This paper presents the research framing, preliminary results and future research plan of the author's on-going Ph. D. project. The project takes a design-oriented research approach to repurposing management theories as conceptualization lens for knowledge engineering. The work focuses on the Work Systems Theory, a theory introduced by S. Alter to describe enterprises with the help of a semistructured "work system" concept. Several updates to the theory have introduced gradually more structured definitions of this concept - some of them approaching the status of metamodels or taxonomies. The Ph. D. project hereby summarized aims to push this trend towards operationalizing the WST concepts in machine-readable forms, for run-time and design-time purposes. The technologies employed for achieving this are Knowledge Graphs (RDF-based) and Metamodeling environments (ADOxx, for modeling method engineering). This doctoral summary paper reports on the preliminary results, envisioned final Ph. D. results and the literature framing of the problem from a Design Science perspective. Directed by the Design Science approach, the work started with developing an ontological schema for RDF-based knowledge graphs built according to the Work Systems Theory perspective and applied over operational data from a legacy knowledge graph available to the host organization from an earlier project. The work further aims to develop a domain-specific modeling tool inspired by WST, for aiding stakeholders to capture and manage knowledge about their work systems in a structured manner, offering a comprehensive high-level view on value creation in modern organizations. Finally, means of integrating the current work on the WST knowledge graph with the future work on the WST modeling tool will be explored towards a streamlined knowledge management approach.
The research tackled during this Ph. D. Thesis is centered on developing an operationalization
bridge for concepts from the Work Systems Theory (WST) [
The Work Systems Theory has been traditionally used to design and evaluate systems in
enterprises and larger-scale public institutions. Any “work system” consists of human and
machine performers who complete a series of tasks, having a specific purpose, obtaining clearly
defined deliverables or measurable results for a "customer's" benefit. This Ph. D. work envisions
information systems made operational with the help of WST-specific RDF knowledge graphs as a
semantic layer over traditional enterprise knowledge graphs lifted from legacy data or created
by other means. The work explores a top-down work systems integration that can remain
compliant with the idea of transcending roles, which is advocated by Work Systems Theory.
Concretely, the “customer” of a specific work system can be a “participant” in another one, as WST
doesn’t enforce immutable roles for the actors involved in an organization’s work systems. This
requires proper contextualization and granularity to be enforced by design and maintained in a
machine-readable knowledge structure governing this. As any work system is composed of
participants, customers and activities, all these comprising parts are being directly affected by
the way digital technologies shape up the organizational environment, especially in the case of
medium to large scale businesses or public institutions. Knowledge graphs have demonstrated
their capabilities to offer a smooth transition in this regard, allowing users to navigate easily
through data on multiple layers of aggregation and abstraction, obtaining increased quality
information through semantics-driven data queries [
Apart from the graph conceptualization of WST, which has already been achieved in the first
part of the Ph. D. program, there are also plans for developing a WST-based conceptual modeling
tool. The modeling tool will allow the mapping of organizational systems according to the theory,
enabling design-time knowledge management and analytics capabilities for modern
organizations. The research plan is to develop the aforementioned tool by way of Agile Modeling
Method Engineering (AMME) [
The reminder of the paper is structured as follows, according to Design Science principles [
Design Science Research investigates artifacts in a context – in our case the motivating organizational context is that of a higher education institution where new procedural changes arose as a resilience plan to fight the pandemic scenario, leading to many changes in the digital facet of the institution, accompanied by a multitude changes in work procedures.
All the work procedures are written and kept solely in human natural language, lacking traceability to their operating context or motivation elements (strategy, environment incentives etc.). This makes it difficult to navigate through procedures and to understand their rationale, or to aggregate their implied resources or stakeholders in the sense that enterprise architecture analysis typically allows. Moreover, situations of non-compliance are difficult to spot when human assessors must perform all the work, instead of having information systems that could make such assessments automatically. The aforementioned organization was previously involved in the process of building an internal knowledge graph, populated with RDF graphs lifted from its legacy databases. Primary results of this effort were first reported in [9], and this thesis plans to build on top of that work by adding a semantic layer inspired by the Work Systems Theory and streamlined with a diagrammatic modeling method for managing enterprise knowledge in WST terms.
By taking into account the context and challenges discussed in the previous subsection we can formulate the following problem statement according to the DSR template:
Improve knowledge management through richer decision support and semantic data integration in organizations (problem context) … by treating it with a WST-conceptualized semantic layer that can serve knowledge engineering efforts (knowledge graph building and modeling method engineering) (artifact) … to satisfy a need for analyzing how work is performed across multiple work systems, considering both run-time resources and design-time decisions involved in work procedures (requirements) … in order to ensure capabilities for semantic traceability of value-creation, resources, actors, and work procedures across multiple work systems in the organization (goals).
Starting from the given problem statement the requirements can be split in two categories, according to their scope, as the research plan is to conceptualize WST for both knowledge graphs and conceptual modeling tools. For the knowledge graph conceptualization we took into account the following guiding principles: • As described in [9], an initial knowledge graph was developed by semantic lifting of legacy relational databases in an academic setting. The WST-based semantic graph should be valuable by itself but also in conjunction with the already existing legacy graph, offering appropriate bridging to the operational data; • Internally approved work procedures should constitute the basis for defining new granular work systems. The knowledge graph aims to define a basic but also rich enough semantic structure for traceability of work and processes obtained from legacy BPM and ERP systems; • The knowledge graph must be able to support scenarios of non-compliance pattern detection and aggregated reporting of work and procedures across multiple work systems. WST concepts and relations must be able to provide the conceptual backbone for analyzing each of these scenarios from a high-level perspective The second artifact, a WST-enabled modeling tool is guided by the following requirements: • The tool should offer a drag-and-drop-based look and feel for building diagrams of work systems decomposable to appropriate granularity; • WST concepts shall be split in model types according to their scope in order to avoid cluttering of diagrams; semantic hyperlinks will ensure meaningful relationships between different diagrams contributing to a multi-perspective WST-based enterprise model; • The tool shall offer basic work systems analytics and visual traceability capabilities, without needing to export diagrams to a different system; • The tool shall offer RDF-based interoperability with the knowledge graph.
This final integrative aspect is related to interconnecting the WST-enabled ontology to the WST-enabled modeling tool. Even though they could be regarded as separate projects at a first glance they must form together a knowledge management method. The modeling tool will become an enabler for integrating the WST-based ontology developed in the first part, into medium to large scale organizational systems.
Structurally, Work Systems Theory is built on six main elements: Customers, Products or Services,
Processes and Activities, Participants, Information and Technologies. Any work system as
described in [
There are a series of scientific works such as [
The modeling tool that is currently in development process, alongside the ontology previously mentioned will increase knowledge management capabilities, by adding a new semantic navigability layer through richer queries and semantic-enabled agents. It transforms WST from a framework of designing and evaluating work systems into a knowledge acquisition lens for modern organizations.
The research plan for this Ph. D. thesis can be summarized in three stages: 1. In the first part scientific literature was studied and we came to the conclusions that conceptualizing WST on a knowledge graph ontology could provide a step forward in regards to designing, analyzing and tracing value in work systems. The literature research work was done by surveying state of the art literature related to Work Systems Theory, Enterprise Knowledge Graphs and Conceptual Modeling Method Engineering. Some of these works are detailed in Section 7. Articles like [12] describe the creation of metamodels conceptualized on specific information management theories, but they don’t always reach to the status of operational artifacts. The work done in this Ph. D. project bridges this gap with knowledge structures that can be used either in relation to run-time traces of work systems or with design-time decisions; 2. Secondly, the research and engineering methodology were chosen for all the artifacts to be developed. As mentioned earlier, Design Science Research and Agile Modeling Method Engineering were used. Currently, the plan is to have three Design Science and AMME iterations for each artifact. On the first subproject related to WST-enabled ontology, the initial DSR iteration was successfully completed with a draft of the knowledge graph being subjected to scalability tests on a GraphDB licensed installation. On the WST-enabled modeling tool development side the project is currently at the beginning of the first Design Science and AMME iterations. The first DSR iteration from the ontology development artifact is synched to the DSR iterations from the WST-enabled modeling tool development research project as the obtained ontology constitutes the foundation for the metamodel of the second artifact; 3. A first ontology for WST-based knowledge graphs was developed during the first DSR iteration and started subjecting it evaluations from competency and scalability perspectives; 4. The fourth step of the research plan is represented by developing a conceptual modeling tool for work systems design and analysis. This phase was recently initiated and we're discussing early metamodel drafts before starting an ADOxx-based implementation. AMME and Design Science will be continuously employed to obtain the expected results in the form of a diagrammatic tool.
In regards to the evaluation of resulting artifacts, the plan is to perform a series of quantitative and qualitative measures, related to the achievement of research goals inspired by the criteria taxonomy in [13].
On the quantitative evaluation side we plan to measure execution times for running queries on knowledge graphs or automatically generating graph instances and diagrams with the modeling tool. This phase is ongoing currently as we are still performing measurements on our artifacts.
The qualitative evaluation will assess how end-users interact with our artifacts when it comes to querying knowledge graphs or designing work systems with our modeling tool. The plan is to use a series of questionnaires for measuring how easily users are able to achieve desired goals with the aforementioned artifacts and also a script that follows each click and action performed by users in the modeling tool will be implemented, helping us to see in real time how the modeling effort is going. Another aspect would evaluating in accordance to Moody’s principles of semantic transparency [14]. The qualitative measurements hasn’t been rolled out yet.
Due to the artifact-oriented nature of our research we chose Design Science as the overarching research methodology for this Ph. D. Thesis. We started DSR iterations having the requirements captured in Section 2 as our foundation for all the modeling tools and artifacts developed as part of this Thesis. Design Science framing is achieved by complying with the suite of phases [15] present in any research which follows this methodology.
1. In the Problem Identification step we surveyed state of the art literature, concluding that we need to center our research around conceptualizing WST as a layer for knowledge graphs in order to improve knowledge acquisition and work systems analytics capabilities. These main research objectives must be achieved in a frictionless manner, as we also aim to develop a Work Systems Theory enabled modeling tool, leading to richer capabilities for work systems analytics; 2. In the Objectives Statement phase we defined the drivers of our research as described earlier in Section 2; 3. The Design and Development phase is an ongoing step at the current time. We have already developed a preliminary graph ontology for enabling WST-based organizational graphs and the WST modeling tool is a work in progress, furtherly detailed in Section 5. 4. In the Demonstration step a larger-sized academic institution work system was mapped on the developed ontology. Concretely, we wanted to assess the ability of our artifact to represent an equipment procurement work system, having a clear view on the work system as a whole; 5. During the Evaluation phase, the obtained treatment was analyzed quantitatively by measuring the speed and performance of specific queries ran on the knowledge graphs developed based on the ontology. Qualitative measurements were performed by using the WST-enabled ontology to answer a series of competency questions. We plan to use the same evaluation approaches when the modeling tool will enter the evaluation phase. 6. The Communication phase consists of disseminating our work through a series of articles that are currently under review;
The chosen engineering methodology for developing the aforementioned WST-enabled modeling
tool is Agile Modeling Method Engineering (AMME) [
AMME relies on a Produce-Use cycle, which can be split into 2 sub-cycles: Conceptualization
and Modeling [
In order to develop the WST-based knowledge graph ontologies and modeling tools which represent the main contributions of this thesis we needed the following ingredients, necessary to achieve the desired conceptual design, successfully developing modeling language constructs and functionalities: • A fast prototyping environment, represented by ADOxx metamodeling platform [17], used alongside its proprietary scripting language, AdoScript [18]. ADOxx brings a supplementary level of agility when it comes to designing modeling languages, ensuring a quick transition from the metamodel creation phase to the actual language constructs implementation phase. AdoScript serves as the key ingredient in developing modeling functionalities such as model parsing or transforming modeling concepts into runnable code, which provides one of the key enablers of the model-driven software engineering facets [19]. • Another key technology used in our research is represented by semantic graph databases, namely GraphDB [20] in correlation with SPARQL [21] as querying language. We relied on semantic technologies in one of the works which are in reviewing phase, where we bridged the design-time view with run-time data found in legacy systems, coming as an aid for the Work Systems Theory (WST) [10].
The elements from the Work Systems Theory were conceptualized into a governing schema for knowledge graphs. The elements presented in Figure 1 were adjusted in order to support the implementation of semantic ontologies.
On the side of specific WST concepts, as seen in Figure 2, the schema is made of the following elements: WorkSystemSnapshot – that acts as the depiction of one analyzed work system of convenient granularity, EnvironmentElement, InfrastructureElement and StrategyElement – which serve as representations for concepts external to the work system, ProcessActivity – which can be decomposed into more granular steps. There is also a mechanism of showing the roles from a work system through the WST Role concept which acts as a superclass for CustomerRole, Product Service Role, Participant Role, Information Role and Technology Role – serving as conceptualizations for internal work systems elements. In our ontology we also needed to add the Thing concept which is the base of instantiation for the concrete composing elements of a work system. The relations between concepts are showcased below: • On the side of external relationships to a work system the following relations were defined: to an Environment element - subjectedTo, to an Infrastructure Element - supportedBy and to a Strategy element - drivenBy. As discussed earlier, all these elements are not involved directly in a WS, but rather they shape execution flows and processes by offering a context in which a work system is able to operate or motivation for its operation; • Internal relationships are tied to processes or activities, being separated in 2 types of concrete relationships: Offering Pattern – for showing situations in which an organizational actor is the customer of a process or activity and Involvement Pattern – when an organizational actor is a participant in a specific process or activity; • Processes must have decomposition capabilities, as any process can be formed of a suite of activities or sub-processes. These granular elements become the core of smaller work systems – the hasSubsystem relation offering the ability to show relations between a work system and its sub-system components. An activity can be decomposed in a series of steps, which leads to the existence of decomposedIn relations. In this case, we also have the next relationship which shows the succession between particular steps; • Work Systems Theory allows interchangeable roles, from one process to another, as one employee can be the customer of a process but then they can be participants in another process. In this case, connecting instances to a work system would lead to an n-ary relationship thus we needed to include ParticipantRole which is connected to an involvementPattern through an _involvesParticipant relation. In this case navigating from a WS snapshot to a specific participant can be done avoiding potential ambiguity caused by n-ary relationships; The knowledge graph from Figure 2 depicts a particularized schema for a university equipment procurement work system. In the lower part there can be seen subclasses of Thing which represent concrete composing elements, tailored to the scope of the knowledge graph, as University Employee, University Unit, Dean or Report of Need. This way, high level concepts of WST like Customers or Participants are brought to a scenario specific needed level of detail. The same idea can be applied to all the components of Work Systems Theory, which can be particularized thanks to the developed ontology.
Semantic queries can be regarded as proxies for competency questions, having the purpose to serve as an evaluation approach for knowledge graphs [22]. The queries were executed directly into a GraphDB [20] instance but a future DSR iteration will bring a dedicated web client for viewing and querying WST-enabled knowledge graphs.
In the following we’ll exemplify semantic queries corresponding to two competency questions: the first aims for extracting instance data by applying the lens of WST roles, while the second one will ignore instance level data, but it will rely on fructifying the enabled conceptualization layer between domain-specific concepts and WST roles.
Competency question 1: Who benefited across all work systems driven by the IT upgrade strategy, what products/services did they get and in what unit do they work?
SELECT ?customer ?unit ?productService
:hasProcessActivity/:hasExecution ?trace. ?trace :involves [:hasInstance ?customer; ?customer :belongsToUnit ?unit. } :inRole/a :CustomerRole], [:hasInstance ?productService; :inRole/a :ProductServiceRole].
Competency question 2: What participants were dropped in the To-Be version of the Equipment Acquisition work system snapshot?
SELECT ?participant WHERE
The current DSR iteration managed to achieve a prototype level ontological schema for adding a Work Systems Theory inspired conceptualization lens for knowledge graphs. The approach aims to become an important aid in bridging design-time and run-time views of work systems, allowing relevant stakeholders to benefit from a comprehensive semantic layer when navigating instances of work systems. Moreover, by exploiting the capabilities of semantic technologies we can add AI-empowered reasoning capabilities to work systems in future Design Science Research iterations.
Future work will be oriented towards developing a WST-supported modeling tool for knowledge acquisition and management, offering increased capabilities for mapping organizational processes, entities and value flows from a high-level view.
As a starting point for this artifact we will use the ontology schema presented in Section 5,
alongside works containing refined WST metamodels, published over the years [
In regards to functionalities of our WST-enabled modeling tool we plan to develop RDF graph serialization of diagrams and analytics capabilities directly in the modeling tool. On the RDF graph serialization of diagrams part, we have previous experience which was materialized in developing a modeling tool for designing knowledge graphs which is currently under review at a well-renown conference.
Figure 4 represents a mockup diagram of how a WST diagram made with the WST-enabled modeling tool could look like, by using the artifact in its early stages. The plan is to implement a few different model types in order to capture all necessary information and provide a clear separation of concepts. In the Core WST Diagram model there will be kept the main concepts derived from Work Systems Theory, which are introduced in Figure 2, such as Processes & Activities, different roles which can be fulfilled by entities, the work system itself which has a series of processes and activities and the rest of core work system related elements. Regarding Strategies, Infrastructure or Environment, which are elements that shape the development and execution of activities inside a work system, the plan is to keep them in separate diagrams as seen in Figure 4, in order to decrease cognitive load and provide a clearer separation of concerns. Linkage between entities from different model types, as seen in the upper part of Figure 4 will be performed through inter-model semantic links, named interrefs in ADOxx terminology. Another important aspect will be incorporating Moody’s principles [14] for offering domain-specific notation that can be easily grasped by practitioners in the modeling tool.
In the following some potential competency questions are presented, which could be used to evaluate the WST-enabled modeling tool. Such examples could be “What strategies drive Work System X.1?”, “Who are the participants involved in each process of Work System X.1?” or “What is the Process Trace that serves Customer A in Work System X.1?”. Executing related queries on diagrams will showcase the achieved semantic traceability of value with the modeling tool, also allowing us to directly measure performance similar to the case of the graph ontology.
Information systems rely on schemas, which serve as formal information models for their data
repositories. These schemas, to some extent, imply the semantics of the information stored within
them and represent specific perspectives on domain conceptualization. The study of Xue et al.
[25] delves into the theoretical underpinnings of ontologies and introduces the concept of
ontological views as an extension of traditional ontologies. It proposes the utilization of
ontological views to tackle the challenges associated with semantic integration. The proposed
approach leverages schemas to create local ontological views, explores the data instances within
the information systems to uncover semantic relationships among concepts within these views,
and constructs a domain ontological view by leveraging discovered equivalence mappings. Our
work also proposes an ontological schema for providing a high-level view on work system, relying
on the concepts proposed by Steven Alter [
The work of Liu et al. [26] proposes an ontology learning model, called domain ontology graph (DOG) that is able to generate ontology graphs, having domain-specific knowledge lifted from organizational text documents. In addition to the proposed Document Ontology Graph (DOG), the research defines two types of ontological operations: document ontology graph generation and ontology-graph-based text classification. To assess the efficacy of the proposed strategy, the authors conducted simulation studies utilizing Chinese text data. These studies aimed to demonstrate the potential effectiveness of this approach by generating DOGs that represent domain knowledge and performing text classifications using the generated ontology graph.
Dong et al. [27] propose the process knowledge graph modeling method which is aimed at enabling efficient reusability information in the process of building ships on a shipyard. Starting from a 3D model of ships, authors’ method is capable of building process graphs by semantic and concept mapping. This way, a unified model representation of heterogeneous ship model semantic is constructed, encompassing all the operations necessary for building that specific vessel. The authors employ a multi-strategy ontology mapping method that bridges the gap between the entity model and the process knowledge graph, leading to decreasing design cycles times and better knowledge reuse in the process of designing heterogeneous ship models. This article presents a way of adding a conceptualization layer to knowledge graphs with applications directly in industrial processes, while the work at hand is oriented towards building and analyzing work systems on a higher, conceptual level. In our case, the scope of ontologies isn’t aimed at representing and storing knowledge about specific physical objects, but at providing the means for evaluating and analyzing work systems, which are composed of more actors, processes, activities and physical elements.
The work done in [28] presents a disaster management framework based on a responsibility matrix lifted from real-world cases, which is underpinned by an ontology. Named as the Disaster Management Ontology (DMO), this ontology-based framework facilitates task allocation among relevant authorities at different stages of a disaster. Additionally, it serves as a knowledge-driven decision support system for providing financial assistance to victims. In the proposed DMO, ontology is employed for knowledge integration and as a working platform for reasoners. The Decision Support System (DSS) ruleset is formulated using the Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. Furthermore, OntoGraph, a class view of taxonomy, enhances the interactivity of the taxonomy for users. This work presents another case of applying conceptualization lenses for knowledge graphs aimed at facilitating knowledge management on real world scenarios, with a strong impact in aiding recovery plans in natural disasters scenarios. The work showcased in this paper also promises to become an important aid for knowledge reuse and obtaining increased efficiency in scenarios that can be found in day-to-day work inside medium to large scale organizations. A WST-based approach could also be rolled out on designing and analyzing life-saving systems, as WST has a high grade of applicability on any sort of work system.
The WST-based knowledge graph and the WST-based modeling tool are intended to be complementing tools of a higher level knowledge management method that will ultimately encompass the Ph. D. results under an overarching frame answering the DSR problem template formulated in Section 2. For now the two parts are somewhat disjoint, the knowledge graph aiming for integration with run-time data from legacy systems, while the modeling tool will focus on design-time decision support. Interoperability opportunities between the two remain to be investigated, considering the already reported experiences with the ability to convert ADOxxbased diagrammatic models to RDF as advertised by the "model-as-a-service" approach in the OMiLAB Digital Innovation environment [29].
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