PLASMA: Platform for Auxiliary Semantic Modeling Approaches
Alexander Paulus, Andreas Burgdorf, Lars Puleikis, Tristan Langer, Andr
e Pomp
and Tobias Meisen
Chair of Technologies and Management of Digital Transformation, University of Wuppertal, Wuppertal, Germany
Semantic Modeling, Semantic Refinement, Semantic Data Management, PLASMA.
In recent years, the impact and usability of semantic data management has increased continuously. Still, one
limiting factor is the need to create a semantic mapping in the form of a semantic model between data and
a used conceptualization. Creating this mapping manually is a time-consuming process, which especially
requires to know the used conceptualization in detail. Thus, the majority of recent approaches in this research
field focus on a fully automated semantic modeling approach, but reliable results cannot be achieved in all use
cases, i.e., a human must step in. The needed subsequent manual adjustment of automatically created models
has already been mentioned in previous works, but has, in our opinion, received too little attention so far. In this
paper, we treat the involvement of a human as an explicit phase of the semantic model creation process, called
semantic refinement. Semantic refinement comprises the manual improvement of semantic models generated
by automated approaches. In order to additionally enable dedicated research in this direction in the future,
we also present PLASMA, a platform to utilize existing and future modeling approaches in a consistent and
extendable environment. PLASMA aims to support the development of new semantic refinement approaches
by providing necessary supplementary functionalities.
Due to the emergence of large amounts of data and
its usage in machine learning processes, the manage-
ment of such data has become increasingly impor-
tant in recent years. Although access to data is often
possible, finding and understanding the needed data
sources to extract information can be a challenging
task without meta information associated to the raw
data sets. Semantic data management in the form
of Ontology-based Data Management (OBDM) has
already laid the foundation for managing heteroge-
neous data sources more efficiently. Advances in this
field of research can be observed in (Linked) Open
Data and the introduction of Enterprise Knowledge
Graphs(Galkin et al., 2016).
There are two important processes that ensure the
quality and acceptance of semantic data management:
the creation and maintenance of conceptualizations as
well as the description of data sources based on such
conceptualizations in form of semantic models. A
semantically annotated data source can be found us-
ing the conceptual representation of the data and pro-
cessed using the provided context information stored
in the model or the conceptualization. In recent years,
research has been focused on reducing the effort that
arises when it comes to defining those conceptual-
izations and the semantic models (cf. Section 2).
However, there are no approaches that generally yield
flawless results, often requiring a user to supervise
the creation and apply modifications. Those modi-
fications can be corrections as well as enhancements
of the semantic models. Although mentioned in pre-
vious publications such as (Taheriyan et al., 2016), in
our opinion, this phase of the modeling process has
not yet received sufficient attention.
This phase, which is referred to simply as refine-
ment by (Taheriyan et al., 2016), follows the auto-
mated semantic modeling. It allows the user to im-
prove an initial version proposed by an automatic
modeling approach by manually correcting or en-
hancing the state of the model. However, a clear defi-
nition of this phase has not yet been proposed, which
we attempt in this contribution.
Furthermore, to support the development of al-
gorithms for the refinement phase, we also pro-
pose PLASMA, a modular platform to integrate ap-
proaches for the entire process of creating a seman-
tic model, including the refinement, but also labeling
and modeling (cf. Section 2). Using PLASMA, users
are enabled to test approaches in a pre-configured en-
vironment. For that, PLASMA allows users to ex-
Paulus, A., Burgdorf, A., Puleikis, L., Langer, T., Pomp, A. and Meisen, T.
PLASMA: Platform for Auxiliary Semantic Modeling Approaches.
DOI: 10.5220/0010499604030412
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 403-412
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
change single components of the semantic model cre-
ation process in order to test and evaluate new or even
already existing approaches in the same system.
Our contributions in this paper are as follows:
First, we introduce the semantic refinement as a phase
of the semantic model creation process and state the
characteristics of this phase. Second, we present the
concept of auxiliary approaches and how those can be
utilized to build a comprehensive semantic modeling
environment. Third, we release PLASMA to serve as
a backbone for future semantic modeling that includes
a refinement UI.
In the further course of the paper, we discuss re-
lated work in Section 2, then we present our concept
of semantic refinement as its own phase in Section 3.
Afterwards, we introduce PLASMA in Section 4, fol-
lowed by current and future usage scenarios of the
platform in Section 5. We conclude and give an out-
look in Section 6.
In the past years, several approaches for performing
the creation of semantic models have emerged. The
process of creating a semantic model has been divided
into two phases so far. A basic annotation of a data
source can be achieved by performing a semantic la-
beling step, followed by the semantic modeling step,
which aims to relate initially mapped concepts and
equip the annotation with additional context (cf. (Vu
et al., 2019)).
Semantic labeling, which is the initial mapping
from labels (e.g., table headers or leafs in a struc-
tured data set) to a conceptualization (e.g., ontology),
is mainly performed by following either a label-driven
or a data-driven strategy. Examples for label-driven
mappings have been proposed by (Polfliet and Ichise,
2010), (Pinkel et al., 2017), (Paulus et al., 2018) or
(Papapanagiotou et al., 2006). Next to those, there
also exist data-driven mapping approaches that rely
mainly on using the data values of a data set. For
instance, (Syed et al., 2010) as well as (Wang et al.,
2012) proposed approaches that provide labels using
the data values within a data set in conjunction with
external (knowledge) databases to identify the most
reasonable concept. (Ramnandan et al., 2015),(Ab-
delmageed and Schindler, 2020) use algorithmic ap-
proaches whereas (Pham et al., 2016), (R
et al., 2018), (Chen et al., 2019), (Hulsebos et al.,
2019) as well as (Takeoka et al., 2019) and (Pomp
et al., 2020) use machine learning approaches to solve
the task.
All previously mentioned semantic labeling ap-
proaches rely on a predefined conceptualization, al-
though approaches like (Pham et al., 2016), (Pomp
et al., 2020) or (Jim
enez-Ruiz et al., 2015) are able to
create concepts during the mapping process by inte-
grating the user into the labeling process. Using such
approaches in platforms, like Optique (Giese et al.,
2015), it becomes possible to redefine the found map-
pings using an R2RML editor (Sengupta et al., 2013).
Following the semantic labeling phase, the initial
mappings can be extended by additional meaningful
concepts as well as selecting the most suitable re-
lationships that hold between the different concepts,
resulting in a semantic model for that data set. Ap-
proaches presented by (Knoblock et al., 2012) and
(Taheriyan et al., 2013; ?; ?) as well as (U
na et al.,
2018) have shown significant improvements in this
area during the last years. Most recent approaches
like (Vu et al., 2019) and (Futia et al., 2020) show the
ongoing research interest in the topic. However, none
of the approaches does solve the task perfectly, due to
ambiguities in the data sets or the encounter of unseen
cases in the training data.
To finalize the model and correct potential errors,
human interaction is needed. However there are only
few approaches that feature tools to inspect and mod-
ify a semantic model after the automated generation.
A first approach to support semantic model modifica-
tion was Karma, presented by (Knoblock et al., 2012)
and (Gupta et al., 2012). Karma is described as a
data integration tool that enables users to feed in data
and create a semantic model for it. The used auto-
mated semantic modeling approach is based on the
method developed by Taheriyan et al. Karma has
been used in various projects (cf. (Szekely et al.,
2015)). A similar approach to Karma is followed by
ESKAPE(Pomp et al., 2017), a semantic data plat-
form for enterprises, handling the full data manage-
ment process from ingestion to extraction. However,
ESKAPE only performs basic semantic labeling au-
tomatically - the semantic modeling has to be done
manually by the users. Furthermore, there exist mul-
tiple commercial tools like PoolParty
or Grafo
focus on creating knowledge graphs but are not avail-
able for free use and furthermore are not extendable
to support own algorithms.
Both Karma and ESKAPE are capable of doing a
data schema analysis, analyzing live input data to ex-
tract labels and structure to perform the labeling step
on. Both also support multiple data formats during
this phase as well as provide a GUI (cf. (Szekely
et al., 2015) and (Pomp et al., 2018)), which allows
users to modify the semantic model. However, al-
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 1: Overview of the phases during the creation of a
semantic model with the semantic refinement phase follow-
ing the (automated) semantic modeling.
though following the human in the loop approach,
both systems do not actively support the user during
the modification phase.
From the observed state of the art, the main pro-
cess of creating a semantic model is depicted in Fig-
ure 1. We identify two main phases, automated se-
mantic labeling and automated semantic modeling
that have received significant research during the past
years, and the subsequent refinement phase featuring
the interactive modeling. Also, as those stages are
provided by modeling systems like Karma, we add a
schema analysis phase that builds the initial syntactic
model from a data set and a final storage phase that
persists the complete semantic model, e.g., by inte-
grating it into a knowledge graph or semantic storage.
While there have been major advances in the field of
semantic labeling and modeling, the subsequent re-
finement has been mentioned but not actively sup-
ported. However, systems like Karma and ESKAPE
provide the ability to improve the quality of gener-
ated models as a fourth phase following the auto-
mated semantic modeling. We refer to this phase as
Semantic Refinement, an interactive and iterative pro-
cess of improving an initial version proposed by an
automatic modeling approach. The terminology is
not new, (Paulheim, 2016) used the ”refinement” term
to describe the automated improvement of knowledge
graphs, whereas (Futia et al., 2020) use it to describe
the final automated validation of their semantic model
computation. However, in both cases this is an auto-
mated procedure and which, in the first case is dis-
connected from the semantic model creation process
and in the latter case we associate it as a part of the
semantic modeling phase. Also, the refinement of a
semantic model has been mentioned in previous pub-
lications (e.g., (Szekely et al., 2013), (Taheriyan et al.,
2015) and (Taheriyan et al., 2016)), but was often not
in the focus and being referred to as a necessity than
a dedicated phase of the semantic model creation pro-
cess. However, it is unclear if the authors referred to
an automated step like proposed by (Futia et al., 2020)
or a fully user-based adjustment as (Pomp et al., 2017)
We see the main characteristic of the dedicated se-
mantic refinement phase as the manual use of a tool to
improve the quality of a semantic model. The user is
kept involved by inspecting the result after each mod-
ification. Possible user operations in this phase are
adding new concepts or relations from the conceptu-
alization, deleting or replacing existing elements or
modifying single elements if permitted by the system
(e.g., changing properties). Even extending the con-
ceptualization (c.f. (Pomp et al., 2019)) is a valid op-
eration if the system used follows the open world as-
sumption and lets the user add own facts to the models
which are not yet present in ontologies.
In our opinion, an essential part of the semantic re-
finement phase is a continuous support by the system,
aiding the user on how to improve the model by taking
the manual load of checking, validating, correcting
and extending the contents, e.g., by solving discrep-
ancies identified during the modeling phase. There-
fore, we propose that algorithms are used repeatedly
after each user-created modification. Such algorithms
use the current state of the semantic model as input
to evaluate and recommend one or multiple possible
changes to the semantic model. For the output, we
propose that approaches yield suggestions on how to
incrementally improve the semantic model by provid-
ing single relations or concepts as well as subgraphs
to be added. For each iteration, the user can then also
decide to accept or reject those changes, possibly up-
dating the model and triggering a new recommenda-
tion iteration. Of course, the user can also ignore the
recommendations and apply own further changes.
Examples for refinements span selection or ex-
change of ambiguous or mutually exclusive concepts,
e.g.. for specifying units of measure, as well as adding
new concepts that were either not included in the au-
tomatically generated model for various reasons, most
commonly due to being unseen in the past. Also,
refinements could include adding new and more ap-
propriate concepts that were unavailable to previous
automated steps, e.g., by using external data sources.
The result should resemble the way the user interprets
the data set as close as possible.
With the new process model in mind, a platform is
needed which supports and focuses manual semantic
PLASMA: Platform for Auxiliary Semantic Modeling Approaches
refinement alongside all other phases. We designed
the PLatform for Auxiliary Semantic Modeling Ap-
proaches (PLASMA) as a platform for scientific re-
search that supports the whole semantic model cre-
ation process and aim to make it publicly available
for scientific purposes
With the release of PLASMA as a new platform,
we aim to build a baseline system for semantic model
creation, while reducing the effort for the initial setup
and maintenance to a minimum. The main driving
factor is the ability to support all five phases of the se-
mantic model creation process (Figure 1) in one plat-
form. This especially includes the semantic refine-
ment phase introduced in Section 3. We also chose a
modular approach to be able to exchange single com-
ponents and connect new ones using a defined inter-
face, while changing as little of the surrounding sys-
tem as possible. This would also apply when connect-
ing existing systems like Serene (U
na et al., 2018) or
SeMi (Futia et al., 2020).
This approach is inspired by (U
na et al., 2018),
which connected Karma to their system to achieve
comparability, as well as the Serene Benchmark
ummele et al., 2018), which focuses on integrat-
ing different fully automated semantic labeling ap-
proaches. PLASMA also allows the utilization of
those approaches but encapsules them in a modu-
lar way making them exchangeable. Furthermore,
instead of solely focusing on the semantic labeling
phase, PLASMA provides a complete modeling envi-
ronment, including the semantic modeling and refine-
ment phase. PLASMA also provides common func-
tionalities like schema analysis and a graphical mod-
eling UI to not require each developer to implement
those each time they design a new approach. Al-
though being provided by the system, those compo-
nents are also to be realized as modules, making them
exchangeable if required.
4.1 Components
In the following, we present the single components of
the architecture separately and outline dependencies
and requirements. Also, a detailed feature overview
is given for each service. Figure 2 gives a schematic
overview of the components and their interactions and
4.1.1 Data Model
The data central model of PLASMA is called a Com-
bined Model. It holds the syntactic model as well
as the current state of semantic model that is cre-
ated. We define the syntactic model as M
(nodes, edges, root) where nodes are general nodes,
representing an object or a collection, or leaf nodes
representing data labels and their values. The root
node is stored in root. The set edges contains all
the edges that connect the nodes to represent the
structure of the data source so that especially nested
structures are preserved. Given a semantic concep-
tualization, like an ontology O = (C, R), where C
denotes available concepts and R the relations be-
tween them, a semantic model is defined as M
, R
) C
C, R
R. The combined model M
, M
, L) then denotes a fusion of the syntac-
tic and semantic model. It contains an additional
mapping L : C
nodes mapping single concepts
from the semantic model to attributes in
the syntax model M
. As L is a injective mapping,
it must hold that @ c
, c
where L(c
) = L(c
(only one concept is assigned to each syntax node).
Furthermore, the combined model defines a for-
mat for modifications to the model. A modifica-
tion for a combined model M
= (M
, M
, L) is
defined as = (C
, A, R
, ω), where C
C and
R denote the sets of modified (added or ad-
justed or deleted) concepts and and relations, respec-
tively. The set A denotes the anchor points, that is
A = {a | a C
(a C
, where C
nodes, L(a) = n)}. This requires the contained sub-
graph to be connected to the existing M
or to con-
tain at least one concept a C
being mapped to an
attribute. Those modifications can either be applied
directly to M
or stored for the user to chose which
ones to apply. In this case, we refer to them as Rec-
ommendations and require A 6=
0, not allowing dis-
connected subgraphs and indicate an (optional) confi-
dence score with ω [0, 1], indicating on how certain
the issuer of the modifications is that this will indeed
improve the state of the model.
4.1.2 Data Source Service (DSS)
Before the user can start modeling, a data source has
to be created using the DSS. A data source is the basic
reference unit of PLASMA. We define a data source
as a tuple DS = (name, note, desc, data), where name
is the unique name of that source, note is a short de-
scription of the data source, desc is a textual descrip-
tion of the data and data is the later provided sam-
ple input data, being empty on creation. The desc is
an extended textual description of the contained data
that can be provided to supply meta information for
labeling and modeling approaches.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
User Interface
Schema Analysis
Data Modeling
Knowledge Graph
Data Source
data exchange optional / partial data exchange
Figure 2: Architectural overview of the PLASMAs components. All connections indicate communicating components.
4.1.3 Schema Analysis Service (SAS)
Once a data source has been created, we begin the
semantic model creation process with phase 1, the
schema analysis. Sample data points need to be in-
serted for a data source to analyze them and extract
the structure and labels. Thus, the SAS takes a data
source DS
and a set of sample data points SDP
, P
, ..., P
to analyze the data. After the analysis,
the SAS identifies the data structure and provides a
syntactic model M
4.1.4 Auxiliary Recommendation Services
The Auxiliary Recommendation Services (ARS) are
services that provide modeling recommendations dur-
ing different stages of the semantic model creation
process. Each ARS encapsulates an algorithm to per-
form a specific task in either the labeling (phase 2),
modeling (phase 3) or refinement phase (phase 4), be-
ing denoted ARS for Labeling (ARS-L), ARS for Mod-
eling (ARS-M) and ARS for Refinement (ARS-R), re-
spectively, based on their type. An ARS receives the
current state of the combined model and optionally
some meta information and will return a combined
model which has been modified or supplemented with
some recommendations
, ..,
for the user to chose
from. The services are using a standardized interface
to be quickly exchangeable and allow PLASMA to
support multiple approaches on how to solve the task
for each phase.
The ARS are modular services, thus posing as lit-
tle limitations as possible to developers on what tech-
nology stack to use when implementing their solu-
tions. As long as an ARS satisfies the given interface
provided by the platform, it can be connected. Using
a decoupled architecture, new approaches can be de-
veloped and tested on their own before being attached
to PLASMA. If the approach to be connected by the
ARS itself is an already implemented and running
system with a predefined API, the ARS can also func-
tion as a proxy to it, e.g., by using an offered interface
and converting communication to match PLASMAs
data format (see Section 4.1.1). A single service
can be registered to perform operations for different
phases, e.g., by providing multiple interfaces. The
three types of ARS are:
ARS for Labeling (ARS-L): receive a data source
and the syntactic model M
and provide an
initial combined model by performing the seman-
tic labeling, returning a combined model M
labeled leaf nodes.
ARS for Modeling (ARS-M): receive DS
and M
and perform the semantic labeling. Return M
cluding the initial semantic model or multiple pos-
sible models represented as recommendations.
ARS for Refinement (ARS-R): receives DS
and computes modifications that might help
improve the state of the model (cf. Section 3).
How this is achieved and which modifications are
deemed fitting is up to the contained algorithm.
Return M
and a number of optional modifica-
, ..,
as recommendations. ARS-R do
not alter M
or M
directly. Also, ARS-R
are expected to be invoked multiple times and
thus may keep an internal state for the specific
data source, e.g., to improve recommendations in
follow-up executions.
PLASMA: Platform for Auxiliary Semantic Modeling Approaches
4.1.5 Semantic Recommendation Service (SRS)
This service coordinates the ARS that are connected
to PLASMA to perform their tasks during phases 2-
4 of the semantic model creation process. There-
fore, the SRS provides three different endpoints to
request modifications for phase 2-4 of the semantic
model creation process. All ARS of one type use the
same interface. Using standardized interfaces allow
the SRS to contact previously unknown services via
auto discovery, a feature that will be operational in the
near future. In the current state, the SRS is configured
which of the available ARS to use. Once development
of a new ARS has finished, the ARS can be integrated
into the architecture and the SRS adjusted to use the
new ARS for further operations.
4.1.6 Data Modeling Service (DMS)
This service keeps the current state of all combined
models M
in its database and updates them once
modifications are issued. Whenever an actor modi-
fies the current state of the combined model, those
changes are processed and validated using the DMS.
These modifications, which are done by applying a
modification to a combined model M
, can either be
made by the user using the UI or originate from (auto-
mated) recommendation systems, like ARS. The only
restriction is that there always has to be one defined
state of combined model M
for each data source DS
in the DMS to be provided to the UI.
4.1.7 Knowledge Graph Service (KGS)
This service is the platform’s main source for seman-
tic information. It maintains the conceptualization
that is used for generating mappings as well as the
mappings itself in the form of semantic models ex-
tracted from the combined model. We refer to those
types as conceptualization layer and mapping layer.
To represent these two layers, we use an upper on-
tology as introduced by Pomp (Pomp, 2020). This
ontology allows to combine the conceptualization and
the mapping layer by representing them in a single
knowledge graph. By utilizing a knowledge graph,
we achieve direct linkage between the conceptualiza-
tion and the mapping layer, allowing algorithms to ef-
ficiently operate on the structure.
The conceptualization layer can be initialized by
importing already existing ontologies in RDF Turtle
syntax to form the target conceptualization for the se-
mantic models. During the import, RDF triples are
converted into Concepts and Relations as defined by
the upper ontology while keeping provenance should
those later be exported again. Both the conceptual-
ization as well as semantic models can be exported as
RDF in Turtle syntax again. Semantic models can be
created, deleted and searched.
4.2 User Interface
Figure 3: Partial screenshot of the refinement UI of
PLASMA during the modeling process with an unfinished
semantic model. Highlighted node is a recommendation.
To allow the user to inspect the processing that has
taken place, we provide a web-based user interface
(UI) that allows to operate the basic functionalities of
PLASMA. In the following, we give a brief overview
of the UI, including the available views and function-
alities. PLASMAs UI consists of multiple views, one
for the creation of data sources, one for the submis-
sion of sample data for an existing data source and a
refinement UI that enables the user to perform man-
ual actions during the semantic refinement phase. The
realized modeling concept for the refinement UI fol-
lows the approach presented by (Pomp et al., 2018),
but has received a major overhaul. Figure 3 shows the
refinement UI displaying the syntactic model and an
unfinished semantic model. Here, the user can inspect
recommendations issued by the system and edit the
current state of the semantic model. The refinement
UI aims to provide the user with a convenient model-
ing environment, hiding technical details for beginner
users. The syntactic model is shown as nodes with
dashed lines and can be modified by the user, i.e.,
nodes can be rearranged. For hierarchical data, the UI
also displays the structure. Based on the environment
the refinement UI is used (cf. Section 5), even more
sophisticated operations, like syntactic node splitting
or assigning data types, can be realized. However, as
this requires modifications to the original data, fur-
ther data processing components are needed that are
not part of PLASMA itself.
The semantic model M
consists of nodes that
are comprised of two parts. A concept part (upper
half), realizing an assigned concept from the con-
ceptualization layer and a mapping part (lower half)
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
representing the mapping layer component (cf. Sec-
tion 4.1.7). Each mapping part serves as a projec-
tion of a concept (denoted in the concept part) in
a semantic model. While concept parts cannot be
edited or adjusted, the mapping part is customizable,
e.g., can be renamed in order to resolve ambiguities
within the model, while still being bound to a con-
cept which ensures universal understandability. Re-
lations are taken directly from the conceptualization
layer and can therefore not be modified. Available
elements can be found using the left side drawer of-
fering a search functionality for existing concepts and
relations. Concepts as well as relations are then added
using drag and drop mechanisms. When a concept is
attached to a leaf node of the syntactic model, a map-
ping is created between the semantic model and the
data set in L
Following each modification, the DMS requests
an update on the recommendations from the SRS us-
ing the now modified model as input. This may result
in recommendations being removed as they no longer
apply, or new ones being generated. Recommenda-
tions are displayed when hovering one in the rec-
ommendations tab and applied by double-clicking on
them, providing a convenient way to extend or modify
the semantic model. However, as we are experiment-
ing with different visualization techniques, this might
change in the future. Figure 3 shows recommenda-
tions as highlighted nodes, in this instance a possi-
ble generalization of an airport as a transportation
hub to indicate it as an organizational unit instead of,
for example, the area. There might be more recom-
mendations available, but are hidden due to a general
threshold for certainty of ω 0.5, resulting in recom-
mendations with lower certainty to be hidden.
4.3 Semantic Model Creation Process in
In this section, we briefly describe the main phases
of the semantic model creation process (Figure 1) and
how those are executed in PLASMA. Figure 4 gives
an overview of the main operations and their order.
For an existing data source DS, data can be added to
be analyzed. The system currently processes JSON
typed example data values as JSON can be used to de-
scribe both structured and flat data sources. The data
is then transmitted to the SAS where it is analyzed
. Once the syntactic model M
has been created
(phase 1), the result is transmitted to the DMS where
an empty semantic model M
is attached to it
The model is then handed to the SRS for the initial
labeling and modeling steps
. To achieve this, the
SRS requests an automated semantic labeling (phase
2) from an ARS-L. The result is an initial labeling of
the leaf nodes of the syntactic model in the returned
combined model M
. This is followed by request-
ing an automated semantic modeling (phase 3) to ex-
tend M
past the labeled concepts. The ARS-M per-
forms this operation and returns the M
to the SRS
. This model is then returned to the DMS
shown in the User Interface
. Once the user per-
forms a manual action, the updated M
is handed over
to the DMS
which then requests a recommendation
for the current M
from the SRS
, using the refine-
ment endpoint. The SRS uses instances of ARS-R to
acquire one or more recommendations
and returns
those to the DMS
where M
is adjusted and the
UI updated
, giving the user the ability to inspect
and optionally accept those recommendations. Steps
are repeatable and form the Seman-
tic Refinement Process (phase 4). Once the user has
finished editing, the finalized semantic model M
is sent to the KGS for storage (phase 5), ending the
refinement process and bringing the semantic model
creation of the given data source to an end
PLASMA was designed to be a baseline support tool
for upcoming projects that aim to improve the se-
mantic model creation process. Future approaches
can be developed and tested in a controlled environ-
ment, where the auxiliary services architecture sup-
ports a plug and play approach to setup and test new
approaches after PLASMA is set up. PLASMA is
currently in use in the Bergisch.Smart.Mobility
search project, which features a data marketplace for
smart city open data, providing the semantic model
creation functionalities for the semantic annotation of
data sources. Within the ongoing project, PLASMA
is integrated alongside the semantic data ingestion en-
gine and provides an infrastructure that enables mu-
nicipal employees to create semantic models to de-
scribe their data sets. The created semantic models
are then used to integrate the data and provide it to
data consumers. Figure 5 shows how PLASMA is po-
sitioned in the general workflow of the marketplace.
During the project runtime, PLASMAs components
are adjusted to improve the usability of semantic tech-
nologies for municipal workers. There are also multi-
ple ARS being tested to improve the overall automatic
modeling performance of the platform, for example
one focusing on frequently used geospatial concepts.
PLASMA: Platform for Auxiliary Semantic Modeling Approaches
Auxiliary Recommendation Services
Schema Analysis
Data Modeling
User Interface
Knowledge Graph
syntactic model combined (syntactic + semantic) model semantic recommendation(s)semantic model
input data
Figure 4: The semantic model creation process.
Upload of input data to the SAS.
Submission of the resulting syn-
tactical model to the DMS.
Request for semantic recommendations based on the current state of the combined model.
Requesting of semantic labeling using separated Auxiliary Recommendation Service (ARS).
Requesting of semantic
modeling using separated ARS.
Provision of initial model to the DMS.
Transmission of current model to GUI.
manually updates model.
Requesting of semantic recommendation using separated ARS.
Provision of one or more
suggestions to the DMS.
Submission of final semantic model to the KGS for storage or export.
5.1 Future Usage Scenarios
In the project, PLASMA is evolved to support the
users when creating semantic models. However, aside
from those advancements, estimating the quality of
different approaches is also in the focus of the plat-
form. For example, to compare two different seman-
tic modeling approaches, after providing the target
ontology and connecting any ARS-L for the labeling
phase, approach A, realized as an ARS-M, would be
connected to the SRS. The model is created and after-
wards the service exchanged to another ARS-M con-
taining approach B. After another run, two results are
available for export and evaluation. By exchanging
only one component during runs, comparable results
can be reached as the remaining environment remains
With the UI including the recommendation in-
spection and selection, PLASMA also aims to pro-
vide a platform to advance research towards user sup-
port during the semantic refinement phase. A specific
aim of PLASMA is to enhance the visualization and
selection of recommendations to actively support the
refinement process. Therefore, new approaches can
be developed utilizing PLASMA as a platform.
In this paper, we introduced the interactive manual
refinement of a semantic model as an explicit phase
of the semantic model creation process. It covers an
essential part of the creation of semantic models us-
ing a human in the loop approach to manually cor-
rect and improve results generated by automated ap-
proaches. We also presented PLASMA, an extend-
able platform aimed to support the development and
integration of semantic labeling/modeling/refinement
approaches using the concept of auxiliary modeling
services. The platform is designed to function as a
tool to develop new approaches for the semantic re-
finement process. PLASMA is build in a modular pat-
tern to (i) allow modifications or replacements of sin-
gle components, to (ii) integrate new approaches and
evaluate them using a common framework and to (iii)
inspect results using a web based UI aimed to support
future semantic model refinement approaches.
In the future, we plan to provide a set of exist-
ing modeling algorithms for PLASMA by implement-
ing current approaches in dedicated ARS. The SRS
is planned to support multiple similar approaches,
e.g., for semantic labeling, and let the user decide
which algorithm to use for the current run. This
would greatly reduce the manual configuration cur-
rently needed and also allow users to evaluate and
compare different approaches without the need to
modify the technical architecture during runs. In ad-
dition, we plan to support batch operations, allowing
to analyze or model multiple data sources automat-
ically, optionally using different ARS instances for
each run. This way, we hope to eventually utilize
PLASMA as an evaluation framework for different
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Marketplace UI
Data Storage
Semantic Data
Integration Engine
Schema Analysis
Data Modeling
Modeling Interface
Knowledge Graph
syntactic model combined (syntactic + semantic) modelsemantic model
input data
data source
sample data
integrated data
processed data
integrated data
PLASMA component
Semantic Data
Access Engine
Figure 5: Ingestion and retrival process for a single data source with PLASMA integrated into a data marketplace. The SRS
is omitted for brevity.
Upload of sample data to the SAS.
Submission of the resulting syntactical model to the DMS.
Creation / refinement of the semantic model for the data source.
Submission of final semantic model to the KGS.
Integration of the raw input data using the created semantic model.
Storage of semantically annotated integrated data in the
data storage.
User(s) search data space based on semantic models.
Raw data or integrated data is requested, processed
and transferred to the user.
automated modeling approaches. In this context, to
even save the need to export and evaluate models, a
integrated benchmarking functionality is envisioned,
but needs further specification.
Abdelmageed, N. and Schindler, S. (2020). JenTab: Match-
ing Tabular Data to Knowledge Graphs. The 19th In-
ternational Semantic Web Conference.
Chen, J., Jimenez-Ruiz, E., et al. (8/10/2019 - 8/16/2019).
Learning Semantic Annotations for Tabular Data. In
Proceedings of the Twenty-Eighth International Joint
Conference on Artificial Intelligence.
Futia, G., Vetr
o, A., and de Martin, J. C. (2020). SeMi:
A SEmantic Modeling machIne to build Knowledge
Graphs with graph neural networks. SoftwareX,
Galkin, M., Auer, S., and Scerri, S. (2016). Enterprise
Knowledge Graphs: A Backbone of Linked Enterprise
Data. In 2016 IEEE/WIC/ACM International Confer-
ence on Web Intelligence (WI).
Giese, M., Soylu, A., Vega-Gorgojo, G., Waaler, A., Haase,
P., Jimenez-Ruiz, E., Lanti, D., Rezk, M., Xiao, G.,
Ozcep, O., and Rosati, R. (2015). Optique: Zooming
in on Big Data.
Gupta, S., Szekely, P., et al. (2012). Karma: A System for
Mapping Structured Sources into the Semantic Web.
In Extended Semantic Web Conference.
Hulsebos, M., Hu, K., et al. (2019). Sherlock. In Proceed-
ings of the 25th ACM SIGKDD International Confer-
ence on Knowledge Discovery & Data Mining.
enez-Ruiz, E., Kharlamov, E., et al. (2015). BootOX:
Bootstrapping OWL 2 Ontologies and R2RML Map-
pings from Relational Databases. In International Se-
mantic Web Conference (Posters & Demos).
Knoblock, C. A., Szekely, P., et al. (2012). Semi-
Automatically Mapping Structured Sources Into the
Semantic Web. In Extended Semantic Web Confer-
ence, pages 375–390.
Papapanagiotou, P., Katsiouli, P., et al. (2006). RONTO:
Relational to Ontology Schema Matching. AIS
Sigsemis Bulletin, 3(3-4):32–36.
Paulheim, H. (2016). Knowledge graph refinement: A sur-
vey of approaches and evaluation methods. Semantic
Web, 8(3):489–508.
Paulus, A., Pomp, A., et al. (2018). Gathering and Com-
bining Semantic Concepts from Multiple Knowledge
Bases. In ICEIS 2018, pages 69–80, Set
ubal, Portugal.
Pham, M., Alse, S., et al. (2016). Semantic Labeling: A
Domain-Independent Approach. In The Semantic Web
ISWC 2016, pages 446–462, Cham. Springer Inter-
national Publishing.
Pinkel, C., Binnig, C., et al. (2017). IncMap: a Journey
Towards Ontology-based Data Integration. Daten-
banksysteme f
ur Business, Technologie und Web (BTW
Polfliet, S. and Ichise, R. (2010). Automated Mapping Gen-
eration for Converting Databases into Linked Data. In
Proceedings of the 2010 International Conference on
Posters & Demonstrations Track.
Pomp, A. (2020). Bottom-up Knowledge Graph-based
Data Management. Berichte aus dem Maschinenbau.
Pomp, A., Kraus, V., et al. (2020). Semantic Concept Rec-
ommendation for Continuously Evolving Knowledge
PLASMA: Platform for Auxiliary Semantic Modeling Approaches
Graphs. In Enterprise Information Systems, Lecture
Notes in Business Information Processing. Springer.
Pomp, A., Lipp, J., and Meisen, T. (2019). You are Miss-
ing a Concept! Enhancing Ontology-based Data Ac-
cess with Evolving Ontologies. In Proceedings, 13th
IEEE International Conference on Semantic Comput-
ing, pages 98–105.
Pomp, A., Paulus, A., et al. (2018). A Web-based UI to
Enable Semantic Modeling for Everyone. Procedia
Computer Science, 137:249–254.
Pomp, A., Paulus, A., Jeschke, S., and Meisen, T. (2017).
ESKAPE: Platform for Enabling Semantics in the
Continuously Evolving Internet of Things.
Ramnandan, S. K., Mittal, A., et al. (2015). Assigning Se-
mantic Labels to Data Sources. In The Semantic Web.
Latest Advances and New Domains, pages 403–417,
Cham. Springer International Publishing.
ummele, N., Tyshetskiy, Y., and Collins, A. (2018). Eval-
uating Approaches for Supervised Semantic Labeling.
CoRR, abs/1801.09788.
Sengupta, K., Haase, P., Schmidt, M., and Hitzler, P. (2013).
Editing r2rml mappings made easy. In Proceedings
of the 12th International Semantic Web Conference,
page 101–104.
Syed, Z., Finin, T., et al. (2010). Exploiting a Web of Se-
mantic Data for Interpreting Tables. In Proceedings of
the Second Web Science Conference.
Szekely, P., Knoblock, C. A., et al. (2013). Connecting
the Smithsonian American Art Museum to the Linked
Data Cloud. In The Semantic Web: Semantics and Big
Data, pages 593–607, Berlin, Heidelberg. Springer
Berlin Heidelberg.
Szekely, P., Knoblock, C. A., et al. (2015). Building and
Using a Knowledge Graph to Combat Human Traf-
ficking. In The Semantic Web - ISWC 2015, volume
9367, pages 205–221. Springer, Cham.
Taheriyan, M., Knoblock, C. A., et al. (2013). A Graph-
Based Approach to Learn Semantic Descriptions of
Data Sources. In The Semantic Web ISWC 2013,
pages 607–623, Berlin, Heidelberg. Springer Berlin
Taheriyan, M., Knoblock, C. A., et al. (2015). Leveraging
Linked Data to Infer Semantic Relations within Struc-
tured Sources. In Proceedings of the 6th International
Workshop on Consuming Linked Data (COLD 2015).
Taheriyan, M., Knoblock, C. A., et al. (2016). Learning the
Semantics of Structured Data Sources. Journal of Web
Semantics, 37-38:152–169.
Takeoka, K., Oyamada, M., et al. (2019). Meimei: An Effi-
cient Probabilistic Approach for Semantically Anno-
tating Tables. Proceedings of the AAAI Conference on
Artificial Intelligence, 33(01):281–288.
na, D. D., R
ummele, N., et al. (2018). Machine Learn-
ing and Constraint Programming for Relational-To-
Ontology Schema Mapping. In Proceedings of the
Twenty-Seventh International Joint Conference on Ar-
tificial Intelligence.
Vu, B., Knoblock, C., and Pujara, J. (2019). Learning Se-
mantic Models of Data Sources Using Probabilistic
Graphical Models. In The World Wide Web Confer-
ence, WWW ’19, pages 1944–1953, New York, NY,
Wang, J., Wang, H., et al. (2012). Understanding Tables
on the Web. In Conceptual Modeling, pages 141–155,
Berlin, Heidelberg. Springer Berlin Heidelberg.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems