identification. This is made
diagram. possible by dynamically extending linguistic
(Rule 3) An adjective in English corresponds to knowledge inside the lexicon component.
an attribute of an entity in an ER diagram. Verb subclass identification. The filtered verb
(Rule 4) An adverb in English corresponds to an classes are based on the NTMS-system
(“Natürlichkeitstheoretische Morphosyntax”) [4]
attribute of a relationship in an ER diagram.
included in the NIBA framework.
(Rule 5) If the sentence has the form: „There
are … X in Y“ then we can convert it into the 4. INTERPRETATION
equivalent form „Y has … X “.
(Rule 7) If the sentence has the form „The X of 4.1 General guidelines for interpretation
Y is Z“ and if Z is not a proper noun, we may Following the different approaches mentioned in the
treat X as an attribute of Y. related work section, the following can be learned for
the interpretation of natural language sentences:
Abbot [1] used heuristics for the generation of Common (individual) nouns are candidates for
program specifications. Parsing techniques were classes and attributes.
introduced in [2] and [11]. NL-OOPS [14] uses the
LOLITA [15] natural language processing toolkit with An adjective and a noun together are candidates
an internal knowledge base to generate first cut for specialized classes.
conceptual models. Meanwhile tagging and chunking Proper nouns are candidates for instance labels.
is the state of the art for the linguistic step. In [13] an
approach is described which uses part of speech
A transitive verb is a candidate for a relationship
type.
tagging and morphological analysis for the generation
of conceptual model element candidates. Additionally The nouns related to the verbs are the involved
an ontology (world model) was used to refine the classes of the relationship type.
candidates for the project specific conceptual model Also prepositions can be candidates for
(discourse model). relationship types.
3. LINGUISTIC PROCESSING In other words, given a source language (e.g., natural
language) and a “meta model” (i.e., the grammar
The system solves the task of Natural Language description of the sentence) as well as a target
Processing of English requirements texts by producing language (e.g., a conceptual model and its meta
chunked and semantically annotated text, which is model), certain instances of the source language can
made ready for the KCPM modeling notions be mapped to instances of the target language. This is
extraction in the interpretation stage of the project. In achieved by defining equivalences between syntactic
a first stage it accepts the tagged sentences which are structures of the source model and syntactic structures
produced by QTag [16]. This output is refined and of the target model.
certain structures are chunked together. Figure 1 in the
appendix shows such a chunk tree representing the These general rules must be adopted for the certain
syntactic structure including phrasal, feature inheriting situation (i.e., the annotated natural language). In our
nodes. case the NTMS was used for annotating the natural
language sentences with syntactic grammar
This chunking output was processed by a modular information. Since the NTMS defines N0 as a noun
system of linguistic subsystems including the and N3 as a noun phrase, a class can be derived from
following functions: a noun (N0) or noun phrase (N3) respectively. If we
The identification of compound nouns. We find a verb (V0) together with two noun phrases then
suppose that unclear compound boundaries are a relationship can be derived from such a pattern.
very often motivated through ambiguity of Figure 1 in the appendix shows such an example.
complex terms, e.g., the implicit structure of
compounds or other groups of words. Although these and other heuristics are commonly
used they cannot really support the interpretation. The
The extraction and generation of inflectional next section will explain some difficulties of
word forms. interpretation.
4.2 Problems of Interpretation and their roles within a relationship. No distinction is
The problems of interpretation arise since the same made between objects and their properties. Every
syntactic structure of a phrase can be interpreted concept is treated equally in a first step.
differently. A typical example of this problem is that Representatives of this kind of paradigm are NIAM
the combination of an adjective and a noun can be [7] and its successor ORM [5]. Both approaches have
seen as a specialization of that noun. It is also possible pros and cons. Object oriented approaches look very
that the adjective together with the noun is the needed compact. In a typical object oriented class diagram
concept. Another problem: It is not always possible to attributes are embedded in the class representation.
distinguish between a class and an attribute just by No additional connections between classes and
analyzing one single sentence. In literature [11] the attributes are necessary which would expand the
subject-predicate-object structure with the predicate diagram. On the other hand, many revisions must be
“has” (e.g., X has Y) is interpreted as follows. The made if such a diagram is used too early in the design
subject X is a class and the object Y is an attribute. phase. Due to information that is collected, classes
However in [9] it was shown that the verb “has” is might become attributes and attributes might become
very ambiguous. classes. According to [5] this is a reason why fact
oriented approaches are better suited to be used as an
Since mainly syntactic structures are analyzed and interlingua.
mapped to elements of the conceptual model there is
no guarantee that all the extracted elements are Since the interlingua is placed before the conceptual
relevant for the target model. There is no guarantee model during an early phase of design the fact
that the model assembled only with the extracted oriented paradigm was preferred. Nevertheless there
elements will be complete or consistent. Even worse if must also be the necessity to provide an easy
an arbitrary text is taken for analyzing and transformation from the interlingua to a conceptual
interpretation there is no guarantee that the intention model like UML since it is actually the standard for
of the customer fits with the intentions of the designer. conceptual modeling. Hence the interlingua for
conceptual modeling of structural aspects of an
3.4 Solution information system consists of the following basic
As one possible solution it is necessary to give the notions:
designer the freedom to select those extracted model
elements which seem to be necessary for the target
Thing type: Any notion which is important in a
certain universe of discourse is treated as a thing
model. Furthermore it is necessary to introduce an
type. Since attributes are not defined also notions
interlingua. This interlingua presents the designer the
like person name, course id etc. are seen as thing
result of the extraction process and the designer can
types.
maintain and refine the results. Hence the model
presented in the interlingua does not represent the Connection type: Connection types relate thing
final result or final conceptual model. It represents a types to each other. Special connection types like
intermediate result that must be discussed, refined and generalization or aggregation can be defined.
improved. A tool was implemented with which the
designer can select necessary model elements and The aim of the interlingua is also to be a support for
manage the elements in the model of the interlingua. all kinds of stakeholders (designers and end users).
This also includes a tool feature for the mapping from Therefore a graphical and glossary based
the interlingua to the conceptual model. representation was used for the collection of
requirements (see Figure 3 in the appendix for the
5. INTERLINGUA graphical representation – the glossary representation
is hidden).
5.1 Overview
According to the underlying paradigm of how a 5.2 Defect detection support
stakeholder perceives the “world”, two types of Beside the purpose to provide a communication
conceptual modeling approaches can be distinguished: platform between stakeholders, the interlingua can
also support the detection of structural inconsistencies
Entity type and object oriented approaches. and incompleteness. The simplest one can be detected
Fact oriented approaches. if the designer takes a look at the cardinality
In the first paradigm the “world” is seen as a world of definitions of the connection types. As it can be easily
objects which have properties. Therefore a clear seen, all of these cardinality descriptions have a
distinction is made between object and object types “?..?”. This means that cardinalities could not be
respectively and their properties. Representatives of extracted from the textual description.
this paradigm are the classical ER approach and Another possibility is to count the number of
UML. Fact oriented approaches on the other hand see connection types of a thing type. This is described in
the “world” as a world of facts. Facts describe objects detail in [12]. With this strategy, centered thing types
can be detected (see Figure 4 in the appendix). The This mapping approach also applies meta-rules to
more connection types a thing type has, the more resolve conflicting situations between the rules. An
centered or important it is. Such centered thing types example of a meta rule is: “Laws overrule proposals”.
appear with a bigger rectangular and in another color
(e.g., green) than other thing types which seem to be 7. CONCLUSION AND FUTURE WORK
less important. However, this must not necessarily
reflect the end users intention. Therefore this strategy In this paper an overview of a mapping process
is used to confront the end user with the result and to from natural language descriptions to a conceptual
discuss the result with him. For instance if the end model was given. It was also described that such a
user wonders why certain thing types like course and process is not straight forward. Instead the designer
professor are not so important (they appear in white must handle problems. As one possible solution the
color and the rectangular is not so big as the interlingua (KCPM) was introduced. This model gives
rectangular for assistant or employee) then this can be the designer an overview of the output of natural
the hint for a defect in the original specification. language processing and provides him with some help
to improve it. Without generating the UML target
If a mapping preview is made, then orphan classes model, he is able to revise it. Different presentation
[10] can be detected. The Figure 5 shows such a case techniques (e.g., graphical view and glossary view)
for the university example. In this case thing types like make it possible to communicate with the end user.
university, faculty, department, assistant, employee, In future, it is planned to find more possibilities to
professor, budget, ut8 and ut3 were detected to be detect defects. These defect detection strategies
class candidates. All the thing types which appear in should then be applied on the notions which were
white color are currently candidates for attributes. extracted from English or from German requirements
Once again this is not the final result but a starting sentences.
point for communication, discussion and refinement.
As can be seen in Figure 5, professor, budget, faculty 8. REFERENCES
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APPENDIX
Fig. 1. Tagged sentence with chunk tree
Customer
has
customer no Customer … Name
name
address …
Product is bought by
buys product id Product
name buys
price
Class diagram ORM diagram … …
Fig. 2. Class diagram versus ORM diagram
Fig. 3. Graphical representation of the interlingua (university example)
Fig. 4. Visualization of centered thing types
Fig. 5. Mapping preview