DoMoRe – A Recommender System for Domain Modeling

Henning Agt-Rickauer

, Ralf-Detlef Kutsche

and Harald Sack

Hasso Plattner Institute for IT Systems Engineering, University of Potsdam, Potsdam, Germany

Database Systems and Information Management Group, Technische Universit¨at Berlin, Berlin, Germany

FIZ Karlsruhe & Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Keywords:

Domain Modeling, Recommender System, Information Extraction, Knowledge-based Modeling.

Abstract:

Domain modeling is an important activity in early phases of software projects to achieve a shared under-

standing of the problem ﬁeld among project participants. Domain models describe concepts and relations of

respective application ﬁelds using a modeling language and domain-speciﬁc terms. Detailed knowledge of the

domain as well as expertise in model-driven development is required for software engineers to create these

models. This paper describes DoMoRe, a system for automated modeling recommendations to support the

domain modeling process. We describe an approach in which modeling beneﬁts from formalized knowledge

sources and information extraction from text. The system incorporates a large network of semantically re-

lated terms built from natural language data sets integrated with mediator-based knowledge base querying in

a single recommender system to provide context-sensitive suggestions of model elements.

1 INTRODUCTION

Motivation. Model-driven engineering (MDE) pro-

poses systematic use of models as primary devel-

opment artifacts for software system construction

(Whittle et al., 2014). These models describe different

aspects of a system on a higher level of abstraction us-

ing particular modeling languages(e.g., UML, entity-

relationship diagrams, or domain-speciﬁc languages).

MDE aims at continuously reﬁning models and gen-

erating source code. Consequently, the effort of man-

ually creating code with programming languages is

reduced and recurring tasks are automated.

An important activity in early phases of model-

driven software development is domain model-

ing (Evans, 2004; Fowler, 2010). Its goal is to create

models that reﬂect the conceptual structures of a busi-

ness domain. These models contain domain-speciﬁc

terms and their relations in order to improve the un-

derstanding of the problem ﬁeld among stakeholders.

Domain modeling requires expertise in model-

drivensoftware development, such as ﬁnding the right

abstractions, creating metamodels and correct usage

of generalizations/specializations and aggregations.

Assuming that software engineers have these compe-

tencies, these techniques are usually applied to differ-

ent application areas and industrial sectors. The engi-

neers are required to have detailed knowledge of the

domain in order to build domain-speciﬁc models and

derive appropriate reﬁned implementation models.

Assembling domain knowledge is a time-consuming

manual process (e.g., talking to domain experts and

reading speciﬁc documentation). Recent modeling

environments (e.g., Eclipse Modeling Project, Mag-

icDraw) provide sophisticated support with respect to

the correct usage of modeling languages and veriﬁca-

tion of models, but assistance with regard to the actual

content and meaning of the model elements is very

limited (Segura et al., 2016; Kuhn, 2010).

Problem Statement. Domain modeling was and

still is a challenging task (Mylopoulos et al., 1990;

St¨orrle, 2010). It involves collecting a lot of pieces of

information delivered by different types of persons,

documents and other knowledge sources. Domain

modeling is a knowledge intensive process and re-

quires intensive collaboration between engineers and

domain experts. The automation of domain modeling

has been addressed by research (Reinhartz-Berger,

2010), but support of this activity is still an open is-

sue (Frank, 2014). The main challenges of domain

modeling and knowledge acquisition are as follows.

Solutions that build on reusable libraries with do-

main information, such as Domain Engineering, suf-

fer from a cold start problem. Reusable domain

knowledge will become only available if enough solu-

Agt-Rickauer, H., Kutsche, R-D. and Sack, H.

DoMoRe – A Recommender System for Domain Modeling.

DOI: 10.5220/0006555700710082

In Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2018), pages 71-82

ISBN: 978-989-758-283-7

tions have already been developed using that method-

ology while new projects already want to beneﬁt from

this domain knowledge. In the end, domain models

frequently have to be developed from scratch (Frank,

2013).

Collaborations of technical stakeholders (model-

ing experts) and non-technical stakeholders (domain

experts) require a time-consuming learning phase dur-

ing a project (Ionita et al., 2015). Domain experts are

often not familiar with modeling notations, and mod-

eling experts usually have to develop a deeper under-

standing of the domain concepts and terms in order to

arrange them properly in domain models. The biggest

effort is at the modeler’s desk, because it is usually

more time-consuming to ﬁnd, understand, and pro-

cess all available domain information than to learn a

few modeling concepts of a visual notation.

Domain information is contained in arbitrary

sources. Structured information sources (e.g.,

databases, XML documents, knowledge bases, mod-

els) may be available, but uniform access to all

sources is not available in most cases and may only

be facilitated by building additional search engines on

top of them. Unfortunately, the amount of structured

information sources is vanishingly small compared to

unstructured information. It is estimated that 80 per-

cent of existing data is unstructured

. Domain infor-

mation is often contained in natural language docu-

ments (e.g., text books, manuals, requirements speci-

ﬁcations). Relevant facts have to be manually located

ﬁrst and then interpreted.

Finally, the availability of large conceptual knowl-

edge bases containing domain information is very

limited. Only a few handcrafted semantic databases

exist (e.g., WordNet, ConceptNet, Wikidata) that by

far do not cover the variety of possible domains.

Most approaches of information extraction (Banko

et al., 2007) and automatically created knowledge

bases (e.g., DBpedia, YAGO) focus on factual knowl-

edge on instance level that cannot be used for do-

main modeling on conceptual level. Additionally, the

core of many works (e.g., YAGO, BabelNet, DBpe-

dia) relies on a single source of information only: ex-

traction from Wikipedia’s structured parts (e.g., in-

foboxes, categories).

Contributions and Outline. In this paper, we

present a domain modeling recommender (DoMoRe)

system that incorporates a ready-to-use large-scale

knowledge base of domain-speciﬁc terms and their

https://www.ibm.com/blogs/watson/2016/05/biggest-

data-challenges-might-not-even-know/ (Last accessed July,

2017)

relationships. DoMoRe also uses a set of exist-

ing knowledge bases to retrieve domain information

and is extensible with additional knowledge bases at

low effort. Connected knowledge sources are used

automatically during modeling to provide context-

sensitive suggestions of model elements. The recom-

mender system is integrated into a widely used mod-

eling tool, the Ecore Diagram Editor of the Eclipse

Modeling Project.

The remainder of the paper is organized as fol-

lows. Section 2 presents the general approach and de-

tails the model reﬁnement steps that our system sup-

ports. In Section 3 we describe how existing knowl-

edge sources are used and how the knowledge base of

related terms was created. Section 4 details the imple-

mentation of the recommender system and shows how

provision of contextual information and search-based

suggestions operate. In Section 5 we report on expe-

riences using DoMoRe in different domains and sce-

narios. Related work is given in Section 6, and Sec-

tion 7 concludes the paper and describes future work

directions.

2 SEMANTIC MODELING

SUPPORT

In this section we introduce the concept of semantic

modeling support and detail our approach. Modeling:

The activity of creating and reﬁning models. In our

case these models are domain models that focus on

concepts and relationships of various application ar-

eas. Support: Modeling activities are assisted with

context-sensitive pieces of information. Tool support

is completely automated in contrast to guidelines or

methodologies. Semantic: Modeling support focuses

on the domain-speciﬁc terms and their relationships

in domain models in contrast to syntactic modeling

language assistance.

General Support Procedure. The semantic mod-

eling support works as follows: (1) At some point

of time during domain modeling a manual change

in the model is made. This is usually referred to

as model reﬁnement, the activity in which a devel-

oper creates, modiﬁes or deletes a model element

(e.g., a new class). We concentrate on supporting

the modiﬁcations that add new content to the model.

All detailed scenarios are described in the next para-

graph. (2) Based on the current state of the model,

domain knowledge is acquired automatically. Knowl-

edge acquisition is based on the terms that are used

to name the elements (e.g., class names or associ-

ation names). We pursue two strategies: First, we

MODELSWARD 2018 - 6th International Conference on Model-Driven Engineering and Software Development

exploit existing structured knowledge sources to ac-

quire the required domain terms and their relations.

We employ mediator-based querying for a uniform

access to this knowledge. Secondly, it is a well known

problem (Colace et al., 2014) that existing knowl-

edge bases (often created manually) do not contain

enough information or do not exist at all for respec-

tive target domains. We address this issue by the au-

tomated creation of own semantic terminology net-

works from natural language datasets that cover a va-

riety of domains. Both approachesare detailed in Sec-

tion 3. (3) Acquired knowledge is transformed au-

tomatically into appropriate suggestions (e.g., related

classes, possible sub- or super-classes) and presented

to the user. It is the goal to present semantically re-

lated model elements that support the developer’s de-

cisions on what to include in the model and how to

connect the elements. After that the procedure starts

all over again.

Modeling Support Scenarios. Many opportunities

exist to create and manage domain models. Domain

modeling is not necessarily bound to using one spe-

ciﬁc modeling language. For example, UML class di-

agrams, ER models, and ontologies can be used. All

approaches have in common that the respective mod-

eling language is used to express conceptual struc-

tures of a domain using speciﬁc terms to improve un-

derstanding of the problem ﬁeld. Since our seman-

tic modeling support concentrates on the terms in do-

main models, the methods presented in this paper are

applicable to several modeling languages. Neverthe-

less, we had to exemplarily choose one approach to

illustrate our work, namely UML-like class diagrams,

because they are the most widely used modeling

paradigm in industry (Reggio et al., 2014; Hutchin-

son et al., 2014).

During domain model development the user has

several options to change the model. In the following

we itemize for which modeling activities what kind

of support will be accomplished. We distinguish be-

tween two different kinds of support. First, contex-

tual information will be provided if an element of a

domain model is selected by the developer (Scenar-

ios 1 and 2). Context information includes possible

related model elements with all kinds of relationships

the modeling language offers. Second, if a new ele-

ment is created, automated suggestions will be pro-

vided on how to name the element (Scenarios 3 to 9).

The support depends on the type of connection be-

tween the new element and existing elements of the

model.

Scenario 1 – Selection of a class. The goal of pro-

viding contextual information is the recommenda-

tion of possible connected model elements together

with their types of relations for a selected domain

model element. In case a class is selected (c.f., Fig-

ure 1) possible generalizations/specializations, aggre-

gations (containers and parts), and associations are

shown. Related classes are either unconnected classes

or classes that are connected with an association that

has no name.

Figure 1: Contextual information for a selected class (Sce-

nario 1).

Scenario 2 – Selection of an association. If an asso-

ciation is selected, alternative association names, and

possible other connected classes for each association

end will be shown (c.f., Figure 2). Note that if nothing

is selected, contextual information for every element

of the model will be shown in a summarized form.

Figure 2: Contextual information for a selected association

(Scenario 2).

Scenario 3 – Creation of a class (no connection).

Modeling tools usually offer the creation of new

classes in a model without any connection. Typi-

cally, this happens, when classes are added to the dia-

gram and the respective connections are drawn after-

wards (c.f., Figure 3). In this case, class name sug-

gestions are dependent on all existing class names in

the model. Particularly, in the list of suggestions class

names should appear that are related to all of the ex-

isting classes ordered by relevance.

Figure 3: Related class name suggestions while adding a

class without a connection (Scenario 3).

Scenario 4 – Creation of a sub class. A sub class will

be created, when the developer uses the specialization

link starting from an existing class to empty space in

the diagram (c.f., Figure ??). In this case, class name

suggestions are dependent on the linked super class.

In the example, different types of doctors are shown

(different kinds of medical specialists).

DoMoRe – A Recommender System for Domain Modeling

Figure 4: Sub class name suggestions while adding a spe-

cialization (Scenario 4).

Scenario 5 – Creation of a super class. Analogous

to the sub class creation, a super class will be cre-

ated when using the generalization link. The example

shows the recommendation of more general terms for

doctor (c.f., Figure 5).

Figure 5: Super class name suggestions while adding a gen-

eralization (Scenario 5).

Scenario 6 – Creation of an aggregated class. In case

the developer uses a composition or aggregation link

starting from an existing class, an aggregated class

will be created in the diagram. The example in Fig-

ure 6 shows possible parts of a hospital.

Figure 6: Aggregated class name suggestions while adding

an aggregated class (Scenario 6).

Scenario 7 – Creation of a container class. If the op-

posite direction of a composition or aggregation rela-

tion is used, a container class will be created. In the

example used in Figure 7, suggestions are provided

what a hospital can be part of.

Figure 7: Container class name suggestions while adding a

container class (Scenario 7).

Scenario 8 – Creation of an associated class. An as-

sociated class will be created, if the developer draws

an association link from a class to empty space in

the diagram (a new class and an association without

a name will be created). Names for the new related

class will be recommended (c.f., Figure 8). This sce-

nario is very similar to Scenario 3, but the suggestions

are dependent on the linked class only.

Figure 8: Associated class name suggestions while adding

an associated class (Scenario 8).

Scenario 9 – Creation of association. If the devel-

oper creates an association link between two classes,

association names (verbs) will be provided. The sug-

gestions are dependent on both class names. In case

the association does not have a direction, verbs are

suggested that apply to both directions.

Figure 9: Association name suggestions while adding an

association (Scenario 9).

3 DOMAIN KNOWLEDGE

SOURCES

Our intended modeling support requires a large body

of background knowledge in order to provide model

element suggestions for nearly every possible do-

main. Since the support focuses on the terms used

in the models, we concentrate on knowledge sources

that provide lexical information.

We pursue two strategies: First, we exploit ex-

isting structured knowledge sources to acquire the

required domain knowledge. Knowledge bases and

ontologies are automatically queried for terms of a

model to retrieve related terms. Secondly, we target

the lack of conceptual knowledge bases by the auto-

mated creation of a semantic network of terms from

natural language datasets.

Mediator-based Knowledge Base Querying. As

described in the introduction, only a few knowledge

bases exist that contain conceptual knowledge. Word-

Net (Fellbaum, 1998) is the most widely used lexi-

cal database for the English language. Other impor-

tant resources are BabelNet (Navigli and Ponzetto,

MODELSWARD 2018 - 6th International Conference on Model-Driven Engineering and Software Development

2012), a multilingual encyclopedic dictionary, and

Cyc (Lenat, 1995) and ConceptNet (Speer and

Havasi, 2012), both common sense knowledge bases.

Most of the other large publicly available knowl-

edge bases (e.g., DBpedia, YAGO, Wikidata) consist

of a relatively small ontology schema describing the

model of the data and a large body of factual knowl-

edge. Nevertheless, these schemata can be used for

modeling suggestions.

The greatest challenge in using these knowledge

sources is the unavailability of uniform access to lex-

ical information. Heterogenous data models prevent

querying the knowledge bases in a consistent way.

Lexical information of terms and their relationships

exist on schema level, intermediate proprietary data

models and on instance level.

Our approach proposes a mediator-wrapper solu-

tion. A mediator allows the interaction of a user or

system with heterogeneous data sources in a uniform

way. Knowledge bases remain as they are, a wrapper

is responsible for content translation, and the media-

tor provides a single point of access to the information

for the modeling recommendations. Figure 10 shows

the architecture of our approach. We differentiate be-

tween three different layers.

Knowledge

Base 1

...

Knowledge

Base n

Mediator

Knowledge Base

Specific Layer

Terminology

Specific Layer

Modeling Language

Specific Layer

Recommender

Mapper

Modeling

Tool

Figure 10: Three layer mediator-wrapper architecture

to retrieve terminological information from heterogenous

knowledge bases.

In the modeling language speciﬁc layer the devel-

oper uses the modeling tool and interacts with the rec-

ommender. This layer deals with elements such as

classes and associations, and the recommender sug-

gests these types of elements depending on the con-

tent of a model.

The mediator and mapper are located in the ter-

minology speciﬁc layer. In this layer domain-speciﬁc

terms used in a model are relevant (e.g., noun terms

and their related terms). The mediator is responsi-

ble for translating from terminology speciﬁc content

to the modeling layer and vice versa. It also man-

ages a set of knowledge bases and their correspond-

ing wrappers and submits queries to them whenever

necessary. The mapper collects and integrates results

of the wrappers and provides the information to the

mediator.

In the knowledge base speciﬁc layer the wrap-

pers communicate with the knowledge bases. Each

wrapper has to deal with different query languages

and formats (e.g., OWL, RDF, SPARQL, JSON)

and different ways of modeling (e.g., graphs, con-

cepts, synsets). We support the automated integra-

tion of three types of knowledge base data models

without the need of any development: (1) Ontol-

ogy schemata: concepts and relationships modeled

using OWL or RDFS classes and object properties,

(2) SKOS-based vocabularies: terms modeled with

concepts and broader/narrower/related relationships,

and (3) Knowledge bases using the lemon model: a

speciﬁc way of modeling lexicons of ontologies (Mc-

Crae et al., 2012).

In case none of these data models apply, we sup-

port any knowledge base that offers a SPARQL end-

point. The effort of adding a new knowledge base

to the system is comparatively low, it is only neces-

sary to specify a small set of queries for taxonomic,

part/whole, related and verbal relationships.

Extraction of Semantically Related Terms. We

apply natural language processing techniques on a

large text dataset to identify terms on conceptual level

and their relationships. The approach relies on syn-

tactical properties of sentences and statistical features

of text corpora to perform a domain-independent ex-

traction. Large text collections include a lot of redun-

dancy and paraphrasing (Banko, 2009). That means

equal facts are repeated in several documents and

are formulated differently. Additionally, natural lan-

guage has the property that certain lexical items tend

to co-occur more often than others. That implies that

words with similar meanings occur in similar con-

texts, known as the distributional hypothesis (Turney

and Pantel, 2010).

We exploit these features to automatically build

a large database of semantically related terms. We

processed the Google Books N-Gram Corpus (Michel

et al., 2011), a dataset derived from 5 million books

with over 500 billion words. It covers a wide range

of domains, because it contains scientiﬁc literature of

lots of areas as well as ﬁction and non-ﬁction books.

The dataset provides the information how often a

certain word or word sequence occurs within the orig-

inal text corpus (an n-gram is a sequence of n con-

secutive words). For example, ”the doctor and the

patient – 8,339” is one example of the 700 million 5-

grams in the dataset (c.f., Figure 11a). We apply part-

DoMoRe – A Recommender System for Domain Modeling

of-speech (POS) tagging on all n-grams to identify

technical terms (Williams, 2013) and record how of-

ten concept terms in different contexts co-occur (e.g.,

doctor – patient – 418,711 times, c.f., Figure 11b).

We exclude proper nouns and named entities (e.g.,

city names, persons). Using this information, we are

able to obtain for every term a set of related terms

and their frequencies and construct a semantic net-

work (c.f., Figure 11c).

A detailed description of the construction is given

in (Agt and Kutsche, 2013). Several improvements

have been made in comparison to the original version.

The semantic network now covers verbal and ternary

relations in order to suggest associations in domain

models, several heuristics were applied to extend the

length of extracted terms, and a more sophisticated

ranking of terms was developed.

term rel. term freq.

doctor nurse 769,932

doctor patient 418,711

doctor degree 298,385

doctor hospital 202,729

… … …

doctor consult 173,786

doctor prescribe 120,267

… … …

hospital doctor 202,729

hospital patient 370,539

… … …

hospital admit 411,666

hospital leave 380,726

hospital discharge 134,348

… … …

(b) Term co-occurrences(a) N-grams frequencies (c) Semantic network

N-gram Freq.

was admitted to the hospital 40,066

admitted to the hospital for 18,594

discharged from the hospital . 12,158

a nurse at the hospital 1,989

the patient to the hospital 8,252

a patient in the hospital 5,243

the doctor and the nurse 4,827

the patient , the nurse 4,245

nurse assists the patient to 1,255

consult your doctor or pharmacist 4,156

the doctor and the patient 8,339

, call your doctor . 5,262

… …

doctor

nurse

patient

hospital

[f:769,932,

rf:0.06547,

nmpi:0.38887]

[f:418,711,

rf:0.03560,

nmpi:0.17270]

consult

[f:370,539,

rf:0.03109,

nmpi:0.19630]

school

[...]

prescribe

[...]

leave

admit

[...]

...

Figure 11: Information extraction procedure to construct a

large-scale semantic network from co-occurring terms in n-

gram natural language datasets (f – absolute frequency, rf

– relative frequency, npmi – normalized point wise mutual

information).

In essence, the semantic network is a large-scale

graph in which each term is a node and each di-

rected edge denotes a weighted relationship between

the terms. It comprises 5.7 million unique one-word

terms and multi-word expressions and 114 million re-

lationships. Each relationship is quantiﬁed with the

absolute co-occurrence frequency, a computed rel-

ative frequency, and the pointwise mutal informa-

tion (PMI) measurement (see Section 4 – Ranking –

for more details on this associativity score between

terms). While the extraction required sophisticated

hardware and runtime, the semantic network requires

just 7 GB disk space and thus is usable on standard

PC hardware.

4 RECOMMENDER SYSTEM

In this section we detail the realization of the domain

modeling recommender (DoMoRe) system. We de-

scribe how the domain information of a set of knowl-

edge bases and a self-created semantic network of

terms is used and transformed to recommendations of

model elements according to our nine modeling sup-

port scenarios.

Essentially, the task of the recommender system

is as follows. For a given model element a set of re-

lated model elements has to be determined that are

connected with a speciﬁc type of relationship (e.g.,

all possible subclasses of the class ”Doctor” or all re-

lated classes of ”Doctor” and ”Hospital”). In the fol-

lowing we ﬁrst discuss a mapping of semantic rela-

tionships across different knowledge representations.

After that, the architecture of the recommender sys-

tem is presented. Then, we detail the features of the

recommender system and describe how suggestions

are ranked by relevance.

Semantic Relationships. Domain models describe

concepts and relationships of an application domain

using the means of a modeling language. Although

there is an ongoing foundational discussion in the

conceptual modeling community (Henderson-Sellers

et al., 2015; Atkinson and K¨uhne, 2015) on how to

properly represent real-world concepts with modeling

languages, UML class diagrams are the most widely

used modeling paradigm in industry (Reggio et al.,

2014; Hutchinson et al., 2014). In this paragraph we

analyze the semantic relationships of UML class dia-

grams from a lexical viewpoint and their representa-

tions in other knowledge sources (c.f., Figure 10 for

the three layers).

We reviewed literature from database re-

search (Storey, 1993), linguistics (Maroto Garca and

Alcina, 2009; Chafﬁn and Herrmann, 1984), infor-

mation systems (Oliv´e, 2007; Guizzardi, 2005), and

semantic web research (Almeida et al., 2011; Huang,

2010) and relate the different types of relationships

to each other. Table 1 provides an overview, details

are given as follows.

Specialization and Generalization are hierarchi-

cal abstraction mechanisms in UML to reﬁne abstract

classes to more speciﬁc ones and to group speciﬁc

classes to more abstract ones. In lexical semantics

these conceptual relationships are referred to as hy-

ponymy and hypernymy between words or phrases.

They are mapped to subClassOf-relationship (and its

inverse) in RDF/OWL ontologies and to the broader

term and narrower term relation in thesaurus speciﬁ-

cation (e.g., based on ISO 25964).

Aggregation is used to specify a part-of relation-

ship between two UML classes. We subsume both

aggregation (parts can exist independently) and com-

position (parts cannot exist independently) under the

term aggregation. In linguistics part-whole relations

are referred to as meronymicrelationships (meronyms

being the parts and holonyms being the wholes). Part-

MODELSWARD 2018 - 6th International Conference on Model-Driven Engineering and Software Development

Table 1: Corresponding semantic relationship types of dif-

ferent modeling paradigms.

Modeling Lan-

guage Relation-

ship

Lexical-

Semantic

Relation-

ship

Knowledge Source

Relationship

Specialization Hyponymy Subclass,

Narrower Term

Generalization Hypernymy Subclass (inv.),

Broader Term

Aggregation

(Part)

Meronymy HasPart (SPW)

Meronym (WordNet)

Aggregation

(Whole)

Holonymy PartOf (SPW)

Holonym (WordNet)

Association

(named)

Agent-

Action

Object Property

Association

(unnamed) or

group of classes

Semantic

Relatedness

Related Term

whole relationships are neither directly supported in

thesaurus deﬁnition nor in RDF/OWL ontology spec-

iﬁcation. There is a W3C best practice speciﬁcation

Simple Part Whole (SPW) with hasPart and partOf

relationships that we support. Nevertheless, there

are knowledge bases that contain part-whole relation-

ships but use a non-standard vocabulary (e.g., Word-

Net).

Association is the third kind of conceptual rela-

tionship we analyzed with respect to other represen-

tations. We differentiate between two types: un-

named associations to express a simple dependency

between two domain model classes and named as-

sociations further specifying the kind of association

(usually with a verb). In linguistics named dependen-

cies fall into the category of case relationships (Chaf-

ﬁn and Herrmann, 1984), more speciﬁcally, in our

case, agent-action relationships. To some extent

RDF/OWL object properties with domain and range

restrictions can be compared to named associations.

In lexical semantics the unnamed association is re-

ferred to as semantic relatedness, an associative re-

lationship describing any functional relationship be-

tween two words. The related term relationship of

thesauri is mapped to this relationship. From a lexical

point of view the unnamed association is similar to a

set of classes grouped by the diagram itself.

Based on these mappings we are able to retrieve

related domain model elements from different knowl-

edge sources for all modeling support scenarios.

Components of the Recommender System. Fig-

ure 12 depicts the architecture of the DoMoRe rec-

ommender system. DoMoRe is integrated into the

Eclipse environment using a set of plug-ins. The

Model Listener observes changes in Ecore models

that are developed with the Ecore diagram editor.

Whenever a change in a model is made the current

content of the model is retrieved together with the

newly added or changed model element and its re-

lationships. The Recommender is notiﬁed and co-

ordinates all subsequent steps of modeling sugges-

tions. First, the domain model is transformed into

a lexical-semantic representation using the domain-

speciﬁc terms and semantic relationship mappings

(c.f., Table 1). Based on this representation the Se-

mantic Network is queried for related terms and di-

rectly delivers ranked lists of related terms to the rec-

ommender. The Ontology Connector manages the set

of connected knowledge bases and is queried as well.

It incorporates the mediator and mapper (c.f., Fig-

ure 10) and operates the translation of the termino-

logical queries to knowledge base speciﬁc queries and

the integration of results. The recommender controls

two components with which the user interacts. The

Model Advisor is a view in the Eclipse environment

that displays contextual information of the model el-

ements. It shows possible generalizations, special-

izations, aggregations, associations, and related ele-

ments. The developer can use this view to easily add

new content to a domain model by drag & drop of

suggested elements to the diagram. The appropriate

relationships will be created automatically. Semantic

Autocompletion is triggered in case a new element in

the model is named or the name of an existing element

is changed. This feature behaves like a search engine.

A context-sensitive pop-up list with names for the re-

spective element is displayed and the suggestions are

ﬁltered while typing.

Onto

Connector

Model

Listener

Semantic

Auto-

completion

Model

Advisor

Recommender

Eclipse Modeling

Environment

SemNet

Figure 12: Architecture of the DoMoRe recommender sys-

tem.

Recommendation Generation. In this paragraph

we provide insights into the features of the recom-

mender system by means of examples. We demon-

strate the knowledge retrieval and recommendation

generation for Scenario 3: A new unconnected class

is created and names for that class are suggested. Fig-

ure 13a shows a small domain model example con-

taining two classes connected with a named associ-

DoMoRe – A Recommender System for Domain Modeling

ation. After creating the new class the model lis-

tener triggers the recommender and the lexical rep-

resentation of the domain model is generated (c.f.,

Figure 13b). The information need depends on the

model reﬁnement step. In this case, noun terms are

required that are semantically related to both Hospi-

tal and Doctor (c.f., Figure 13c).

Figure 13: Lexical preparation in the procedure of the rec-

ommendation generation.

In the following, the information need is decom-

posed to separate lexical queries for each term and

relationship type (c.f., Figure 14d). The main rea-

son for retrieving the information separately is that

practically no conceptual knowledge base exists that

contains n-ary relationships. In contrast, our semantic

network directly supports ternary relationships allow-

ing more precise results for term pairs, but for more

than two terms separate queries have to be executed

in any case. In the next step, the semantic network is

queried for each term (c.f., Figure 14e) and each con-

nected knowledge base is queried for each term (c.f.,

Figure 14f).

Figure 14: Retrieval in the procedure of the recommenda-

tion generation.

So far, for each term of the original domain model,

and for each relationship type, and for each knowl-

edge source separate lists of related terms have been

determined

. First, results from the knowledge bases

are integrated based on the following principle. Per

term the distinct union of all intermediate results (e.g.,

related terms of ”doctor” from WordNet, BabelNet,

ConceptNet, etc.) is created it is recorded how often

each term occurred. The resulting lists have prelim-

inary order indicating more important terms appear

ﬁrst (c.f., Figure 15g).

For example, if three classes exist in the model and

broader and narrower relationships have to be retrieved

from ﬁve knowledge sources, 30 intermediate result lists

will be generated.

Figure 15: Integration and ranking in the procedure of the

recommendation generation.

In the ﬁnal step, the presorted knowledge base re-

sults are integrated into the semantic network results.

First, the knowledge base result and the respective se-

mantic network result for each term are joined using

the importance weight. It assures that terms found in

many knowledge bases receive more prominent posi-

tions in the ﬁnal ranking. Secondly, one ﬁnal list of

recommended terms is created. Separate results are

intersected and relative frequencies of common terms

are multiplied. The ﬁnal list is divided into n seg-

ments: Terms that are related to n query terms appear

ﬁrst. After that terms follow that are related to n − 1

query terms, and so forth. Finally, a sorting by rel-

evance is achieved by applying the pointwide mutal

information score (c.f., Figure 15h). This measure-

ment is explained in the paragraph ”Ranking”.

Generated lists of ranked terms are directly used

in the Semantic Autocompletion feature of the recom-

mender system. Whenever the name of a class or as-

sociation is edited a context-sensitive pop-up list of

related terms is shown. It behaves like a search en-

gine and is ﬁltered while typing (c.f., Figure 16).

Figure 16: Semantic Autocompletion of the recommender

system: Context-sensitive name predictions and inﬁx

search.

For the other scenarios preparation, retrieval, inte-

gration, and ranking is similar. They only differ in the

queried relationship type (e.g, subclasses/narrower

terms instead of related terms). To display contextual

information several relationship types are queried and

displayed in the Model Advisor view (c.f., Figure 17).

MODELSWARD 2018 - 6th International Conference on Model-Driven Engineering and Software Development

Figure 17: Model Advisor of the recommender sys-

tem: Suggesting possible related classes, superclasses, sub-

classes, and aggregations.

Ranking. It is likely that queries to our semantic

network and the connected knowledge bases deliver

numerous related terms (up to a few thousand for

each query). The ranking implemented in the rec-

ommender component is responsible for presenting

the most relevant model elements ﬁrst. Hence, if a

list of related terms is retrieved, it will be ordered

and the most important terms appear at the top. This

is achieved by combining different relatedness mea-

sures.

From the construction of the semantic network we

know absolute frequencies of co-occurring terms (c.f.,

Figure 11b). For each term in the network we com-

pute relative frequencies with respect to each con-

nected related term. This normalization allows to

compare the relatedness among different terms. Both

measures allow a basic ranking of terms, but they have

a shortcoming: very general terms (e.g., time, man,

year) that appear in almost all contexts are likely to

be ranked on prominent positions.

To overcome this disadvantage we implement an

information theoretic measurement: Pointwise mu-

tal information (PMI) and its normalized form (c.f.,

Equations 1). It measures the dependency between

the probability of coinciding events and the probabil-

ity of individual events (ﬁrst introduced into lexicog-

raphy by (Church and Hanks, 1990)).

pmi(x, y) = log



p(x, y)

p(x)p(y)



npmi(x, y) =

pmi(x, y)

− log[p(x, y)]

(1)

Applying PMI to the semantic network means that

x and y are terms, and PMI relates the probability of

their coincidence p(x, y) with the probabilities of ob-

serving both terms independently p(x)p(y). PMI is

an associativity score of two terms taking into ac-

count their individual corpus frequency, thus, very

frequent and general terms receive lower scores. Un-

fortunately, this measurement also has a shortcom-

ing: Although very general terms are ranked lower,

very rare terms that co-occur only very few times with

other terms tend to receive high scores.

Finally, in order to achieve a balanced ranking our

recommender system uses the lexicographers mutual

information (LMI), which is the NPMI score multi-

plied with the absolute co-occurrence frequency (Mi-

lajevs et al., 2016).

5 DoMoRe IN PRACTICE

In this section we summarize experiences of using

our recommender system in different settings and do-

mains. A ﬁrst prototype of the system was used in the

context of the BIZWARE project (Agt et al., 2012;

Agt and Kutsche, 2013). BIZWARE investigated the

potential of domain-speciﬁc languages (DSLs) and

model-driven engineering for small and medium en-

terprises in different domains like healthcare, man-

ufacturing/production, ﬁnance/insurance, publishing

and facility management. The actors in the setting

were software engineers of the respective companies

aiming at the introduction of DSL-based workﬂowsto

improve their development tasks. Modeling experts

from research closely worked together with the soft-

ware engineers and provided the recommender sys-

tem. The companies planned to used DSLs in cus-

tomer projects, but in the course of the project it

turned out that using DSLs for modernizing own soft-

ware products and development infrastructures was

more effective. The software engineers had little ex-

perience with DSLs, therefore, DoMoRe mainly sup-

ported the domain analysis phase to determine and

agree on domain-speciﬁc terms that were later used

in metamodels of the DSLs. Especially, the sugges-

tions helped in the abstraction process to correctly dif-

ferentiate between class and instance level. Analysis

of the modeling sessions showed that the ranking had

to be improved (too general terms at top positions)

and the software engineers missed suggestions of re-

lations between domain-speciﬁc terms (extraction of

verbal relations was not available at that time).

DoMoRe was also used in the dwerft project (Agt-

Rickauer et al., 2016), a research collaboration to

apply Linked Data principles for metadata exchange

throughall steps of the media value chain. The project

successfully integrated of a set of ﬁlm production

tools based on the Linked Production Data Cloud, a

technology platform for the ﬁlm and tv industry to

enable software interoperability used in production,

distribution, and archival of audiovisual content. One

of the crucial tasks of the project was the develop-

ment of a common data model conveying all metadata

from the different production steps (e.g., ﬁlm script,

production planning, on-set information, post produc-

tion, distribution). The actors in the setting were do-

DoMoRe – A Recommender System for Domain Modeling

main experts of the respective tasks (most of them

with no technical background) and modeling experts

that built the domain models and metadata schemata.

A lot of interviews with domain experts had to be

conducted to collect domain-speciﬁc knowledge and

discuss drafts of domain models. DoMoRe mainly

supported subsequent modeling steps after the meet-

ings and preparation of interviews with more exten-

sive models that allowed a more efﬁciently agreement

on necessary metadata.

Currently, DoMoRe is used in the context of the

AdA project

, an interdisciplinary research group in

which ﬁlm scholars work together with computer sci-

entists to support empirical ﬁlm studies with tool-

supported semantic video annotation and automated

video analysis. The project objective is to reduce

the effort of elaborate, manual annotation routines to

accelerate the ﬁlm-scientiﬁc analysis of audiovisual

movement patterns on the level of larger data sets. All

annotation data and analysis results will be published

as Linked Open Data using the project’s semantic vo-

cabularies. The collaboration with respect to domain

modeling is similar to the aforementioned project. We

expect similar support by using the recommender sys-

tem for the domain expert interviews.

6 RELATED WORK

Modeling Assistance. Systems for modeling assis-

tance provide additional information and features dur-

ing the modeling process to help the development

of models. They usually concentrate on two areas:

(1) Creation of model libraries or similar content and

(2) developing assistance frameworks and features

that use these libraries. Largest known model repos-

itory in the area of UML models and metamodels is

the GothenburgUML Repository (Hebig et al., 2016).

It contains over 20,000 models crawled from the web,

images, and from GitHub (only a collection of almost

1,000 models is publicly searchable). It is not sur-

prising, that the majority of the models are imple-

mentation models rather than domain models (e.g.,

a search for ”hospital” or ”doctor” returned 7 mod-

els while a search for ”interface” returned 90 mod-

els). Other important resources are ReMoDD (France

et al., 2012), MOOGLE (Lucr´edio et al., 2012), the

AtlanMod Metamodel Zoos

(altogether containing a

few hundred models).

The HERMES project a framework to build model

recommendation systems in order to support reuse of

http://www.ada.cinepoetics.fu-berlin.de/

http://web.emn.fr/x-info/atlanmod/index.php?title=Zoos

software models (Dyck et al., 2014). Its main goal

is to provide tool support for building model libraries

and offering the implementation infrastructure to cre-

ate recommenders that use content of these libraries.

The EXTREMO assistant (Segura et al., 2016) is a

similar tool to facilitate metamodel development with

uniform model element search in a model repository.

We are sharing the same objectives, but both sys-

tems face a cold start problem: Reusable content will

become only available, if enough solutions have al-

ready been developed or converted to the repository,

but new projects already want to beneﬁt from the do-

main knowledge. To some extent this challenge is ad-

dressed by using WordWeb/WordNet, which we also

use, but those databases contain roughly 150,000 con-

cepts in contrast to our semantic network with 5.7M

terms.

Knowledge-based Modeling. There are several

works on how model-driven engineering can ben-

eﬁt from formalized knowledge. On the concep-

tual level there are approaches to unify ontologi-

cal and software modeling paradigms (K¨uhne, 2016),

approaches to adopt modeling concepts from each

other (Henderson-Sellers et al., 2015), and to extend

MDE languages with ontological foundations (Guiz-

zardi, 2005).

The OntoDSL framework (Walter et al., 2014)

leverages ontology technologies on metamodel level

(e,g, reasoning) to assist DSL users with detect-

ing inconsistencies on model level. The CoCoViLa

tool (Ojamaa et al., 2015) generates metamodels of

domain-speciﬁc languages from OWL descriptions

and an approach by (Tairas et al., 2008) use ontologies

in the analysis phase of DSL development, but both

works require manual development of the respective

ontologies.

Several knowledge sources from other research ar-

eas exist that can be exploited for domain modeling.

The most famous resource is WordNet (Fellbaum,

1998), a lexical database for the English language,

that most other knowledge-based approaches use (as

we do). It contains around 82,000 nouns synsets and

100,000 noun relationships. OpenCyc (Lenat, 1995)

is common sense ontology with around 230,000

classes and 300,000 relationships. ConceptNet (Speer

and Havasi, 2012) is a multilingual semantic graph

that contains approximately 415,000 English con-

cepts and 900,000 relationships. Linked Open Vocab-

ularies

is a dataset that stores vocabulary speciﬁca-

tions. To the best of our knowledge BabelNet (Nav-

igli and Ponzetto, 2012) is the largest available se-

mantic dictionary with 3 million concepts. It is based

http://lov.okfn.org/dataset/lov/

MODELSWARD 2018 - 6th International Conference on Model-Driven Engineering and Software Development

on WordNet, integrates several other dictionaries and

uses machine translation to achieve multilingualism.

Our DoMoRe recommender system uses all of them

to retrieve terms and domain information.

7 CONCLUSION AND FUTURE

WORK

We presented DoMoRe, a recommender system that

automatically suggests model elements for domain

models. The system relies on a large-scale seman-

tic network of related terms that features 5.7 mil-

lion distinct nodes and 114 million binary and ternary

weighted relationships. DoMoRe additionally inte-

grates with several existing knowledge bases using

mediator-based information retrieval of lexical infor-

mation. This allows to provide context-sensitive in-

formation during domain modeling (Model Advisor)

and to suggest semantically related names for model

elements ordered by relevance (Semantic Autocom-

pletion).

In our future work, we will address more mod-

eling support scenarios (e.g., suggestion of attributes,

operation names, relationship types) that require other

types of information extraction. We will also inves-

tigate how lexical information can be used to detect

semantic inconsistencies in domain models.

Currently, we are preparing a controlled experi-

ment to quantitatively measure the efﬁciency of our

recommender system in contrast to the qualitative

feedback we received earlier during the practical ap-

plication of DoMoRe. Participants will be introduced

to a modeling tool and asked to perform several do-

main modeling tasks. Subjects are randomly divided

into a treatment group using the tool with recom-

mendation support and into a control group model-

ing without the recommender system. It is planned

to measure the outcome variables time on task and

model completeness.

ACKNOWLEDGMENT

This work is partially supported by the Federal Min-

istry of Education and Research under grant number

01UG1632B.

REFERENCES

Agt, H. and Kutsche, R.-D. (2013). Automated construc-

tion of a large semantic network of related terms

for domain-speciﬁc modeling. In Advanced Informa-

tion Systems Engineering, 25th International Confer-

ence, CAiSE 2013, Valencia, Spain, June 17-21, 2013,

volume 7908 of Lecture Notes in Computer Science

(LNCS), pages 610–625. Springer.

Agt, H., Kutsche, R.-D., Natho, N., and Li, Y. (2012). The

bizware research project. In Model Driven Engineer-

ing Languages and Systems-Exhibition Track, 15th In-

ternational Conference, MODELS.

Agt-Rickauer, H., Waitelonis, J., Tietz, T., and Sack, H.

(2016). Data integration for the media value chain.

In International Semantic Web Conference (Posters &

Demos).

Almeida, M., Souza, R., and Fonseca, F. (2011). Semantics

in the semantic web: a critical evaluation. Knowledge

organization, 38(3):187–203.

Atkinson, C. and K¨uhne, T. (2015). In defence of deep mod-

elling. Information & Software Technology, 64:36–51.

Banko, M. (2009). Open Information Extraction for the

Web. PhD thesis, University of Washington.

Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M.,

and Etzioni, O. (2007). Open information extraction

from the web. In IJCAI, volume 7, pages 2670–2676.

Chafﬁn, R. and Herrmann, D. J. (1984). The similarity and

diversity of semantic relations. Memory & Cognition,

12(2):134–141.

Church, K. W. and Hanks, P. (1990). Word association

norms, mutual information, and lexicography. Com-

putational linguistics, 16(1):22–29.

Colace, F., De Santo, M., Greco, L., Amato, F., Moscato,

V., and Picariello, A. (2014). Terminological ontol-

ogy learning and population using latent dirichlet al-

location. Journal of Visual Languages & Computing,

25(6):818–826.

Dyck, A., Ganser, A., and Lichter, H. (2014). On design-

ing recommenders for graphical domain modeling en-

vironments. In Model-Driven Engineering and Soft-

ware Development (MODELSWARD), 2014 2nd In-

ternational Conference on, pages 291–299. IEEE.

Evans, E. (2004). Domain-driven design: tackling complex-

ity in the heart of software. Addison-Wesley Profes-

sional.

Fellbaum, C. (1998). WordNet : An Electronic Lexical

Database. The MIT Press, Cambridge, MA.

Fowler, M. (2010). Domain-speciﬁc languages. Pearson

Education.

France, R. B., Bieman, J. M., Mandalaparty, S. P., Cheng,

B. H., and Jensen, A. (2012). Repository for model

driven development (remodd). In Software Engineer-

ing (ICSE), 2012 34th International Conference on,

pages 1471–1472. IEEE.

Frank, U. (2013). Domain-speciﬁc modeling languages: re-

quirements analysis and design guidelines. In Domain

Engineering, pages 133–157. Springer.

Frank, U. (2014). Multi-perspective enterprise modeling:

foundational concepts, prospects and future research

challenges. Software & Systems Modeling, 13(3):941–

962.

DoMoRe – A Recommender System for Domain Modeling

Guizzardi, G. (2005). Ontological foundations for struc-

tural conceptual models. CTIT, Centre for Telematics

and Information Technology.

Hebig, R., Quang, T. H., Chaudron, M. R., Robles, G., and

Fernandez, M. A. (2016). The quest for open source

projects that use uml: mining github. In Proceedings

of the ACM/IEEE 19th International Conference on

Model Driven Engineering Languages and Systems,

pages 173–183. ACM.

Henderson-Sellers, B., Gonzalez-Perez, C., Eriksson, O.,

Agerfalk, P. J., and Walkerden, G. (2015). Software

modelling languages: A wish list. In 7th IEEE/ACM

International Workshop on Modeling in Software En-

gineering, MiSE 2015, Florence, Italy, May 16-17,

2015, pages 72–77.

Huang, C.-r. (2010). Ontology and the lexicon: a natural

language processing perspective. Cambridge Univer-

sity Press.

Hutchinson, J., Whittle, J., and Rounceﬁeld, M. (2014).

Model-driven engineering practices in industry: So-

cial, organizational and managerial factors that lead to

success or failure. Science of Computer Programming,

89:144–161.

Ionita, D., Wieringa, R., Bullee, J.-W., and Vasenev, A.

(2015). Tangible modelling to elicit domain knowl-

edge: an experiment and focus group. In Conceptual

Modeling, pages 558–565. Springer.

Kuhn, A. (2010). On recommending meaningful names

in source and uml. In Proceedings of the 2nd Inter-

national Workshop on Recommendation Systems for

Software Engineering, pages 50–51. ACM.

K¨uhne, T. (2016). Unifying explanatory and constructive

modeling: towards removing the gulf between ontolo-

gies and conceptual models. In Proceedings of the

ACM/IEEE 19th International Conference on Model

Driven Engineering Languages and Systems, pages

95–102. ACM.

Lenat, D. B. (1995). Cyc: A large-scale investment in

knowledge infrastructure. Communications of the

ACM, 38(11):33–38.

Lucr´edio, D., Fortes, R. P. d. M., and Whittle, J. (2012).

Moogle: a metamodel-based model search engine.

Software & Systems Modeling, 11(2):183–208.

Maroto Garca, N. and Alcina, A. (2009). Formal description

of conceptual relationships with a view to implement-

ing them in the ontology editor protg. Terminology.

International Journal of Theoretical and Applied Is-

sues in Specialized Communication, 15(2):232–257.

McCrae, J., Aguado-de Cea, G., Buitelaar, P., Cimiano, P.,

Declerck, T., G´omez-P´erez, A., Gracia, J., Hollink, L.,

Montiel-Ponsoda, E., Spohr, D., et al. (2012). Inter-

changing lexical resources on the semantic web. Lan-

guage Resources and Evaluation, 46(4):701–719.

Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray,

M. K., Team, T. G. B., Pickett, J. P., Hoiberg, D.,

Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak,

M. A., and Aiden, E. L. (2011). Quantitative Analysis

of Culture Using Millions of Digitized Books. Sci-

ence, 331(6014):176–182.

Milajevs, D., Sadrzadeh, M., and Purver, M. (2016). Robust

co-occurrence quantiﬁcation for lexical distributional

semantics. ACL 2016, page 58.

Mylopoulos, J., Borgida, A., Jarke, M., and Koubarakis, M.

(1990). Telos: Representing knowledge about infor-

mation systems. ACM Transactions on Information

Systems (TOIS), 8(4):325–362.

Navigli, R. and Ponzetto, S. P. (2012). Babelnet: The au-

tomatic construction, evaluation and application of a

wide-coverage multilingual semantic network. Artiﬁ-

cial Intelligence, 193:217–250.

Ojamaa, A., Haav, H.-M., and Penjam, J. (2015). Semi-

automated generation of dsl meta models from formal

domain ontologies. In Model and Data Engineering,

pages 3–15. Springer.

Oliv´e, A. (2007). Conceptual Modeling of Information Sys-

tems. Springer-Verlag New York, Inc., Secaucus, NJ,

USA.

Reggio, G., Leotta, M., and Ricca, F. (2014). Who

knows/uses what of the uml: a personal opinion sur-

vey. In Model-Driven Engineering Languages and

Systems, pages 149–165. Springer.

Reinhartz-Berger, I. (2010). Towards automatization of

domain modeling. Data & Knowledge Engineering,

69(5):491–515.

Segura,

A. M., Pescador, A., de Lara, J., and Wimmer, M.

(2016). An extensible meta-modelling assistant. In

Enterprise Distributed Object Computing Conference

(EDOC), 2016 IEEE 20th International, pages 1–10.

IEEE.

Speer, R. and Havasi, C. (2012). Representing General

Relational Knowledge in ConceptNet 5. In Proceed-

ings of the Eight International Conference on Lan-

guage Resources and Evaluation (LREC’12), Istan-

bul, Turkey.

Storey, V. C. (1993). Understanding semantic relationships.

The VLDB Journal, 2(4):455–488.

St¨orrle, H. (2010). Structuring very large domain models:

experiences from industrial mdsd projects. In Pro-

ceedings of the Fourth European Conference on Soft-

ware Architecture: Companion Volume, pages 49–54.

ACM.

Tairas, R., Mernik, M., and Gray, J. (2008). Using ontolo-

gies in the domain analysis of domain-speciﬁc lan-

guages. In International Conference on Model Driven

Engineering Languages and Systems, pages 332–342.

Springer.

Turney, P. D. and Pantel, P. (2010). From frequency to

meaning: vector space models of semantics. J. Artif.

Int. Res., 37(1):141–188.

Walter, T., Parreiras, F. S., and Staab, S. (2014). An

ontology-based framework for domain-speciﬁc mod-

eling. Software and Systems Modeling, pages 1–26.

Whittle, J., Hutchinson, J., and Rounceﬁeld, M. (2014). The

state of practice in model-driven engineering. Soft-

ware, IEEE, 31(3):79–85.

Williams, S. (2013). An analysis of pos tag patterns in on-

tology identiﬁers and labels. Technical report, Tech-

nical Report TR2013/02, Department of Computing,

The Open University, UK.

MODELSWARD 2018 - 6th International Conference on Model-Driven Engineering and Software Development