=Paper=
{{Paper
|id=Vol-3005/sample-4col
|storemode=property
|title=Knowledge Graph for Explainable Cyber Physical Systems: A Case study in Smart Energy Grids
|pdfUrl=https://ceur-ws.org/Vol-3005/04paper.pdf
|volume=Vol-3005
|authors=Peb Ruswono Aryan
}}
==Knowledge Graph for Explainable Cyber Physical Systems: A Case study in Smart Energy Grids==
https://ceur-ws.org/Vol-3005/04paper.pdf
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
Knowledge Graph for Explainable Cyber
Physical Systems: A Case study in Smart
Energy Grids?
Peb Ruswono Aryan1[0000−0002−1698−1064]
Vienna University of Technology, Vienna, Austria
peb.aryan@tuwien.ac.at
Abstract. The rapid development of computing technology and au-
tomation widens the scope of the task delegated to cyber-physical sys-
tems (CPS) such as smart grids or smart buildings. Explainability, i.e.,
the ability to provide explanations about system states or behaviors be-
comes one of the requirements for future cyber-physical systems as more
complex computer-made decisions affect our daily lives. The work on the
explainability in CPS is scarce despite recent attention on the explain-
ability of algorithms in artificial intelligence. This doctorate research
aims to comprehensively understand the scope of explainability in CPS,
identify the critical components of an explainable CPS, and methods and
metrics to evaluate them. Specifically, our main research question is how
and to what extent Knowledge Graphs can be applied in enabling the
explainability of CPS. Using the design science approach, we attempt to
answer these questions in a set of iterations, starting with a simulation-
based approach and constructing a baseline system followed by more
focused studies and more realistic settings using data from real-world
CPS. The selected application domain in this work is industrial energy
systems such as smart grids and smart buildings. The expected outcome
of this work is a theoretical foundation and methods for developing an
explainable CPS applicable in various domains.
Keywords: knowledge graph · explainability · cyber-physical systems.
1 Problem Statement
Recently there has been concern that future CPS which span both the realm
of physical and cyber-worlds are challenged to explain their behavior to users,
engineers, and other stakeholders [7]. Rapid technological development in the
digital aspect of CPS, such as communication, control, and computation, drives
the increasing scale and complexity of CPSs. For example, low-power wireless
communication allows the proliferation of objects connected through a more ex-
tensive network. Advances in machine learning allow data processing algorithms
?
Supported by Siemens AG and Austrian Research Promotion Agency (FFG) in the
PoSyCo project (FFG No. 3036508).
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
25
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
to become adaptive and capable of solving complex real-world tasks. When these
complexities gain more influence on systems that impact our day-to-day life, the
necessity of having explanations in terms of the behavior of systems is emerging.
Explainability is the ability of a (software) system to provide explanations
about its states or behaviors in terms of a set of facts [14]. Explanations foster un-
derstanding of a thing being explained (explanandum) by linking it with existing
knowledge on the receiving stakeholder’s side [11]. Recent studies about explain-
ability are primarily oriented towards artificial intelligence (AI)-based methods
which function as black-boxes, meaning that their decisions are not transpar-
ent to end users [3]. Explainability is also an emerging issue in more complex
systems, such as cyber-physical systems. However, limited research has been per-
formed so far on understanding the theoretical foundations of explainability in
CPS as well as on exploring suitable solution paradigms to this problem.
Exploring various risk-related scenarios is undesirable to be conducted in the
real system, that is in vivo. Having a in vitro platform and reusable framework
allows for a more rapid development process and avoiding the unnecessary cost
of trial and error when developing an explainable CPS.
As an illustrative example in the energy domain, smart electricity grids evolve
from static to dynamically changing networks of large numbers of devices, e.g.,
photo-voltaic units (PV), electric vehicle charging stations (EVCS). The slow
charging of an EVCS is an event that requires an explanation for several stake-
holders including the EVCS owner, customer service representatives, field engi-
neers, and grid planners. An explanation could be that overcast weather leads
to lower than usual energy production through PVs in the region, this leads to a
lack of supply in the grid segment and to a control intervention to reduce charg-
ing power by the grid operator. From the consumer’s perspective, the change in
energy consumption should be seen as independent of how it is produced. The
energy production is then expected to run uninterrupted and provide sufficient
supply even when there is an increase in consumption. A swift response is de-
sired if an unwanted event such as failure to fulfill expected service or blackout
is unavoidable since the loss caused by the fault would be a function of time. On
the one hand, the technical operation employees expect detailed explanations
in order to be able to decide the next course of action to remedy a potential
fault/anomaly. On the other hand, the possibly larger population of affected
consumers, an ideal explanation would be more succinct and related to their
context, such as the service contract.
In order to generate perspicuous explanations for the intended stakeholders,
the explanatory system needs to integrate information from various sources such
as the structure of the system, the relationship between elements of the system,
and the history of the system’s state. Additionally, understanding the recipient
of an explanation is also essential to be tailored to be easy to understand.
26
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
2 Related work
Artificial intelligence, mainly the area of expert and knowledge-based systems,
extensively studies the task of providing explanation based on formalized logical
reasoning [14]. Recently the necessity to provide explainable reasoning for the
complex network has been reignited due to rapid progress in practical appli-
cations of machine learning in particular deep architectures of artificial neural
networks. Explainability becomes a hot topic in the AI community following the
concern about the ethical implications of applying machine learning solutions
under biased data [3]. Explainable AI techniques developed to explain complex
machine learning models to the users suggest that user orientation as one of the
critical aspect of explainability[10].
The interpretation of explainability from the perspective of industrial sys-
tems is even more pragmatic. Related topics such as anomaly detection and
subsequent root-cause analysis are essential topics in industrial (cyber-physical)
systems and are currently achieved with methods such as FMEA (Failure Mode
and Effect Analysis) [4] and FTA (Fault Tree Analysis) [6]. These methods re-
quire the specification of possible anomalies and their causes by various experts
that know (parts of) the system and are typically hampered by the ambiguity
and inconsistency of the collectively collected knowledge. The inconsistent ter-
minology also hampers deriving meaningful explanations as a follow-up step of
identifying a root cause for a given defect. Because of its specification in nat-
ural language, FMEA knowledge is difficult to reuse, is incomplete, and likely
inconsistent (as there no formal way to check consistency) [5].
CPS, particularly the smart grid, is relatively new and evolving, and it com-
bines different disciplines such as physics, statistics, and socio-economics. Studies
of explainability in CPS are scarce, especially for specific topics such as the ap-
proaches based on the knowledge graph. One of the closest approaches is fault
diagnosis systems in a smart building that combines a physical process model
and data-driven approach[13]. This work builds causality knowledge from ex-
perts into a knowledge graph and applies SPARQL update rules to infer poten-
tial causes of a given event. Considering the multi-disciplinary nature of CPS,
different communities use different representations of causality knowledge for
solving different tasks. For example, in the community of distributed systems
and cloud computing, one tries to automate causality mining from time-series
data using correlation[15]. In the other community, i.e., energy and power sys-
tems, an ensemble of statistical causal models and deep neural network[16] are
used to build models for short-term forecasting.
In summary, explainability encompasses, on the one end, a human who needs
an explanation and, on the other end, causality knowledge that is not known ex-
plicitly from the system’s description. Existing literature addressed these issues
only partially, and the focus of different communities is diverse. Only by col-
lecting various puzzle pieces can we see the big picture and establish a solid
foundation of explainable CPS.
27
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
3 Research Questions
The literature suggests that there is a limited understanding of what constitutes
an explainable cyber-physical systems. One line of research focuses on only user
aspect of explanation while other line struggles with ad-hoc or partial solutions.
Therefore, the main question for this research is:
RQ0 What are the main theoretical, methodological, and engineering founda-
tions that enable effective and efficient implementation of explainable CPS?
This question is the starting point to the more specific questions: What are
the core components needed to achieve explainability? To what extent knowledge
graph can help build an explainable CPS? What are the requirements for making
an explainable system applicable to various domains?
The literature also indicates that causality knowledge and user-oriented ex-
planation generation algorithms are the critical aspect of an explainable CPS.
Thus, this research also aims to address the following questions:
RQ1 What is the effective semantic representation to integrate different repre-
sentations of causality knowledge? A different source of causality knowledge may
have a different meaning of weight of a causal relationship. Some may involve the
coefficient of a differential equation, and some others might refer to probabilistic
quantities to refer to subjective belief or derived quality metrics.
RQ2 How to acquire causality knowledge efficiently from data and domain ex-
perts? How can we support domain experts to express causal relationships based
on domain knowledge and data? One approach to express causality captured
from domain experts used in [2] is SPARQL. How can we aid domain experts to
express their knowledge without learning about SPARQL first? Can we acquire
causality knowledge by analyzing temporal data using time-series analysis or
machine learning?
RQ3 What are effective and efficient algorithms for generation and ranking of
(alternative) explanations? What are the criteria to decide that an explanation
is plausible? Given that there are multiple competing hypotheses, what metrics
can be applied to compare and rank multiple explanation alternatives?
RQ4 How to effectively present explanations to system end-users? What are
the cognitive aspects of a user that are important in determining whether an
explanation is understandable or not? What are the metrics used for measuring
the comprehensibility of an explanation output on a selected user model?
The questions above correspond to the core functionalities of explanation
generation: causality knowledge acquisition and exploitation. Other aspects of
28
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
Fig. 1. Architecture of explainable CPS built on smart grid simulation platform [1, 2]
the interface to the end-users, such as visualization or generation of explanation
in natural language, will not be addressed in this research. These aspects will
become more apparent when the core and realistic use case scenarios have been
developed.
4 Research Plan and Preliminary results
This study adopts a classical Design Science methodology [8]: (i) the rigor cycle
is ensured by grounding methods for answering all research questions from a
thorough understanding of relevant literature studies and dissemination of the
intermediate results in the scientific community; (ii) deriving requirements from
concrete application contexts using simulation and living lab data from ongoing
research projects and the creation of PoCs to address these requirements con-
stitute the relevance cycle; (iii) method development, testing, and subsequent
revision constitute the design cycle.
This study has been conducted for a year. The following 2-3 years will be
focused on answering each research question. Specifically, the second year will
be allocated for addressing the representation and acquisition (RQ1) of causality
knowledge (RQ2). The analytics (RQ3) and presentation (RQ4) aspect of the
explanation will be conducted in the third year.
Preliminary results The current state of the PhD has resulted in an understand-
ing of explainability and explainable CPS. In the first iteration, the work is
oriented towards understanding the explainability in energy systems. Develop-
ing plausible and feasible scenarios and data acquisition drives the focus on using
a simulation platform (i.e. BIFROST [12], see Fig. 2). Additionally, the general
idea of an explanation generation algorithm was developed and evaluated using
synthetic data of a scenario related to electric car charging [1].
The following iteration builds upon the previous idea with more concrete
artifacts. One of the results is the architecture shown in Figure 1. The realiza-
tion of this architecture was then implemented as a prototype application for
demonstrating that the explanations from the scenario can be derived based on
29
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
Fig. 2. Prototype of explainable CPS built on smart grid simulation platform
simulated data and captured knowledge. The solution design and implementa-
tion result was then published in the energy community proposing the solution
based on semantic web technologies [2]. To this end, an ontology 1 for modeling
data and knowledge described in the architecture has been developed.
Figure 2 displays the prototype of an explainable CPS build as a part of the
BIFROST smart grid simulation engine.
Behind the user-facing interface is the engine that integrates data coming
from the simulation into knowledge graphs in the triple store, deducing causal
relations, detecting events, and deriving explanation for the detected events.
The explanation is then displayed as shown on the right side of the figure. From
this first iteration, we better understand what aspects are needed to build an
explainable CPS.
5 Expected results and their evaluation
The goal of an explainable CPS is to provide explanations of events for a variety
of scenarios. Close collaboration with domain experts is necessary To achieve
plausible scenarios that can be used as a basis for further evaluation. The devel-
oped scenarios are then implemented in a simulation to generate data. Further-
more, actual measurements will be used to ensure the validity of the simulation
data. User studies and empirical analysis are performed to evaluate the research
questions. The following describes the outcome and outputs for each research
question.
RQ1 The outcome for RQ1 is the incorporation of different causality represen-
tations to generate an explanation. A vocabulary for different meanings (e.g.,
1
https://pebbie.org/expcps/
30
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
relationship weight) of causality will be designed to augment the basic model of
causality. Additionally, an algorithm to fuse these different semantics of causality
will be developed as part of the explanation generation algorithm.
RQ2 Answering RQ2 will involve user studies and implementation of algorithms
to derive causality knowledge from simulated data. Method to acquire causal-
ity knowledge will be developed and evaluated using user-study and empirical
analysis.
RQ3 A set of metrics and ranking algorithms will be developed, and an expla-
nation generation task will be executed based on a prepared scenario. A group
of domain experts will be asked to manually create explanations as the gold
standard for measuring the algorithm’s performance. The evaluation of the al-
gorithm will use metrics such as MRR or Precision@10 as a base and modified
to accommodate comparing the graph structure of the explanation.
RQ4 A qualitative study will be conducted to acquire key characteristics of
explanation target or user profiles. A set of user-profiles will be defined, and
the explanation generation task will be executed using the scenarios. Another
user study will assess whether the customized generated explanation is relevant
to the intended explanation recipients. To this end, System Causability Scale
(SCS) [9] will be used as one of the metrics.
6 Discussion and Future work
The previous section described an end-to-end prototype of how an explainable
CPS should work after the first year of the doctoral study. This initial work helps
to identify issues formulated in the research questions for the next iteration. The
collection of artifacts from investigating each research question forms a solution
framework to build an explainable CPS.
Some topics possibly related to explainable CPS are intentionally not ad-
dressed considering the limited scope of this research. e.g., such as scalable
storage techniques to handle large-scale CPS data. Other topics depend on the
mentioned research questions, such as the study of specific presentation forms of
explanation (e.g., visualization) or exploratory search systems to explore the ex-
planation hypothesis and history of events. Further research on these topics will
enrich the framework for building an explainable CPS and enables explainability
in various systems.
7 Acknowledgement
This work is funded through a grant project titled ”Power System Cognification”
(PoSyCo) by the Austrian Research Promotion Agency (FFG) with Siemens AG.
I am thankful for PhD supervisory team Marta Sabou, Fajar Juang Ekaputra,
and Tomasz Miksa for the professional advises and personal supports.
31
Proceedings of the Doctoral Consortium at ISWC 2021 - ISWC-DC 2021
References
1. Aryan, P.R., Ekaputra, F.J., Sabou, M., Hauer, D., Mosshammer, R., Einhalt, A.,
Miksa, T., Rauber, A.: Simulation support for explainable cyber-physical energy
systems. In: 8th Workshop on Modeling and Simulation for Cyber-Physical Energy
Systems (MSCPES2020) (2020)
2. Aryan, P.R., Ekaputra, F.J., Sabou, M., Hauer, D., Mosshammer, R., Einhalt, A.,
Miksa, T., Rauber, A.: Explainable cyber-physical energy systems based on knowl-
edge graph. In: 9th Workshop on Modeling and Simulation for Cyber-Physical
Energy Systems (MSCPES2021) (2021)
3. Barredo Arrieta, A., Dı́az-Rodrı́guez, N., Del Ser, J., Bennetot, A., Tabik,
S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., et al.: Explainable
Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and chal-
lenges toward responsible AI. Information Fusion 58, 82–115 (Jun 2020).
https://doi.org/10.1016/j.inffus.2019.12.012
4. Ben-Daya, M.: Failure Mode and Effect Analysis, pp. 75–90. Springer London,
London (2009)
5. Dittmann, L., Rademacher, T.T., Zelewski, S.: Performing FMEA Using Ontolo-
gies. In: 18th International Workshop on Qualitative Reasoning. pp. 209–216 (2004)
6. Ericsson, C.A.: Fault Tree Analysis Primer (2011)
7. Greenyer, J., Lochau, M., Vogel, T.: Explainable Software for Cyber-Physical Sys-
tems (ES4CPS): Report from the GI Dagstuhl Seminar 19023, January 06-11 2019,
Schloss Dagstuhl (2019)
8. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems
research. MIS quarterly pp. 75–105 (2004)
9. Holzinger, A., Carrington, A., Müller, H.: Measuring the quality of explanations:
the system causability scale (scs). KI-Künstliche Intelligenz pp. 1–6 (2020)
10. Kirsch, A.: Explain to whom? putting the user in the center of explainable ai.
In: Proceedings of the First International Workshop on Comprehensibility and
Explanation in AI and ML 2017 co-located with 16th International Conference of
the Italian Association for Artificial Intelligence (AI* IA 2017) (2017)
11. Lombrozo, T.: The structure and function of explanations. Trends in cognitive
sciences 10(10), 464–470 (2006)
12. Mosshammer, R., Diwold, K., Einfalt, A., Schwarz, J., Zehrfeldt, B.: BIFROST: A
Smart City Planning and Simulation Tool. In: Karwowski, W., Ahram, T. (eds.)
Intelligent Human Systems Integration. pp. 217–222. Springer (2019)
13. Ploennigs, J., Schumann, A., Lécué, F.: Adapting Semantic Sensor Networks
for Smart Building Diagnosis. In: 13th International Semantic Web Conference
(ISWC). pp. 308–323. Springer (2014). https://doi.org/10.1007/978-3-319-11915-
1 20
14. Preece, A.: Asking ‘Why’in AI: Explainability of intelligent systems–perspectives
and challenges. Intelligent Systems in Accounting, Finance and Management 25(2),
63–72 (2018)
15. Qiu, J., Du, Q., Yin, K., Zhang, S.L., Qian, C.: A causality mining and knowledge
graph based method of root cause diagnosis for performance anomaly in cloud
applications. Applied Sciences 10(6), 2166 (2020)
16. Sriram, L.M.K., Ulak, M.B., Ozguven, E.E., Arghandeh, R.: Multi-network vulner-
ability causal model for infrastructure co-resilience. IEEE Access 7, 35344–35358
(2019)
32