BPMN Model and Text Instructions Automatic Synchronization

Leonardo Guerreiro Azevedo

1,2

, Raphael de Almeida Rodrigues

and Kate Revoredo

Graduate Program in Informatics (PPGI), Federal University of the State of Rio de Janeiro (UNIRIO),

Av. Pasteur, 456, Urca, 22290-240, Rio de Janeiro, RJ, Brazil

IBM Research, IBM, Av. Pasteur 146, Botafogo, 22290-240, Rio de Janeiro, RJ, Brazil

Keywords:

BPM, BPMN, Natural Language Generation, Natural Language Processing.

Abstract:

The proper representation of business processes is important for its execution and understanding. BPMN is

the de facto standard notation for business process modeling. However, domain specialists, which are experts

in the business, do not have necessarily the modeling skills to easily read a BPMN model. Natural language is

easier to read for them. So, both model and text are necessary artifacts for a broad communication. However,

unilateral manual editions of them may result in inconsistencies. This research proposes a framework for

synchronizing BPMN model artifacts and its natural language text representation. It generates textual work

instructions from the model, and it updates the original model if the textual instructions are edited. The

framework was implemented using Java standard technology and evaluated through experiments. In the ﬁrst

experiment, we showed the knowledge represented by the textual work instructions and the correspondent

process models are equivalent. Furthermore, in a second experiment, we showed our approach for maintaining

the texts and models consistent performed satisfactory, where we veriﬁed the equivalence of the two artifacts.

1 INTRODUCTION

Many companies maintain both process models and

textual work instructions to depict their processes

(van der Molen, 2011). The use of both represen-

tations is needed to address speciﬁc audience. Usu-

ally, domain experts are not qualiﬁed for reading pro-

cess models, due to lack of knowledge in the mod-

eling notation, and they rely on the textual descrip-

tions. On the other hand, modeling experts prefer

using the model representation which are easier for

them to read and understand. Companies face redun-

dant effort for updating both process knowledge rep-

resentation artifacts. This task is error-prone, and may

bring inconsistencies.

The scientiﬁc problem addressed by this work is

how to maintain both knowledge representation arti-

facts (texts and models) synchronized automatically,

ensuring their consistency. We aim at enabling mod-

elers to generate texts from models and domain ex-

perts to create or update formal models through tex-

tual descriptions writing and editions. As a result,

the proposal leverages the information potential of al-

ready existing text documents. It preserves process

verbalization techniques. It provides substantial sav-

ings, reducing manual efforts. It solves the inconsis-

tency model-text problem. It enables a quicker real-

ization of BPM-projects and their beneﬁts.

This work proposes a round-trip framework for

automatically maintaining the consistency of pro-

cesses representations by reﬂecting to a process

model modiﬁcations done to the text and vice-versa.

The approach generates natural language text from

a process model, and updates the process model

from edited text. To perform these synchronizations,

the framework creates correlations among process

model’s elements and the text. So, if one change one

of the artifacts, it can use the framework to automati-

cally update the other.

Using our approach domain specialists do not

need to be trained in a speciﬁc business process mod-

eling notation or in process modeling discipline. They

can update a process model by editing the natural lan-

guage text derived from the original process model.

On the other hand, system analysts are relieved from

the time-intensive task of writing and updating natural

language texts that represents the models.

The remainder of this work is structured as fol-

lows. Section 2 presents the proposed round-trip

framework and its implementation. Section 3 presents

the evaluation. Section 4 presents the related works.

Finally, Section 5 presents the conclusions, proposals

of future work and existing framework’s limitations.

484

Guerreiro Azevedo, L., de Almeida Rodrigues, R. and Revoredo, K.

BPMN Model and Text Instructions Automatic Synchronization.

DOI: 10.5220/0006809604840491

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 484-491

ISBN: 978-989-758-298-1

2 THE ROUND-TRIP

FRAMEWORK

This section presents the round-trip framework. Sec-

tion 2.1 presents an overview of the framework and

the scenarios it supports. Section 2.2 presents the trip

to generate text from business process models, while

Section 2.3 presents the trip to generate business pro-

cess models from text instructions.

2.1 Framework Overview and

Supported Scenarios

Figure 1 gives an abstract overview of the round-trip

framework. One can starts from models or texts, top

and bottom of the ﬁgure, respectively. The links be-

tween model’s elements and text’s sentences (mid-

dle of the ﬁgure) are created through pattern match-

ing and stored for synchronization queries executed

by Process Model to Natural Language (labeled as P)

and Natural Language to Process Model (labeled as

N) components. The pattern matching rules are de-

ﬁned as templates.

Figure 1: Abstract overview of the round-trip framework.

The framework ﬁts the following scenarios:

• Generate Text from Process Model: The frame-

work receives a model as input, triggers the

Process Model to Natural Language component,

which queries the linked elements, and generates

the whole text as natural language sentences.

• Generate Model from Process Textual Descrip-

tions: The framework receives a text as input,

triggers the Natural Language to Process Model

component, which queries the linked elements,

and generates the whole model.

• Update A Model from Edited Text: The frame-

work’s Synchronization Component (labeled as S)

detects changes in the text, and reﬂects them to the

process model. This is done by triggering the Nat-

ural Language to Process Model component and,

thus, generating an updated version of the model.

This scenario only reﬂects changes, updating only

the process model’s elements that were altered.

• Update a Text from Edited Model: The text up-

date is handle as a new text generation scenario,

i.e., the whole model is submitted as input to the

Process Model to Natural Language component

and the whole text is regenerated. The decision

to generate the whole text again instead of updat-

ing only sentences which had changes was based

on cost-beneﬁt analysis, considering the perfor-

mance gain and development effort.

• Round-Trip: This scenario is represented by a

generation cycle. For illustration purpose, con-

sider the process model as starting point. The

model is submitted as the framework’s input, and

the text is generated. Then, the text is manually

changed and the original process model is auto-

matically updated. In the same cycle, the up-

dated model is submitted again to assert whether

it matches the text description. At this point, the

user can go in both directions, generate text from

model or model from text.

The NLG and NLP core components are com-

posed by several ready-to-use building blocks (Frozen

spots) and deﬁne interfaces which must be imple-

mented to support speciﬁc languages (Hot spots).

Interfaces provide the ﬂexibility to satisfy speciﬁc

needs. For instance, regarding NLG, each hot spot

can be implemented for a speciﬁc language (e.g., Ger-

man and Spanish) without the need to change any

component. Regarding NLP, the hot spots can be

implemented to override the default behaviour or to

offer support to different text formats (i.e., text pat-

terns). The architecture’s frozen spots are represented

by classes, while the hot spots are represented by in-

terfaces (Pree, 1994).

Figure 2 presents a package diagram of the

hot spots implementations developed for Por-

tuguese and English. They are sufﬁxed as Re-

alizer since they realize the implementations of

GeneralLanguageCommon package interfaces. Each

language has its own speciﬁc implementation

(e.g., PortugueseLabelHelper class implements

ILabelHelper). So, PortugueseRealizer and

EnglishRealizer classes implement hot spots and

use frozen spots to accomplish necessary tasks.

BPMN Model and Text Instructions Automatic Synchronization

485

Figure 2: Implementation of the hot spots deﬁned by the

architecture.

2.2 Model to Text: Natural Language

Generation from BPMN Process

Model

This section describes the component Process Model

to Natural Language, i.e., the NLG component.

The main challenge for generating text from pro-

cess models is to adequately analyze the existing

natural language fragments from the process model

elements, and to organize the information in a se-

quential fashion. The Model to Text component

of the framework was implemented following the

Three-Step NLG pipeline proposed by Reiter and

Dale (Ehud Reiter, 1997), which are:

• Text Planning: Determines the information to be

communicated in the text, and speciﬁes the order

this information should be conveyed.

• Sentence Planning: Chooses speciﬁc words to ex-

press the information determined in the previous

step. It may aggregate messages and include pro-

nouns in the text to obtain smoothly text.

• Sentence Realization: Transforms the messages

into grammatically correct sentences.

The pipeline was instantiated as presented in Fig-

ure 3 and detailed as follows:

• Text Planing

– Linguistic Information Extraction: Decom-

poses process model element’s labels using

a linguistic label analysis technique (Leopold

et al., 2013). For instance, it decomposes

the activity label Inform customer about prob-

lem into action (inform), business object (cus-

tomer), and addition (about problem).

– Annotated RPST Generation: Creates Re-

ﬁned Process Structure Tree (RPST) (Vanhat-

alo et al., 2009)) from the process model.

– Text Structuring: Annotates the RPST tree’s

nodes with the linguistic information obtained

in the ﬁrst step.

• Sentence Planning

– DSynT-Message Generation: Maps the anno-

tated RPST elements to a list of intermedi-

ate messages. Each sentence is stored as a

Deep-Syntactic Tree (DSynT) (Mel’

cuk and

Polguere, 1987).

– Message Reﬁnement: Aggregates messages,

generates referring expressions (e.g., replace

the role analyst to he), and inserts discourse

marker (e.g., afterwards or subsequently) if

needed. It performs these reﬁnements usually

on long sequences of tasks.

• Realization

– Surface Realization: Transforms the interme-

diate messages into grammatically correct sen-

tences. This is accomplished by systematically

mapping the generated DSynT to the corre-

sponding grammatical entities.

2.3 Text to Model: BPMN Process

Model Generation from Natural

Language Texts

This section describes the component Natural Lan-

guage to Process Model, i.e., the NLP component.

It uses NLP techniques to extract and parse texts

to identify BPMN process model elements and cre-

ate/update the model. It was implemented as pre-

sented in Figure 4 and detailed as follows:

• Text Planning

– Text Pattern Extraction: Infers the textual pat-

tern from the text received as input. It vali-

dates if the text received as input follows the

supported text patterns, thus saving both user’s

time and system processing resources.

– Natural Language Processing: Analyzes the

text’s semantic and extracts relevant linguis-

tic information. It employs NLP techniques

to: remove stop words (e.g., articles and dis-

course markers, like Then, Afterwards); recog-

nize patterns (e.g., end of line); split the text

into sentences; split the sentences into words

array and classify words according to their re-

spective syntactic rule, i.e., it executes Part-Of-

Speech Tagging (POS), which consists of tag-

ging each word with its respective syntactic role

(e.g., plural noun, adverb).

– Text Cleaning: Replaces referring expressions

and sentences aggregations (e.g., replaces he by

the role analyst).

• Sentece Text Planning

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

486

Figure 3: Extended pipeline approach for NLG.

– Sentence Text Generation and Structuring:

Generates SentenceText objects for the original

RealizedText while preserving the description

structure, e.g., generates activities in the correct

sequence order and correlation among them.

• Process Moderl Realization

– Model Elements Generation: Generates the

model elements from the SentenceText objects.

The support of multiple text structures and for-

mats is this step challenge.

– Process Model Structuring: Structures the gen-

erated elements according to the order which

they appear within the text and also respecting

sentences correlations. It takes advantage of

the link between the SentenceText and the as-

sociated model elements, and it beneﬁts from

the initial sentences structuring. Each Sen-

tenceText is mapped only to one model ele-

ment, and it is just needed to traverse through

the sentences tree nodes, fetch its associated

model element and create the ProcessModel

object. With the ProcessModel object (which

is not notation speciﬁc), it is possible to trans-

late it to any speciﬁc notation and print out the

whole process model to the desired output (e.g.,

JSON, XML, BPMN, etc), which is done by the

next and ﬁnal NLP’s pipeline component.

– BPMN Model Generation: Generates a busi-

ness process model in a BPM notation. In this

work implementation, the used notation was

BPMN. It generates a JSON ﬁle that represents

the process model as a machine artifact. The

JSON ﬁle can then be used in a BPMN graphic

modeler tool to dispose all the elements visu-

ally. In particular, Signavio

online platform

was used to read the JSON ﬁle and output the

graphic process model to the user.

available at www.signavio.com

3 EVALUATION

This section presents the evaluation of the techniques

developed within the language-independent frame-

work.

The ﬁrst experiment’s objective was Assess

whether the knowledge represented by the generated

process description can be considered equivalent to

the process model. It was guided by the following

research question (RQ1): “Is the knowledge repre-

sented by the natural language text, generated by the

framework, equivalent to the process model?”. Thus,

the focus were in determining how many answers

were within the equivalence range varying between

100 and 68%, how many were between 67 and 34%,

and, ﬁnally, how many were between 33 and 0%. The

expectation was the number of answers between 100

and 68% be higher than the sum of the other two

groups (participants who rate equivalence between 67

and 0%).

Figure 5 depicts the overall evaluation for this ex-

periment. As can be observed, 342 answers were

within the equivalence group ranging from 100% to

68%, which can be read as 74% of the participants

claim that the equivalence between both knowledge

representations vary from 100% to 68%

. We ar-

gue the chosen text structure can achieve the expected

results, which is to transmit the process knowledge

through a natural language representation. Based on

this, the answer for RQ1 is: The knowledge repre-

sented by the natural language text, generated by the

framework, can be considered equivalent to the pro-

cess model.

The questionnaire is available at

https://docs.google.com/forms/d/

1zvuuxojWUVWnyRpMsao-F1Yjygs-Dm2ebfrMjjjJqx4/

viewform (in Portuguese)

Each participant contributed with seven (7) answers.

Thus, if we divide the total number of answers by seven

(342/7) it gives us the average number of participants that

choose the same answer (48 participants).

BPMN Model and Text Instructions Automatic Synchronization

487

Figure 4: Text to model pipeline.

Figure 5: Subject’s answers distribution among equivalence

intervals.

The second experiment’s objective was Assess

whether the knowledge represented by the automati-

cally updated version of the process model, after been

synchronized by text-based changes, represents the

same knowledge as the manually updated textual de-

scription. It was guided by the following research

question (RQ2): “Is the knowledge represented by

the manually updated text equivalent to the auto-

matically updated process model?”. Thus, the fo-

cus was to determine how many answers were within

the range varying from Strongly Disagree to Strongly

Agree. The expectation was the number of answers

between Strongly Agree and Slightly Agree be higher

than the sum of the other groups (subjects who rate ac-

cordance with the synchronization strategy between

Neutral and Strongly Disagree). Thus, to enable a

better reading of the results, the answers for options

Strongly Agree and Slightly Agree were grouped into

one group. All the remaining answers were grouped

into a second group.

Figure 6 depicts the overall evaluation for this

question. As can be observed, 160 answers were

within the equivalence group ranging from Strongly

Agree and Slightly Agree, which can be read as 78%

of the subjects

claims the knowledge represented by

The questionnaire is available at

http://goo.gl/forms/JacmFbV3yTpPEﬁb2 (in Portuguese)

Each subject contributed with seven (14) answers.

the manually updated text is equivalent to the auto-

matically updated process model. Based on this, the

answer for RQ2 is: The knowledge represented by the

manually updated text can be considered equivalent

to the automatically updated process model.

Figure 6: Subject’s answers distribution among the avail-

able accordance options (grouped into two groups).

4 RELATED WORK

Several related works proposed similar approaches

to our framework. They can be grouped into three

(3) speciﬁc topics: (i) Text to model generation; (ii)

Model to textual description generation; and, (iii)

business process understandability.

4.1 Text to Model and Model to Text

Generation

We inspected related works where natural language

techniques was used to achieve BPM relevant goals

or areas where the creation of process models was the

focus. These approaches provided initial insights for

the construction of the NLG Core module architec-

ture). They are presented in Table 1 along with com-

parisons with our work.

Thus, if we divide the total number of answers by fourteen

(160/14), it gives us the average number of subjects that

choose the same answer (11 subjects)

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

488

Table 1: Papers’ Comparison.

Paper and Objective Description Comparision

Leopold et al.

(Leopold et al.,

2012):

Generate natural

language texts from

BPMN models.

Approach that automatically transforms

BPMN process models into natural lan-

guage texts. It is based on Reiter

and Dale’s NLG pipeline (Ehud Reiter,

1997). Moreover, Leopold et al. pro-

posed a new approach that validates pro-

cess model through natural language gen-

eration (Leopold et al., 2014).

Our framework is capable of parsing pro-

cess models and generating textual pro-

cess descriptions from these models like

Leopold et al. approach. However, our ap-

proach is capable of generating the model

from the text and is not language speciﬁc.

Gonc¸alves et

al. (de AR Goncalves

et al., 2009):

Generate conceptual

models from natural

language texts.

A method for deriving conceptual mod-

els from text focusing on the derivation of

models from group stories. They provide a

prototype which handles Portuguese texts.

As opposed to our work, theirs offer no

support to any other language, it parses

only story like texts, considered a limited

set of BPMN elements and provides a sim-

ple evaluation. Furthermore, a couple of

their exhibits show that syntactical prob-

lems occur in some cases.

Ghose et al. (Ghose

et al., 2007):

Generate UML com-

pliant process mod-

els from natural lan-

guage texts.

Toolkit that uses a syntax parser to identify

verb-object phrases in the given text and

also scans the text for textual patterns.

The results are BPMN model snippets

rather than a fully connected model.

Kop and Mayr (Kop

et al., 2005):

Generate mod-

els from natural

language texts.

Proposed a procedure called KCPM (Kla-

genfurt Conceptual Predesign Model) and

developed a corresponding tool. It parses

textual input in German and ﬁlls instances

of a generic meta-model, the KCPM. Us-

ing the information stored in this meta-

model, an UML activity diagram and a

UML class diagram can be created.

As opposed to our work, the transforma-

tion from natural language input to the

aforementioned meta-model is not a fully

automated process and supports only Ger-

man written texts.

Sinha et al. (Sinha

and Paradkar, 2010):

Generate process

model from natural

language text.

They employ a linguistic analysis engine

based on the UIMA Framework, which en-

ables the constructions of linguistic analy-

sis systems by combining different blocks

into a pipeline.

Their work does not contain a full text and

model example, which does not allow a

comparison with ours. Besides, it is a text

type speciﬁc since it uses only use-case de-

scriptions.

Wang et al. (Wang

et al., 2009):

Generate BPMN

model from struc-

tured use cases.

Describe a procedure which creates a

BPMN diagram, given that data items,

tasks, resources (actors) and constraints are

identiﬁed in an input text document.

Compared to Wang et al. work, our ap-

proach does not require user-interaction

during the parsing execution. Ours sup-

ports 15 different BPMN elements, and

does not impose any restriction regard-

ing the number of gateways outgoing arcs.

They also do not present any process

model they use.

Friedrich et

al. (Friedrich et al.,

2011):

Generate model from

natural language

text.

Proposes an automatic procedure for gen-

erating BPMN models from natural lan-

guage text composed by an anaphora res-

olution component. The evaluation count

with more than 45 models from both,

academy and industry.

Their work generates a conceptual model

from natural language text and does not

generate natural language texts from the

models. It is capable of parsing only En-

glish texts and they do not present possi-

bilities to extend or adapt the technique to

ﬁt speciﬁc needs.

4.2 Business Process Understandability

The ﬁeld of process model understandability is dis-

cussed from different perspectives.

Mendling et al. show the number of arcs has an

important effect on the overall model understandabil-

ity (Mendling et al., 2007). Mendling et al. demon-

strates the impact of natural language in the activ-

ity labels for model comprehension (Mendling et al.,

2010). Zugal et al. investigated how far the cogni-

BPMN Model and Text Instructions Automatic Synchronization

489

tive inference process affects the model understand-

ing (Zugal et al., 2011). Leopold et al. build on these

insights trying to lower the overall burden of process

model comprehension (Leopold et al., 2014).

Prior work comparing process modeling nota-

tions can be roughly grouped into two categories:

(i) Graphical notation comparison; (ii)Textual versus

graphical notation comparison. The ﬁrst is the most

prominent one. Several of these studies suggest ex-

pertise is the most relevant factor in comprehension

(Curtis et al., 1989), but there is no absolute better

or worse representation. In a previous work, we run

an experiment on process model understandability us-

ing textual work instructions and BPMN Models. The

results were instructions or process models do not in-

ﬂuence process understandability for non-expert users

but do inﬂuence experienced users (Rodrigues et al.,

2015).

Haisjackl and Zugal compared declarative pro-

cess models against a text based notation using sub-

jects with experience in modeling declarative pro-

cess (Haisjackl and Zugal, 2014). Different from their

work, ours used imperative process models, involved

subjects with experience in process modeling varying

from none to expert, and presented a natural language

text simulating a human description of the process.

While the experiment described by Ottensooser et

al. compares model understanding with written use

cases (using the Cockburn format), our framework is

based on a more ﬂuent and natural representation of

the text (Ottensooser et al., 2012). More speciﬁcally,

it was considered a natural language text which re-

quires no background knowledge of layout or speciﬁc

patterns. Also, our research focus on process under-

standing while Ottensooser et al. focus on domain

understanding through different representations. Nev-

ertheless, our work’s ﬁndings corroborates to Otten-

sooser’s results which indicate there is no signiﬁcant

superiority between using graphical or textual nota-

tion for describing business process.

5 CONCLUSION

This work proposed a round-trip framework capa-

ble of automatically generating natural language text

from process models and updating these models from

text editions. It solves the problem of maintaining

text and model elements representation synchronized,

saves time and effort of analysts to manually write

text descriptions, enables domain experts to edit for-

mal process models without the efforts of learning a

business process modeling language.

The round-trip framework combines existing tools

from graph decomposition, natural language process-

ing and generation in an innovative way. The frame-

work provides the foundations for integrating textual

and model-based information, meeting the challenge

of processing both textual process descriptions and

models.

From a practical perspective, the framework helps

organizations to simplify business process manage-

ment since: (i) Creates automatically business pro-

cess instructions in natural language text; (ii) Allows

update the process model from text editions; (iii) Re-

duces efforts required by a business analyst which can

use the text to understand the processes.

The ﬁrst experiment’s concluded the textual work

instructions can be considered equivalent, in terms of

knowledge representation, to process models within

an acceptable threshold. 74% of the subjects claims

the equivalence between both knowledge representa-

tions vary from 100% to 68%, and 86% of the sub-

jects claims the textual descriptions vary from ex-

cellent to good. This result is aligned with what

we expected due to the use of NLG techniques like

Discourse Marker insertion and Referring expression

generation, which are capable of enhancing the text

and improving its readability. The second experiment

concluded the knowledge represented by the manu-

ally updated text can be considered equivalent to the

automatically updated process model after the syn-

chronization within an acceptable threshold. 78%

of the subjects claims knowledge represented by the

manually updated text is equivalent to the automati-

cally updated process model.

Despite these encouraging results, the framework

is able to read process descriptions consisting of full

sentences. Another prerequisite is the text be gram-

matically correct, does not contain questions, and

has little process irrelevant information. Also, Word

Sense Disambiguation and Named Entity Recognition

was not within the scope. Another issue is the test

data set, which comprised thirty text-models. It is a

relatively small dataset. Therefore, this research’s test

results are not fully generalizable.

Some future work are: evolve the framework to

a “human in the loop” approach through the interac-

tion with domain experts to provide missing informa-

tion interactively, and further relive the business ana-

lyst from interviewing tasks; support speciﬁcation or

overriding the text patterns; analyze and evolve it to

process large amounts of textual information, cover

other BPMN elements, include charts and other data

models, and add new languages.

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490

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