=Paper=
{{Paper
|id=Vol-1794/afcai16-paper2
|storemode=property
|title=Requirements of Affective Information Systems in the Industrial Domain
|pdfUrl=https://ceur-ws.org/Vol-1794/afcai16-paper2.pdf
|volume=Vol-1794
|authors=Joachim Baumeister
|dblpUrl=https://dblp.org/rec/conf/afcai/Baumeister16
}}
==Requirements of Affective Information Systems in the Industrial Domain==
https://ceur-ws.org/Vol-1794/afcai16-paper2.pdf
Requirements of Affective Information Systems
in the Industrial Domain
Joachim Baumeister1,2
1
denkbares GmbH, Friedrich-Bergius-Ring 15, 97076 Würzburg
2
University of Würzburg, Am Hubland, 97074 Würzburg
Abstract. Recently, semantic technologies improved the information
access to massive amounts of data in various manners. Also industrial
domains benefit from semantic approaches. Information now can be ac-
cessed more precisely and is directly linked with related data. However,
the actual support for humans to access suitable content at time is still
in its infancy. In this paper, we motivate a number of requirements for
extending current state-of-the-art information systems in order to sup-
port human-aware information access. The requirements are based on the
experiences we made during the introduction of industrial information
systems over the last years.
1 Introduction
We motivate the application of semantic technologies and affective computing
in the domain of Technical Service. The primary task of Technical Service is the
troubleshooting and maintenance of advanced machinery. For instance, Tech-
nical Service has to identify the reason for a malfunction of a machine and in
consequence has to replace and adjust faulty components. With the increasing
complexity of machinery the Technical Service becomes more and more challeng-
ing even for well-trained service technicians. Technical documentation provides
the service-related information to support technicians during their work. Having
complex machinery, however, the information typically covers some thousand
pages only for a single machine. Consequently finding relevant information units
in a problem situation is difficult, time-consuming, and often not successful.
Many machinery companies report, that their globally distributed service tech-
nicians are often not able to find helpful information during their operations.
Recently, semantic technologies advanced information systems to improve
the search and navigation in the information data bases [5]. In contrast to full-
text search systems, semantic metadata are attached to information resources to
precisely point to information. Then, for instance, a chapter within a technical
documentation explicitly states its function as a repair instruction and names
the concrete components that are assembled [6]. With these metadata, the search
and navigation is improved dramatically. One prominent technology are seman-
tic search systems [7], where the retrieval of information is based on semantic
queries. With semantic search, a query can be stated much more precisely and
search results can be reduced significantly. The introduction of semantic tech-
nologies improved the application of service information systems. However, a
number of challenges are still left unsolved.
In this position paper, we discuss the requirements of the domain Technical
Service in Section 2. We propose a conceptual model approaching these require-
ments in Section 3. We conclude the paper in Section 4.
2 Requirements for Affective Information Systems in
Technical Service
Semantic technologies improve the accessibility of resources in information
systems dramatically. In comparison the standard information systems with text-
based queries and tags, semantic information systems are able to precisely point
to resources based on concepts stated in the query. Further, they are able to
rewrite queries so that results include semantically related or synonym resources.
Nevertheless, a number of requirements exist in Technical Service, which
should be taken into account to simplify and refine the information access for
the user. We claim, that current systems miss different aspects of awareness that
in summary complicate the access to information. An overview of the aspects is
depicted in Figure 1.
Competence-awareness:
Context-awareness:
Carrier 12:00 PM
Service Mate
- User persona
- Product under work
http://www.domain.com Google
- Already researched &
- Problem context consumed knowledge
- Location of work Semantic
- Available equipment Information
- Already consumed Systems
User-awareness:
knowledge - Already consumed
knowledge
- Emotional state
- Affect detection
- Emotive UI
- Body sensors
- Multi-modal input
Fig. 1. Requirements of affective semantic information systems together with support-
ing technologies.
We discuss the aspects in the following:
1. Context-awareness: For large corpora, even semantic search yields large
amounts of information resources and the selection of the most appropriate
resource rarely can be done automatically. With the knowledge of the cur-
rent user context, a context-aware system can filter the query results to the
most appropriate ones. For instance, when looking for the replacement of a
component, it is not necessary to also offer the operation manual for this
component.
2. User-awareness: Stating a semantic query is not necessarily more user-
friendly than a standard text query. Users have frequently problems for-
mulating appropriate queries. With growing frustration of not finding an
appropriate information resource, the users are again turning away from
the documentation system missing important information for their work. A
user-aware system will monitor the user’s satisfaction during the information
research and consumption. It will propose alternative ways of communication
when the user’s satisfaction is downgrading.
3. Competence-awareness: Technical information is typically written for
well-trained technicians. In the context of world-wide service operations the
technicians may not be well-trained in some regions. Then, the information
resources are simply not useful because they miss important detail for less
competent technicians. A competence-aware system will preferably present
information with an appropriate level of detail to the user.
In the following section, we introduce a conceptual model for the realization
of the requirements stated above.
3 Conceptual Model
We develop a conceptual model relating information resources from a semantic
information model with concepts representing context-awareness, user-awareness,
and competence-awareness. The semantic information model is fitted to the
needs of information management in the domain of Technical Service and has
been successfully used in a number of industrial projects.
The shown model comprises task-specific ontologies and it will be used as the
blueprint for implementing smart service systems in the future. For the imple-
mentation and visualization of the ontologies we used the open-source version of
the semantic wiki KnowWE [2].
Figure 2 shows the general concept of information resources, i.e., entities.
We use the class prov:Entity from PROV-O [9, 11] to represent general in-
formation units. When needed, PROV-O allows for a standardized representa-
tion of provenance of entities, such as the creation date, author and version
history. Each instance of prov:Entity is assigned to a mime type to identify
the kind of information. Elements of metadata are represented as instances of
skos:Concept. SKOS [10] is a standard ontology for representing knowledge
structures, such as concept hierarchies. The properties skos:broader and the
skos:broader
prov:Entity skos:Concept
has_annotation
has_type
MimeType
Fig. 2. Basic elements of an information systems: Information resources are assigned
to concepts describing the metadata.
inverse skos:narrower are used with corresponding sub-properties to build a
flexible hierarchy structure.
3.1 Information Representation in Technical Service
The basic ontology from above is extended to model the general artifacts of
the domain Technical Service. An actual application–the entities of a concrete
manufacture–will align to this upper ontology.
In Figure 3 we see the class sm:Concept sub-classing from the general class
skos:Concept (the prefix sm stands for service model ). The class sm:Concept
represents general entities the can build of elements with the same type. For
example, we usually define a hierarchy of sm:Product instances, where each
products consists of hierarchically structured sm:Component instances. Usually,
a component fulfills a specific sm:Function. Final instances of sm:Component
can be referenced to sm:Part instances. Please notice, that a real-world ontology
additionally describes more detailed types of components, for example, classes
representing electrics/hydraulics, located components, and virtual components.
In Figure 4 we see an example with concrete instances (prefix ex is used
for example instances). When describing the component climatic system, we see
that this system (among others) consists of parts like AC pump and a climate
control module. Specific components fulfill specific functions of the machine. In
the shown example, the component climatic system is related with the function
cooling and the function heating. Having represented the product world of the
application, we are able to use the instances as annotation values. In the ex-
ample, the concrete book ex:book4711 consists of a chapter for replacing the
climatic system (ex:acReplacement) and a chapter showing the wiring diagram
(ex:acWiring). The first chapter is assigned to the pump of the climatic system,
i.e., how to replace the referred pump. The second chapter depicts the wiring of
the corresponding climatic control module.
In an information system, the query “give me information about the com-
ponent ex:climaticSystem” will find both resources ex:acReplacement and
ex:acWiring, since the reasoning will show that the resources are assigned to
parts that are included in the climatic system. Also, when looking for electric
skos:broader
prov:Entity skos:Concept
has_annotation
has_type subClassOf subClassOf
sm:Concept
MimeType sm:Location
subClassOf
subClassOf
has_location
sm:Component sm:Product
has_part
refers_function
sm:Part sm:Function
Fig. 3. Excerpt from a domain ontology describing the artifacts of Technical Service.
sm:Function sm:Component sm:Part prov:Entity
a a a a
has_part ex:acPump ex:acReplacement (r1) skos:broader
ex:cooling refers_function
has_annotation
ex:climaticSystem ex:book4711
ex:heating refers_function
has_part ex:climateControl- skos:broader
ex:acWiring (r2)
Module
has_annotation
Fig. 4. Instances of the upper ontology describing the component climatic system with
parts and information resources.
information for the function ex:cooling, the system will yield the information
resource ex:acWiring, since the semantic reasoning will derive this resource as
an appropriate (because close) match.
In the following, we briefly sketch how the ontology can be extended in order
to fulfill the requirements of an affective information system. All these attributes
can be used to further reduce the number of information resources shown as
query results but also should be used to present the appropriate user interface
depending on the context and state of the user.
3.2 Context-Awareness
In summary, the context of a technician comprises:
– Product under work, i.e., the actually considered product the technician
is working with.
– Problem context describing the activity of the technician, e.g., assembly
vs. diagnosis.
For larger problem contexts the current working step is also an interesting
attribute of the context.
– Location of work, e.g., workshop vs. field.
– Available equipment, e.g., which level of tools/measurement devices are
available
– Already consumed knowledge, i.e.. which information resources the tech-
nician already used during the current problem context.
Context-awareness should be primarily used to further filter the query re-
sults [1, 3]. Knowing the context of the user, the system can easily remove in-
appropriate/unhelpful information. In the past, a number of ontologies for rep-
resenting context-awareness were introduced [4, 12]. When introducing context-
awareness for Technical Service applications, these ontologies need to be re-
considered and extended accordingly, as it was already discussed in previous
work [8].
3.3 User-Awareness
The user-awareness can be attributed to the following information:
– Consumed knowledge identifying which information was already seen by
the technician.
– Emotional state of the user showing the satisfaction with the current sit-
uation. The situation can be possibly derived from the software functions or
problem context. The user’s satisfaction may be derived by affect detection
algorithms and/or using body sensors.
Knowing the state of the user the tool interface should adapt accordingly.
Adaptation of the software can be implemented by multi-modal user interfaces.
For example, after realizing that a speech recognition did not succeeded for a
couple of tries, the system should switch to alternative input interfaces.
3.4 Competence-Awareness
– User persona means distinguishing expert vs. apprentice. A user persona
is identified in order to present an appropriate interface and information
granularity to the user.
– Already researched and consumed knowledge can be used to derive the
competence level/needed information granularity of the current user.
Competence-awareness should be used to select the appropriate information
granularity for a specific query. For instance, the system can decide to imme-
diately show the disassembly of a specific component (expert level) or to first
explain the location and operation of the component, before presenting its disas-
sembly (beginners level). The disassembly itself could also distinguish the user’s
competence: Experts may only need a summary of steps with service-relevant
facts (bolt strengths, pressures, etc.), whereas for beginners the systems shows
a step-by-step disassembly instruction possible supported by 3D animations for
better identification and orientation of components.
4 Discussion
In this position paper we introduced a number of requirements of information
systems posed in Technical Service. The requirements are based on a 10-years
experience of establishing and improving information systems in this field. We
claim, that current state-of-the-art information systems need affective extensions
in order to cope with this requirements. In the previous sections we sketched
possible ways of extensions. Although the presented approach is by far not ex-
haustive, we see a field of exciting research questions with a practical motivation
in industry.
References
1. Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on context-aware systems.
IJAHUC 2(4), 263–277 (2007)
2. Baumeister, J., Reutelshoefer, J., Puppe, F.: KnowWE: A semantic wiki for knowl-
edge engineering. Applied Intelligence 35(3), 323–344 (2011), http://dx.doi.org/
10.1007/s10489-010-0224-5
3. Bobek, S., Nalepa, G.J.: Uncertain context data management in dynamic mobile
environments. Future Generation Comp. Syst. 66, 110–124 (2017), http://dx.doi.
org/10.1016/j.future.2016.06.007
4. Chen, H., Finin, T., Joshi, A.: An ontology for context-aware pervasive computing
environments. Knowledge Engineering Review 18(3), 197–207 (2003)
5. Elst, L., Abecker, A.: Ontologies for knowledge management. In: Staab, S., Studer,
R. (eds.) Handbook on Ontologies. pp. 435–454. Springer, Heidelberg (2004)
6. Furth, S., Baumeister, J.: On the semantification of 5-star technical documentation.
In: Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. pp.
264–271 (2015)
7. Guha, R., McCool, R., Miller, E.: Semantic search. In: Twelfth International World
Wide Web Conference (WWW 2003) (2003)
8. Legler, A., Baumeister, J.: Towards context-aware technical service. In: Bergmann,
R., Görg, S., Müller, G. (eds.) Proceedings of the LWA 2015 Workshops: KDML,
FGWM, IR, and FGDB. CEUR Workshop Proceedings, vol. 1458, pp. 288–295.
CEUR-WS.org (2015), http://dblp.uni-trier.de/db/conf/lwa/lwa2015.html#
LeglerB15
9. Moreau, L., Groth, P.: Provenance: An Introduction to PROV. Synthesis Lectures
on the Semantic Web: Theory and Technology, Morgan and Claypool (2013)
10. W3C: SKOS Simple Knowledge Organization System reference: http://www.w3.
org/TR/skos-reference (August 2009)
11. W3C: PROV-O: The PROV Ontology: http://www.w3.org/TR/prov-o (April
2013)
12. Wang, X., Zhang, D., Gu, T., Pung, H.: Ontology based context modeling and
reasoning using owl. In: 2nd IEEE Conference on Pervasive Computing and Com-
munications Workshops (PerCom 2004 Workshops). pp. 18–22 (2004)