Informatica 42 (2018) 77–84 77
Towards Creative Software Blending: Computational Infrastructure and Use
Cases
Matej Martinc1,2, Martin Žnidaršič1, Nada Lavrač1,3 and Senja Pollak1
1 Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
2 Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
3 University of Nova Gorica, Nova Gorica, Slovenia
E-mail: matej.martinc@ijs.si, nada.lavrac@ijs.si, martin.znidarsic@ijs.si, senja.pollak@ijs.si
Keywords: computational creativity, software blending, visual programming platforms
Received: October 31, 2017
Numerous visual programming platforms support the generation, execution and reuse of constructed scien-
tific workflows. However, there has been little effort devoted to building creative software blending sys-
tems, capable of composing novel workflows by autonomously combining individual software components
or even entire workflows originally designed for solving tasks in different research fields. Based on the
review of relevant computational creativity research and of contemporary web platforms for workflow
construction, this paper defines the desired functionality of a software blending system. Considering the
required autonomy of the system and the workflow complexity limitations, we investigate the necessary
conditions for the implementation of a creative blending system within the existing visual programming
platforms.
Povzetek: Številne platforme za vizualno programiranje podpirajo gradnjo, izvajanje in ponovno uporabo
zgrajenih znanstvenih delotokov. Dosedanje raziskave niso posvečale pozornosti izdelavi kreativnih siste-
mov za spajanje programske opreme, ki bi bili sposobni avtonomnega sestavljanja posameznih program-
skih komponent ali celo celotnih delotokov, prvotno izdelanih za reševanje nalog na različnih znanstvenih
področjih. Na podlagi pregleda raziskav s področja računalniške ustvarjalnosti in obstoječih spletnih plat-
form za gradnjo delotokov v tem članku definiramo želeno funkcionalnost sistema za kreativno spajanje
programske opreme. Upoštevaje zahteve po avtonomnosti sistema in dovoljeno kompleksnost delotokov
preučimo tudi pogoje za implementacijo takega sistema v obstoječih platformah za vizualno programiranje.
1 Introduction
Creativity was defined by M. Boden [3] as “the ability to
come up with ideas or artefacts that are new, surprising,
and valuable”. It is considered as an aspect of human intel-
ligence, grounded in everyday abilities such as conceptual
thinking, perception, memory and reflective self-criticism.
Software is usually not considered creative because it
follows explicit instructions of the programmer [4]. Ho-
wever, writing software is considered to be a creative task.
If a program could define its own instructions, this would
clearly mean that the program has some level of creativity.
A subfield of artificial intelligence has recently emerged,
in which one of the main goals is the creation of software
that is able to model, simulate or replicate human creativity.
This field, called computational creativity, has been defi-
ned by S. Colton and G. Wiggins [6] as “the philosophy,
science and engineering of computational systems which,
by taking on particular responsibilities, exhibit behaviours
that unbiased observers would deem to be creative.”
Note that the field of computational creativity should not
be confused with the field of creative computing. Alt-
hough these two research areas partly overlap, creative
computing differs from computational creativity by gene-
rally not being considered as a subfield of artificial intel-
ligence, since it mostly addresses the task of creative de-
velopment of computing products and with how to write
software that would better serve the needs of the creative
community [13].
Infrastructures supporting computational creativity and
the generation of creative systems are scarce, although
some recent research attempts has tried to fill this gap. One
of the recent developments is FloWr [4], a system for im-
plementing creative systems as scripts over processes and
manipulated visually as flowcharts. Another is the Con-
CreTeFlows infrastructure [27], which was developed to
enable the construction, sharing and execution of compu-
tational creativity (CC) workflows, composed of software
ingredients of different partners of European project Con-
CreTe1. Both of these infrastructures use different types
of resources (e.g., musical, pictorial and textual inputs) in
order to support the development of some typical CC task
such as poetry generation, metaphor creation, generation of
narratives, creation of fictional ideas and conceptual blen-
ding.
1http://conceptcreationtechnology.eu
78 Informatica 42 (2018) 77–84 M. Martinc et al.
These platforms, which enable the user to build procedu-
res capable of producing a variety of different creative arte-
facts, could hardly be called creative systems, since they do
not exhibit creative behavior in terms of automated work-
flow development. The arguably most creative system for
automated workflow construction, optimization and altera-
tion, which is implemented in the FloWr platform, requires
a lot of manual user input and could only be called creative
with some major reservations.
To fill the identified gap, this paper addresses the task
of developing an infrastructure capable of autonomously
composing novel scientific workflows by creatively combi-
ning individual software components or even entire work-
flows originally designed for specific tasks in different rese-
arch fields. We consider the process of autonomous work-
flow composition—which we name creative software blen-
ding in this paper—to be an important first step towards a
long term goal of creating software that could write code
directly. The proposed system would be able to bridge dif-
ferent scientific fields by combining methods from specific
fields into novel interdisciplinary workflows. It would ide-
ally also be capable of automated interdisciplinary research
by autonomously discovering novel scientific procedures.
This paper presents the design principles underlying a
creative system described above. Section 2 introduces the
research topic and presents the infrastructures suitable for
the implementation of a creative software blending system.
Section 3 motivates this research by presenting two exis-
ting hand-blended workflows. Section 4 presents the re-
lated software blending and computational creativity rese-
arch, followed by an outline of the desired system functi-
onality, investigating the necessary conditions for the im-
plementation of a creative system for autonomous creative
workflow generation. The paper concludes by presenting
plans for future work.
2 Research background and
infrastructures
As background to our creative software blending research,
this section first outlines some creativity support tools, fol-
lowed by a brief description of a selection of easy-to-use
workflow management systems that allow the user to com-
pose complex computational pipelines in a modular visual
programming manner.
2.1 Creative software
As Colton’s and Wiggins’ definition of computational cre-
ativity [6] is hardly operational for measuring creativity of
a program, G. Ritchie [23] proposed some empirical crite-
ria for attributing creativity to a computer program. The
main idea is to use empirically observable and comparable
factors, such as the properties of the generated output of
the creative system, when trying to assess the creativity of
a system. These observable factors can be judged by two
quantifiable and essential criteria:
Novelty of an output determines to what extent is the
produced item dissimilar to existing examples of its
genre.
Quality of an output determines to what extent is the pro-
duced item a high quality example of its genre.
Using these criteria, we can say that the system for cre-
ative software blending is creative if it outputs novel and
high quality scientific workflows.
Another relevant question is what types of creative beha-
viors exist and how can they be computationally modeled.
Boden [3] distinguishes three basic types of creativity:
Combinational creativity involves making unfamiliar
combinations of familiar ideas.
Exploratory creativity involves exploration of a concep-
tual space, which is characterized as a structured style
of thought, and coming up with a new idea or artefact
within that thinking style.
Transformational creativity refers to the modification of
the conceptual space so that new kinds of ideas and
artefacts can be generated.
Combinational creativity is the easiest one to be modeled
on a computer. However, created combinations should be
meaningful and interesting, which usually requires a solid
background knowledge and the ability to form and evaluate
relations of many different types. Several programs exist
that can explore a given space and invent new artefacts with
a certain style, for example, a program for automatic music
generation [19] or a program for generating game designs
[7]. Some programs can even transform their conceptual
space by altering their own rules; for example, evolutionary
algorithms can make random changes in their current rules
and by this evolve new structures.
Another important distinction made by Boden [3] is a
distinction between psychological creativity (P-creativity)
and historical creativity (H-creativity). P-creativity rela-
tes to creation of surprising, valuable ideas and artefacts
that are new to the person who comes up with it. Howe-
ver, if an artefact or idea has arisen for the first time in
human history and (so far as we know) nobody else has
had it before, then we are talking about H-creativity. We
anticipate that if the targeted creative software blending sy-
stem is to be an active participant in scientific discovery or
artefact creation, it should ideally be H-creative, although
even a P-creative system can play a very useful supporting
role in scientific research and its development is therefore
a worthy research goal.
2.2 Infrastructures
A system for creative software blending would best be im-
plemented inside an already existing infrastructure ena-
bling interdisciplinary and creative scientific workflow
Towards Creative Software Blending. . . Informatica 42 (2018) 77–84 79
composition. In this section we present the ClowdFlows
and ConCreTeFlows platforms that host the two motivatio-
nal use cases, but other platforms, such as FloWr [4], Ra-
pid Miner [18], KNIME [2], ORANGE [8] are also worth
exploring as potential infrastructures for creative software
blending.
ClowdFlows [16] is a cloud-based web application2 for
composition, execution and sharing of interactive data
mining workflows. It has a web based user interface
for building workflows, runs in all major browsers and
requires no installation. It contains a large set of work-
flow components called widgets, which can be con-
nected in a specific meaningful order to create a work-
flow. ClowdFlows enables visual programming and
has a graphical user interface which consists of a wid-
get repository and a workflow canvas.
ConCreTeFlows [27] is a platform3 built on top of the
ClowdFlows infrastructure. It is specialized in com-
putational creativity tasks, including conceptual blen-
ding based on textual or visual input or text generation
tasks, such as poetry generation.
The specialization of ConCreTeFlows in computational
(and especially text-based) creativity, as well as a smal-
ler number of implemented widgets, makes it less appro-
priate for the implementation of the proposed system for
creative software blending, but it is appropriate to show-
case the creative blending process. On the other hand,
ClowdFlows is not specialized in a single specific rese-
arch field and contains widgets from the fields of text mi-
ning, machine learning and NLP, which makes it appro-
priate for the implementation of a creative software blen-
ding system since combining tools from different research
fields would most likely increase the chance of the sy-
stem to be H-creative. As a basis of automated software
composition, ClowdFlows already includes a—somewhat
loosely defined—ontology of its components (named wid-
gets), which should be enhanced and elaborated in further
work, to enable ClowdFlows to actually become a useful
infrastructure for software blending.
3 Motivational use cases
This section presents two hand-crafted motivational work-
flows, which illustrate the usefulness of blending software
from different scientific fields in order to develop new in-
novative scientific methods. In this sense, they represent
the type of workflows that a system for creative software
blending would be capable to produce.
3.1 Wordification use case: Blending data
mining and text mining in ClowdFlows
Propositionalization [15] is an approach to inductive lo-
gic programming (ILP) and relational data mining (RDM),
which offers a way to transform a relational database into a
propositional single-table format. Consequently, learning
with propositionalization techniques is divided into two
self-contained phases: (1) transformation of relational data
into a single-table format and (2) selecting and applying a
propositional learner to the transformed data set. As an ad-
vantage, propositionalization is not limited to specific data
mining tasks such as classification, which is usually the
case with ILP and RDM methods that directly induce pre-
dictive models from relational data. This section motivates
creative software blending by outlining the Wordification
workflow [22], implemented in ClowdFlows, which per-
forms propositionalization by combining data mining and
text mining techniques.
In the Wordification workflow, shown in Figure 1, given
a MySQL relational database as input, the user selects the
target table from the initial relational database, which will
later represent the main table in the Wordification compo-
nent of the workflow. The user is able to discretize each of
the tables using one of the available discretization techni-
ques. These discretized tables are used by the Wordifica-
tion widget, where the transformation from the relational
tables to a ‘corpus of documents’ is performed.
Several elements of blending data mining and text mi-
ning techniques are incorporated in the Wordification: i.e.
transforming attribute values into bags of word-like items,
using TF-IDF weighting of items, and the possibility of
using n-grams of items where n-gram construction is per-
formed by taking every combination of length n of items
from the set of all items corresponding to the given indivi-
dual. Nevertheless, the element of the workflow that most
clearly illustrates the software blending potential is the in-
clusion of a word cloud visualization (an approach deve-
loped in text mining research), together with decision tree
construction and visualization (an approach developed in
data mining research).
3.2 Conceptual blending use case:
computational creativity in
ConCreTeFlows
The elements of the conceptual blending theory [12], des-
cribed in more detail in Section 4, are an inspiration to
many algorithms and methodologies in the field of com-
putational creativity. In brief, according to this theory, two
different concepts for which we can define (find) a simila-
rity, can be blended into a new concept in the context of
knowledge that is necessary to represent and generalize the
two concepts.
2Available at http://clowdflows.org
3Available at http://concreteflows.ijs.si
80 Informatica 42 (2018) 77–84 M. Martinc et al.
Figure 1: Clowdflows Wordification workflow with additional analyses after the wordification process, available at http:
//clowdflows.org/workflow/1455/.
Figure 2: Workflow implementation of multimodal blending in ConCreTeFlows, available at:
http://concreteflows.ijs.si/workflow/137/.
Let us present a conceptual blending CC workflow [27],
implemented in the ConCreTeFlows platform by different
partners of the ConCreTe project. Its process components
are implemented either as internal functions, wrapped stan-
dalone programs or as Web services. The publicly available
workflow, presented in Figure 2, can be executed, changed
and extended with additional functionality.
The workflow presents conceptual blending by con-
structing conceptual graphs from textual input and repre-
senting the results (blends) as graphs, natural language des-
criptions and visual representations. Two textual inputs
are transformed into conceptual graphs by a series of wid-
gets: the Download web page for obtaining the Web page
source from a given URL (In the example, these are the
Wikipedia pages for two animals: hamster and zebra.), Boi-
lerplate removal and Text2Graph transforming the textual
content into conceptual graphs (output g). The outputs of
Text2Graph widgets enter Blender basic, which blends the
two graphs together and outputs a combined blended graph
(output bg). This one gets served to the Textifier widget,
which produces a textual description of the blend. Its out-
put is presented by a standard Display String widget. The
two main entities from Text2Graph widgets enter also the
Vismantic2 visual blending widget [28], which either chan-
ges the texture of one input space to the texture of the other
(see Figure 3a), or puts one in the usual surroundings of
the other. (Figure 3b). Its outcome is shown in an output
similar to the ones shown in Figure 3.
4 Towards design principles for
creative software blending
There are two major paradigms in artificial intelligence re-
search: problem solving and artefact generation [5]. While
the problem solving paradigm deals with a series of pro-
blems that needs to be solved, in the artefact generation pa-
radigm the task is to generate a series of valuable artefacts.
This study is more related to the latter and the artefacts of
our interest are functional workflows.
A creative software blending system should be able to
build new workflows composed of software components
from different fields, leading to novel ways of software
composition for computational purposes that were not ex-
pected in advance. Such blending of software would best
be implemented in an existing infrastructure for interdisci-
plinary scientific research with already implemented com-
ponents for specific and well defined tasks.
As shown in Section 2.2, much effort in the fields of
data mining and NLP has already been devoted to the de-
velopment of infrastructures that provide support for easier
and quicker experimentation. One of the biggest challen-
Towards Creative Software Blending. . . Informatica 42 (2018) 77–84 81
(a) (b)
Figure 3: Two outputs of the Vismantic2 widget for the example of blending the concepts of hamster and zebra: left is
a result of exchanging hamster’s texture with zebra’s and the right is an example of exchanging zebra’s with a hamster’s
common visual context.
ges in implementation and use of these infrastructures has
been the integration of different components into functio-
nal workflows. Combining different tools and technologies
in a common infrastructure is a difficult task because of
software incompatibility and inappropriately defined onto-
logies.
4.1 Related software blending research
To design an appropriate creative software blending system
one should consider three fields of study. First, one has to
reflect upon the concept of creativity and how to build soft-
ware that exhibits creative behavior (see the related rese-
arch in Section 2.1). Next, one has to be aware of strengths
and limitations of the existing infrastructures that could be
used as a platform for the implementation of our system
(see the related infrastructures in Section 2.2). Finally, one
has to become aware of potentially existing implemented
approaches for software blending, surveyed below.
While the FloWr framework [4] is conceptually very si-
milar to the two infrastructures described in Section 2.2, it
is currently the only one with a specifically defined aim
of being able to automatically optimize, alter and ulti-
mately generate novel workflows presented as flowcharts.
This automatic workflow generation via the combination
of code modules means that FloWr has the potential to in-
novate at the process level and the manifested long-term
goal is a software system that can write program code for
itself [4]. Although the platform currently does not sup-
port fully functioning software blending, some preliminary
experiments to automatically alter, optimize and generate
flowcharts have been conducted.
One of the FloWr experiments dealt with an automatic
construction of a system for producing poetic couplets from
scratch. In order to reduce the number of possible com-
binations of different workflow components, only a sub-
set of all the available components were manually selected
for blending in the experiment. Possible options for the
input parameters were manually reduced and the number
of components in the generated workflow was limited to 3
to 5. Despite these limitations, at the end there were still
over 261 million variable definition combinations. For this
reason the brute-force approach of testing all combinati-
ons was intractable, so a depth-first search for all possible
workflows was implemented in a way that just one node
combination and one parameter setting were randomly se-
lected from a set of allowed combinations. The compati-
bility of sequential components and some other restrictions
were taken into account, which reduced the number of pos-
sible workflow candidates. The algorithm was run 200 ti-
mes resulting in 200 workflows. A manual evaluation sho-
wed that 18.5% of workflows worked successfully and pro-
duced poetic couplets.The conducted experiment required
a lot of human intervention in order to be successful and
the evaluation of produced artefacts was done by humans.
Because of this we can question the creativity of the propo-
sed software blending approach since a software should —
at least in our opinion — have the capacity to evaluate its
own performance in order to be called creative.
While FloWr belongs to CC research, several attempts
have been made to develop support systems also in the field
of knowledge discovery. These systems are to some extent
related to our research, since they either support the users
workflow composition by recommending the new compo-
nents that could be attached to an existing workflow, or
by generating entire workflows according to user require-
ments.
Zakova et al. [26] proposed a semi-automatic system for
workflow generation that is based on a background know-
ledge ontology in which all workflow components are des-
cribed together with their inputs, outputs and pre-/post con-
ditions. The system uses a planning algorithm and returns
just one optimal workflow with the smallest number of pro-
cessing steps. Given that alternative workflows are not ge-
nerated, this is not in accordance with a desired system
for creative software blending. Complexity limitations are
another problem, which is common to all the systems that
use planning approaches for workflow generation.
The IDEA system by Bernstein et al. [1] is based on an
ontology of data mining components that guides the work-
flow composition and contains heuristics for the rankings
of different alternatives. The system does not enable fully
82 Informatica 42 (2018) 77–84 M. Martinc et al.
Figure 4: The conceptual blending network [11].
automatic workflow generation but was implemented as a
support system for the user who decides on the weights to
determine the trade-off between different performance cri-
teria (e.g., speed, accuracy, comprehensibility). IDEA is
limited to proposing fairly simple workflows.
Kietz et al. [14] proposed a KDD support system that
uses a data mining ontology. The ontology contains infor-
mation about the objects manipulated, the meta data, the
operators (i.e. components containing algorithms for spe-
cific tasks) and a description of the goal, which is a for-
malization of the user desired output. The system takes a
goal description as an input and returns a workflow toget-
her with all the evaluation and reporting needed to let the
user assess if it fulfills the user defined success criterion.
The system, implemented in the RapidMiner platform, is
not fully autonomous, as it was designed as a support sy-
stem for the user.
4.2 Design principles
As the above survey shows, no adequate solution for the au-
tonomous creative software blending currently exists. To
build such a system, first, an ontology with well-defined
rules and relations needs to be created, in order to ena-
ble combining software components in a meaningful way.
Next, a system for creative blending of software compo-
nents would be created, enabling automated combination
of components in functional workflows.
Computational creativity, which is still in early phrases
of its development [25], provides some methodological ap-
paratus and inspiration for designing the guiding princi-
ples for a creative software blending system. One of the
very productive fields of research in computational crea-
tivity is the conceptual blending (CB) theory [12], which
inspired many algorithms, methodologies and discussions
in the field (e.g., [24, 21, 17]).
CB is a basic mental operation that leads to new mea-
ning, global insight, and conceptual compressions useful
for memory and manipulation of otherwise diffuse ranges
of meaning [9]. A key element is the mental space, a partial
and temporary structure of knowledge built for the purpose
of local understanding and action [10].
To describe the CB process, the theory [12] makes use of
a network of four mental spaces (see Figure 4). In blending,
structure from two input mental spaces (Input I1, Input I2),
is projected to a new space, the blend. A partial mapping
between elements of input spaces—that are perceived as
similar or analogous in some respect—is performed. The
third mental space, called generic space, encapsulates the
conceptual structure shared by the input spaces, generali-
zing and possibly enriching them. This space provides gui-
dance to the next step, where elements from each of the in-
put spaces are selectively projected into the blend, i.e. the
new blended mental space. Emergent structure arises in the
blend that is not copied there directly from any input.
The conceptual blending model is not directly transfe-
rable from the human cognition to the blending of soft-
ware. However, the methodology, together with the opti-
mality principles [11] that optimize the blending process—
which were already addressed also in computational mo-
dels [20]—should be considered when implementing the
software blending algorithm and the workflow ontology.
For example, in software blending the two inputs would not
represent concepts but rather two workflows from two dif-
ferent scientific domains. The “generic space” could then
be adapted to software blending in order for the blending
system to find all the compatible widgets from two diffe-
rent input workflow domains. Finally, the blend would be a
newly produced workflow containing new emergent struc-
tures not copied from original workflows. The optimality
principles, such as the relevance principle (which dictates
that all elements in the blend should be relevant) and inte-
gration principle (which states that the final blend should
be perceived as an enclosed unit) should be kept in mind
when designing the ontology.
Another important aspect to be considered in the imple-
mentation of the system is its creative part. In order for the
system to be recognized as creative, its produced artefacts
should be novel and of good quality [23] and the human
interference in the production and evaluation of these ar-
tefacts should be minimal. Three criteria are proposed for
attributing creative autonomy to a system [25]:
Autonomous evaluation The system should be able to
evaluate new creations autonomously and possess its
own “opinion” on which creations are better than ot-
hers.
Autonomous change The system should be able to
change its evaluation function without explicit directi-
ons.
Non-Randomness (Aleatoricism) Random behavior is
not creative, so evaluation and change should not be
Towards Creative Software Blending. . . Informatica 42 (2018) 77–84 83
completely random, although some randomness can
be involved.
In order to satisfy these criteria and since most of the afo-
rementioned platforms contain a large set of manually built
workflows that could be used as a training set, we propose
a combination of an evolutionary algorithm and a classifi-
cation model induction. An evolutionary algorithm would
operate directly on representations of workflows and gene-
rate new workflow candidates, according to the constraints
defined by ontology rules. These constraints would enforce
a minimum quality for the produced workflows (correspon-
ding to the criterion of quality [23], which is, as explained
earlier, one of the guiding principles in the construction of
creative artefacts).
The initial population of the evolutionary algorithm
would consist of manually built workflows that would be
“blended” into new workflow candidates with the help of
mutation and crossover. The fitness function used for eva-
luating the fitness of the generated workflow candidates
would contain following elements:
A binary classification model trained on the features ex-
tracted from successful and unsuccessful workflows
would serve as an additional workflow quality check.
A similarity function for determining the similarity be-
tween a generated workflow candidate and existing
workflow would be used for evaluating the novelty of
the candidate.
In this way the system would be able to generate new—
possibly creative—workflows and even propose changes in
the existing rules for workflow generation, which would
make this system capable of transformational creativity ac-
cording to [3].
5 Conclusions
In this study we elaborate the initial design principles of a
system for automatic workflow generation that would be
capable of autonomous composition of novel workflows
from existing software components. We have presented
two workflows with human-designed blending, implemen-
ted in the ClowdFlows and ConCreTeFlows platforms for
online workflow composition. The first workflow clearly
illustrated the potential for the composition of computa-
tional creativity solutions. The second use case presents
several computational creativity software components that
were combined in a collaborative effort to implement an
interesting conceptual blending solution, resulting in con-
ceptual, visual and textual blends. The benefits of a uni-
fying workflow for blending are twofold: the user can get
blends of various kinds through the same user interface and
the components can affect one another to produce a more
coherent and orchestrated set of multimodal blending re-
sults. The presented prototype solution is fully operational
and serves as a proof of concept that such an approach to
multimodal conceptual blending is possible.
On the other hand, the sketched evolutionary algorithm
approach to blending workflows and workflow components
shows, that the theory of conceptual blending can be trans-
ferred to the problem of creative software blending. We
also demonstrated that the system will be capable of self
evaluation by using the empirical criteria of novelty and
quality in the fitness function.
In our future work we will first design an ontology capa-
ble of supporting the planned widget recommender system.
We also plan to integrate a larger number of widgets and
workflows in the presented platforms. Moreover, we will
undertake the challenging task of the implementation. We
realize that creation of software that can innovate at a pro-
cess level is a very demanding task and we can expect many
challenges during this phase. Anyhow, we do believe that
the effort will be fruitful and bring us closer to the long-
term goal of creating software that could write novel and
valuable code directly.
Acknowledgments
We acknowledge the support of the Slovenian Research
Agency through research programme Knowledge Techno-
logies (grant number P2-0103), and project ClowdFlows
Data and Text Analytics Marketplace on the Web (CF-
Web), which has received funding from the European Uni-
ons Horizon 2020 research and innovation programme un-
der grant agreement No 754549. We would like to thank
Pedro Martins and Amilcar Cardoso for numerous discus-
sions on the topic of conceptual blending.
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