=Paper=
{{Paper
|id=Vol-3645/forum4
|storemode=property
|title=Towards a modeling method for low-code knowledge graph building
|pdfUrl=https://ceur-ws.org/Vol-3645/forum4.pdf
|volume=Vol-3645
|authors=Andrei Chiș,Robert Andrei Buchmann,Ana-Maria Ghiran
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/ChisBG23
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==Towards a modeling method for low-code knowledge graph building
==
https://ceur-ws.org/Vol-3645/forum4.pdf
Towards a modeling method for low-code knowledge
graph building
Andrei Chiș1, Robert Andrei Buchmann1 and Ana-Maria Ghiran1
1
OMiLAB@UBB-FSEGA, Faculty of Economics and Business Administration, Babeș-Bolyai University, Cluj-Napoca, Romania
Abstract
Context and motivation. Knowledge graphs (KGs) provide an effective means for representing and managing
both knowledge and data in a uniform format. KG editors have traditionally taken the form of logic-based
or slot-based ontology/schema editors obscuring the graph structure or limiting the graph perspective to
visualizations of KGs created by other means.
Question/problem. KG implementation is tied to RDF serializations that citizen users might have difficulties
handling. This poses a blockade on citizen-level adoption of knowledge capture means as the majority of
stakeholders lack technical skills. There's a shortage of open tools enabling a diagrammatic user experience,
particularly needed by educators and those who must build KG exemplars.
Principal ideas/result. The paper presents an early version of a modeling tool for developing KGs in a visual
environment, providing a drag-and-drop user experience combined with tabular editing and features enabled
by means of metamodeling.
Contribution. The proposed modeling tool advocates the repurposing of metamodeling practices towards
establishing flexible low-code KG building environments to enable more citizen-centric knowledge capture
methods based on diagramming.
Keywords
Low code knowledge graphs, RDF, Agile modeling method engineering, Conceptual modeling, ADOxx 1
1. Introduction
Even though knowledge graphs are not a new technology their citizen-level adoption has been
deterred due to a series of factors such as technological readiness (limited number of market-ready
products for many years). Building ontologies and graphs involves non-trivial skills regarding e.g.
RDF serialization formats [1] such Turtle, TriG or N-Triples. Users must be proficient with knowledge
representation before any attempt to capture even the simplest KG fragment – but most citizen users
attempting to capture networks of associations are only familiar with mind mapping and similar
visual means of note taking. With our work we try to find a middle ground between the two, by
repurposing our experience with DSML (domain-specific modeling language) engineering towards
developing a visual language for not only designing, but actually building graph-like visual structures
that can be exported as RDF fragments for both the TBOX and ABOX levels.
Currently, the ongoing trend in the business and technological worlds of automating as much as
possible with minimal code input/handling led to the apparition of “citizen development” and “citizen
automation” [2], paving the way for “citizen knowledge acquisition” [3] as a new paradigm for
sourcing and employing relevant organizational information from stakeholders. In order to
democratize stakeholder access to Semantic Web-based knowledge acquisition, this paper presents
early results from a project developing the GRAPHxx modeling tool, which aims to help users create
ontologies and knowledge graphs in a visual, frictionless manner, hiding from users most parts of
Companion Proceedings of the 16th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling and the 13th
Enterprise Design and Engineering Working Conference, November 28 – December 1, 2023, Vienna, Austria
andrei.chis@econ.ubbcluj.ro (A. Chiș); robert.buchmann@econ.ubbcluj.ro (R. A. Buchmann);
anamaria.ghiran@econ.ubbcluj.ro (A. M. Ghiran)
0000-0003-0173-7250 (A. Chiș); 0000-0002-7385-1610 (R. A. Buchmann); 0000-0001-7890-9386 (A. M. Ghiran)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
OWL's [4] technicalities and formalism. Even the more experienced knowledge graph developers such
as KG engineers, educators or academics can benefit from a tool that simplifies the process of creating
and managing graph artifacts, thus reducing the effort of knowledge capture, graph development or
of building didactic examples that can be edited by visual means. Therefore, GRAPHxx acts as a
visual-first, drag and drop development platform for KGs. Other capabilities offered by GRAPHxx are
a Turtle format export and direct loading of exported graphs to a selected GraphDB [5] server for
querying and operationalization.
Numerous visual tools or visual plug-ins for KG tools are available, such as Gra.fo [6], OWLGrEd
[7], Protégé [8] or Graffoo [9], which is a visual syntax for graphical modeling and visualization of
ontologies, used in conjunction with yEd [10]. Another artifact in this field is represented by
GRAPHOL [11] - a fully graphical language inspired by UML, having the purpose of modeling OWL
2 ontologies. Moreover, Chowlk [12] presents another approach being positioned as an UML to OWL
ontology converter. These artifacts are primarily focused on the ontological level of knowledge
representation, offering no or little support for visually populating knowledge graphs.
Methodologically, the work presented here is the result of an initial iteration of the Design Science
Research (DSR) cycle [13] (as an artifact-oriented research methodology) and of Agile Modeling
Method Engineering (AMME) [14] adopted as prototyping methodology.
The remainder of the paper is structured as follows: Section 2 summarizes some motivating tool
requirements. Section 3 presents design decisions and implementation details, while Section 4
provides more information on the evaluation approach. Section 5 concludes the paper, discussing
several limitations of GRAPHxx.
2. Prospective users and tool requirements
By interacting with KG educators and students who must create and showcase KG examples we
collected a series of usability requirements, which are the main focus of the new tool compared to
other tools who are either only visualizers, or are closely aligned to the OWL formal concepts rather
than the knowledge acquisition experience.
Usability requirements refer primarily to specific criteria and features which allow users to
accomplish the main tasks when it comes to visually developing and editing KGs – our motivating
requirements are summarized below:
• to have the ability to separate schema and data graph elements in separate diagrams;
• to provide a frictionless experience, suitable for less advanced users need to create knowledge
graph fragments through a diagramming experience (mostly) agnostic from the RDF
technological specificity;
• to intuitively handle patterns such as blank nodes, lists and prefixes;
• to integrate both tabular editing and visual editing of graph elements and relations, providing
convenient switching between them, as well as domain-specific icon customization to achieve
semantic transparency as advocated by [15].
The main user categories of the modeling tool are constituted by:
1. those who are used to the diagramming user experience of modeling tools or mind mapping
tools but are otherwise non-technical or with a limited RDF technological background – that
need to structure organizational knowledge in a machine-readable format;
2. knowledge graph engineers – who would benefit from a visual modeling tool that allows them
to develop ontologies but also concrete knowledge graphs or graph fragments in a faster
manner than writing RDF code by hand;
3. educators – who need means of presenting quickly drawn KGs to students and novices during
IS/CS courses and seminars, sometimes long before delving into the more advanced RDF/OWL
jargon (prefixes, containers, axioms etc.).
3. Design decisions and implementation details
In order to assess the feasibility of using the modeling tool for KG development and knowledge
capture on a real world scenario, a use-case derived from a higher education institution was
formulated. It follows the creation of a graph fragment alongside its ontological schema, being
populated with instances pertaining to the organizational environment of the university where the
authors of the paper perform their teaching and research activities. The institution in cause was
subject to several digitalization strategies over the course of last years, exploring the adoption of
knowledge graphs in order to store relevant information.
Concretely, the developed KG was populated with entities related to administrative units such as
departments or faculties, roles involved in teaching and research activities, persons who accomplish
such roles and several data nodes. Information about entities and data nodes was obtained from
different organizational sources, mostly being tied to legacy databases. Alongside entities,
relationships were added in order to show the dependencies between organizational units, roles and
staff. Each entity, data node or relationship available in the knowledge graph has a series of attributes
that were set in order to increase the level of information provided. The knowledge graph obtained
in this manner offers semantic querying capabilities (see Section 4) which can prove to be an
important means of decision support for management departments. The development of larger scale
knowledge graphs containing data about the whole academic organizational context could prove to
be of significant help for higher management purpose and traceability of value chains involved in
teaching, research and academic development activities.
Agile Modeling Method Engineering (AMME) [14] is an established approach to engineering and
evaluating modeling methods. A modeling method comprises a modeling language (typically a
DSML), operating procedures and model-driven mechanisms/artifacts.
ADOxx [16] is the most used metamodeling platform of choice for AMME, due to its agile
metamodel deployment and capabilities of scripting model-driven artifacts, which have been
employed in projects of diverse domain-specificities [17] and is the core technological enabler of
OMiLAB's digital innovation environment [18].
The ADOxx-based metamodel was tailored to incorporate the RDF specificity as the "domain" of
a DSML, however avoiding as much as possible specific OWL jargon, offering a friendlier
environment for novices in the semantic web technologies field.
GRAPHxxModellingNode, SchemaNode and ContentNode act as superclasses for the concepts
available in their respective modeltypes. GRAPHxx achieves separation of concepts in 2 different
model types: Graph Schema and Graph Content.
Figure 1: Proposed metamodel of GRAPHxx Modeling Tool
In Graph Content diagrams the primary concepts are ThingIdentifier – an individual that can act
as a subject or object in RDF terminology, being of a ThingType; Data – for storing literal values,
HelperNode – anonymous nodes in RDF terminology and Container – which can represent ordered or
unordered lists by setting the ContainerType attribute. On the relation classes side there are RelatedTo
– being a concrete representation of Property from Graph Schema diagrams, IsOfType, HasAttribute –
for linking ThingIdentifiers (instances) to specific Data and RelatedToContainer – for linking things to
Containers. The relation sameAs comes in the form of a hyperlink supported by ADOxx to navigate
between graph fragments about the same thing (owl:sameAs).
The Graph Schema diagrams are used for building ontologies and use constructs such as Property
(predicates from RDF terminology), DatatypeNode, ThingType (classes that are instantiated in Graph
Content diagrams).
On the relations side the most used are IsOfType (a non-OWL-specific representation for rdf:type),
the From (trivialized from rdfs:domain) and the To (trivialized from rdfs:range).
There are two model level attributes for storing prefixes and for choosing whether prefixes should
be listed in the diagram or obscured.
Two main functionalities - graph serialization as Turtle file and serialized graph loading to
GraphDB server instance - were implemented with the help of AdoScript [19] and Python.
Figure 2 suggests this diagram-to-RDF code generation functionality. Both content and schema
(pertaining to an RDF description of some university resources) are visible and written in a Turtle file
- instances on the left, the RDF schema on the right and the option of adding isOfType relationships
either as hyperlinks between the two or as visual arrows (if there is not risk of visual cluttering), or
of switching between the two (e.g. first link ontology classes to instances, then visualize them in the
instance-level diagram also). Other functionalities of the modeling tool include prefix changing over
multiple diagram elements, URI generation from entity Labels and finally the upload to a dedicated
GraphDB triple store, directly from the modeling environment.
Figure 2: KG exemplar converted to Turtle via Ado-scripting
Figure 3: Patterns of handling RDF predicates: tabular on the left and diagrammatic on the right
Figure 3 showcases two patterns of handling RDF properties. The modeling tool offers more
control to end users regarding how much visual knowledge has to be incorporated in a certain
diagram, the tabular format being a viable alternative in cases where visual cluttering becomes an
issue. They can be edited separately and converted between tabular and visual representations, just
like the type links mentioned before - actually this script-driven switching between different user
perspectives on the graph content is one key motivator behind GraphXX.
Figure 4: Modeling attributes of a Thing
Figure 4 shows the way in which attributes can be set for ThingIdentifiers in the modeling tool.
The same idea applies for all the other modeling concepts that have attributes which can be modified
by users. In the case of ThingIdentifiers, the combination between File selection and Load image
attributes allows users to load custom graphical representations at design time, bringing the KG
content closer to a mind mapping or "boxes-and-arrows" diagramming experience, achieving
semantic transparency [15], thus making diagrams friendlier for novices and teaching purposes.
4. Evaluation approach
The initial Design Science iteration is still on-going, with only early evaluation steps and further
planning available at the time of this reporting. According to the Design Science artifact evaluation
criteria taxonomy [20] the following criteria are targeted:
A. Evolution (“learning capability”) which translates to gradually capturing richer
conceptualization following feedback received from users, thanks to the AMME
methodology. AMME’s iterative nature and the usage of ADOxx fast-prototyping
environment allows this. Currently, the state of the presented modeling tool shows progress
made after the first iteration - initial feedback includes the requirement to raise the
abstraction level above RDF to graph databases in general;
B. Consistency with technology (“harnessing existing technology”) translates to the requirement
of interoperability, already accomplished but open to providing more diverse export formats;
C. Completeness achieved through responding to a set of semantic traceability requirements, by
harnessing AQL [21], the ADOxx embedded metamodel-driven query engine. It allows
parsing intermodel hyperlinks to extract information related to a multi-diagram graph
structure, chains of relationships within a diagram, chains of links between different
diagrams, tabular properties, e.g.:
… retrieve the class at ontology level for the Professor element in the Business Information
Systems Department container:
(<"ThingIdentifier">[?"Label" = "Business Information Systems Department"]-
>"RelatedToList"[?"Name" = ":DepartmentRoles"])<-"Is inside"[?"Label" = "Professor"]--
>"IsOfType";
… retrieve all the names of the employees of the Business Information Systems Department:
(<"ThingIdentifier">[?"Label" = "Business Information Systems Department"]-
>"RelatedToList"[?"Name" = ":DepartmentEmployees"])<-"Is inside"[?"Label" != ""]
These represent two examples of university top-management questions which could be answered
by running AQL queries, thus achieving Semantic traceability through diagrammatic hyperlinks.
The article of Siau and Rossi [22] presents several different approaches when it comes to
evaluating modeling tools. In relation to our developed artifact the research plan includes:
• Feature comparison with other commercial tools for developing knowledge graphs, like
TopBraid Composer [23] or PoolParty [24]. Typically, most tools are aimed for OWL experts
or are limited to the TBOX level, offering low support for OWL-agnostic users who are
however capable of structuring a description in a "box-and-arrow" sketching approach;
• Metrics, which relies on running a series of experiments for determining times needed for
graph and ontology development, measuring the performance of GRAPHxx in serializing
small to medium developed graphs or graph fragments as Turtle compliant files against the
classical approach of writing RDF code by hand in Turtle format, as seen in Table 1;
• Survey, performed by running a series of modeling experiments with a group of students,
subsequently responding to a series of questionnaires about the modeling experience with
GRAPHxx.
Table 1
Comparison of serialization times
No. of triples Graph design and serialization Writing equivalent Turtle by
times using GRAPHxx (s) hand (s)
10 155.45 178.54
20 385.89 438.37
30 464.14 530.25
5. Limitations and outlook
In its current state, GRAPHxx is limited to Turtle serialization and is tied to building knowledge
graphs following the RDF standard, which remains to be developed to also cover labelled property
graphs from non-RDF servers [25]. Other serialization formats will be explored as part of future
iterations in the modeling tool development cycle, as well as the potential brought by bridging Large
Language Models (LLMs) with the modeling tool in an effort of providing automated graph
description or querying in natural language directly from the modeling tool.
In a longer term, we aim to integrate this with enterprise modeling languages where a notion of
data or knowledge objects is already available and could be drilled down to detailed linked data assets
in KG-driven enterprise architectures or business processes.
Acknowledgements
The present work has received financial support through the project: Integrated system for
automating business processes using artificial intelligence, POC/163/1/3/121075 - a Project cofinanced
by the European Regional Development Fund (ERDF) through the Competitiveness Operational
Programme 2014-2020.
References
[1] W3C, The Resource Description Framework (RDF). URL: https://www.w3.org/RDF/. Last
accessed on 25.09.2023.
[2] L. V. Herm, C. Janiesch, A. Helm, F. Imgrund, A. Hofmann, A. Winkelmann, A Framework for
Implementing Robotic Process Automation Projects, Information Systems and e-Business
Management, Springer, 21 (2022): 1-35. doi:10.1007/s10257-022-00553-8.
[3] A. Voelz, C. Muck, D. M. Amlashi, D. Karagiannis, Citizen-centric Design of Consumable Services
for Smart Cities, Digital Government: Research and Practice, Association for Computing
Machinery, 4.3 (2023): 1 – 18. doi:10.1145/3597420.
[4] W3C, The Web Ontology Language (OWL) Specification. URL: https://www.w3.org/OWL/. Last
accessed on 24.09.2023.
[5] Ontotext, The GraphDB Semantic Graph Database. URL: https://graphdb.ontotext.com/. Last
accessed on 26.09.2023.
[6] Data.world, The Gra.fo Knowledge Graph Designer. URL: https://gra.fo/. Last accessed on
25.09.2023.
[7] IMCS University of Latvia, The OWLGrEd Ontology Editor. URL: http://owlgred.lumii.lv/. Last
accessed on 30.09.2023.
[8] Stanford University, The PROTÉGÉ Ontology Editor. URL: https://protege.stanford.edu/. Last
accessed on 30.09.2023.
[9] R. Falco, A. Gangemi, S. Peroni, D. Shotton, F. Vitali, Modelling OWL Ontologies with Graffoo,
in: The Semantic Web: ESWC 2014 Satellite Events, ESWC 2014, Lecture Notes in Computer
Science, Springer, Cham, 2014, 8798. doi:10.1007/978-3-319-11955-7_42.
[10] yWorks GmbH, The yEd graph editor. URL: https://www.yworks.com/products/yed#. Last
accessed on 30.09.2023.
[11] D. Lembo, V. Santarelli, D. F. Savo, G. De Giacomo, Graphol: A Graphical Language for Ontology
Modeling Equivalent to OWL 2, Future Internet, 14.3 (2022): 78. doi:10.3390/fi14030078.
[12] S. Chávez-Feria, R. García-Castro, M. Poveda-Villalón, Chowlk: from UML-Based Ontology
Conceptualizations to OWL, in: The Semantic Web: ESWC 2022, Lecture Notes in Computer
Science, Springer, Cham, 2022, 13261. doi:10.1007/978-3-031-06981-9_20.
[13] R. J. Wieringa, Design Science Methodology for Information Systems and Software Engineering.
Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. doi:10.1007/978-3-662-43839-8.
[14] D. Karagiannis, Agile Modeling Method Engineering, in: Proceedings of the 19th Panhellenic
Conference on Informatics, Association for Computing Machinery, 2015, pp. 5–10.
doi:10.1145/2801948.2802040.
[15] D. Moody, The “Physics” of Notations: Towards a Scientific Basis for Constructing Visual
Notations in Software Engineering, IEEE Transactions on Software Engineering, 35.5 (2009):
756–779. doi:10.1109/TSE.2009.67.
[16] BOC GmbH, The ADOxx Metamodeling Plaform. URL: https://www.adoxx.org/live/home. Last
accessed on 01.10.2023.
[17] D. Karagiannis, M. Lee, K. Hinkelmann, W. Utz (Eds.), Domain-Specific Conceptual Modeling.
Springer, Cham, 2022. doi:10.1007/978-3-030-93547-4.
[18] D. Karagiannis, R. A. Buchmann, X. Boucher, S. Cavalieri, A. Florea, D. Kiritsis, M. Lee, OMiLAB:
A Smart Innovation Environment for Digital Engineers, in: Proceedings of PRO-VE 2020,
Springer, 2020, pp. 273-282. doi:10.1007/978-3-030-62412-5_23.
[19] BOC GmbH, The AdoScript Scripting Language. URL: https://www.adoxx.org/live/adoscript-
documentation. Last accessed on 28.09.2023.
[20] N. Prat, I. Comyn-Wattiau, J. Akoka, Artifact Evaluation in Information Systems Design-Science
Research – A Holistic View, in: Proceedings of PACIS 2014, AIS, 2014, 23. URL:
https://aisel.aisnet.org/pacis2014/23.
[21] BOC GmbH, The AQL Query Language. URL: https://www.adoxx.org/live/adoxx-query-
language-aql. Last accessed on 30.09.2023.
[22] K. Siau, M. Rossi, Evaluation of Information Modeling Methods - A Review, in: Proceedings of
the HICSS 1998, IEEE, 1998, 5, pp. 314-322. doi:10.1109/HICSS.1998.648327.
[23] TopQuadrant, The TopBraid EDG. URL: https://www.topquadrant.com/taxonomy-and-
ontology-management. Last accessed on 26.09.2023.
[24] Semantic Web Company, The PoolParty Semantic Suite. URL: https://www.poolparty.biz/. Last
accessed on 27.09.2023.
[25] R. Angles, The Property Graph Database Model, in: Alberto Mendelzon Workshop on
Foundations of Data Management, CEUR-WS, 2100, 26, 2018. URL: https://ceur-ws.org/Vol-
2100/paper26.pdf.