The guidance of the user is a defining requirement for the OGVIC approach; therefore, we briefly give a definition of guidance, discuss different kinds of guidance and show where in the visualisation pipeline the user can be guided.
Definition 5 (Guidance) The »act or process of guiding« where guiding is defined as to »direct or control the path or course of (something)« [MWa].
This definition leaves much space on the degree of enforcement of guidance rules (»direct« vs. »control«); therefore, we introduce levels of guidance in Chapter 8. Furthermore, according to Si-Said et al. [SSR98], guidance can be categorised into step guidance and flow guidance. Step guidance concerns guiding a user in fulfilling a single process step. Flow guidance concerns guiding a user from one step in a process to the next step in a process that is the best to perform. Both kinds of guidance are relevant to visualisation processes.
2.3.1
Guidance in Visualisation
Guidance for visualisation has been discussed by Bull [Bul08] and is inherently interlinked with tooling, since it is the system that shall guide the user. Guidance can be offered for all customisation steps in the visualisation reference model (cf. Fig. 2.2), whenever the user interacts with the system. The user can be guided when filtering data, when performing the visual mapping and also when performing view transformations.
Guidance for Filtering When filtering the source data, one means for guiding the user is faceted browsing , which became popular in recent years. The user is restricted by construction of the GUI in order to allow only those filtering queries that return results. Ben-Yitzhak et al. [BGH+08] also refer to faceted browsing as guided navigation.
Guidance for Visual Mapping When performing the visual mappings the user may be guided to select expressive, effective and appropriate visual means (cf. Sect. 5.7). Some visualisation design systems from the field of statistics analyse the data and data types in order to suggest visual means based on the probable scale of measurement (e. g., nominal, ordinal, quantitative; cf. Sect. 5.6.2).
Guidance for View Transformations Even in the final rendered view, the user may be guided, for example by enabling navigation along visual structures in addition to a free movement through the graphical space.
The focus of the OGVIC approach is on the second case, the guidance for performing the visual mapping. We show that with the emergence of highly structured data, new and different oppor- tunities for guiding the user are possible. These go beyond the abilities of classic visualisation design systems for tabular statistical data.
Chapter 3
Problem Analysis
Data stored in ontologies is highly interrelated and has formal semantics offering good op- portunities for connecting various data sources and enabling complex querying and filtering. However, intentionally, this data is completely free from presentation, structuring and formatting information. On the one hand, this means that the pure ontological data is hard to read and understand by humans. On the other hand, this can be seen as an advantage, since the raw shape of data offers ideal conditions for visualisation. We argue that current ontology visualisation approaches often do not exploit the benefits that ontological data offers. Furthermore, ontologies could help to overcome problems with the interoperability of visualisation systems and the exchange of visualisation knowledge.
In this chapter, we list the problems we identified in this context (Sect. 3.1). Based on these problems, we formulate concretised research questions (Sect. 3.2) and describe the case studies we conducted (Sect. 3.3) in order to analyse concrete ontologies from various domains (Sect. 3.4) and identify frequently occurring visual mapping situations (Sect. 3.5 and 3.6). This problem analysis chapter results in a list of precise requirements for our ontology-driven visualisation approach (Sect. 3.7).
When identifying concrete problems in the next step, in many cases we describe these problems from the perspective of a specific actor. Therefore, we briefly introduce these actors and their use cases. Both actors are typically experts on their domain (e. g., biotechnology), but not necessarily skilled in visualisation or programming:
• The Visualisation Author – Her goal is to visualise ontological data that she found on the Semantic Web or that is available from local ontologies. After specifying a set of helpful visual mappings, she wants to send her visualisation settings to a colleague who told her that he needs to visualise a similar ontology. In a later project, she also needs to reuse the settings herself. However, some changes and extensions have to be done, since the ontology grew in the meantime. Additionally, her company decided to use a completely different visualisation suite. A third colleague also defined some visualisation settings. She could save some time if it were somehow possible to combine his settings with hers. • The Domain Author – She created a new domain ontology and wants to suggest a good
visualisation that is helpful for people using her ontology. Just like authors of XML data sometimes offer style sheets, she would like to offer recommendations for visualising her ontology.
CHAPTER 3. PROBLEM ANALYSIS
3.1
Problems of Ontology Visualisation Approaches
We identified the following eight problems of current visualisation approaches for ontological data1:
P-1 Often no visual mapping exists, but a general transformation of data to document shape P-2 Visualisations often follow a single visual paradigm (e. g., Node-Link-Diagrams), while
others are ignored
To make use of the human eye’s capability of quickly perceiving complex data sets, we need to encode data variables to visual means. It is not sufficient to bring a huge ontology file into a document shape (P-1) or simply reflect the graph structure of the ontology by using a trivial node-link-diagram representation (P-2). The ontological data from our case studies greatly varies with respect to its scale of measurement, data types and structure. Since different visual structures have different effectiveness for encoding different kinds of data, multiple visual structures should be provided to create an appropriate tailor-made visualisation instead of using generic visualisations.
P-3 Visualisation authors cannot reuse visual mappings in other tools and share them with other people
P-4 Visualisation authors cannot compose their own mappings with existing ones
P-5 Domain authors do not have a standard format to ship visualisation information along with their domain ontologies
Ontology visualisations are created for specific platforms and separately for each visual paradigm. They cannot be shared with others and reused on other visualisation platforms (P-3) or in combination with different visual paradigms (P-4). Whereas more and more standardised formats are used for storing domain knowledge, visualisation settings are not stored based on standards (P-5).
P-6 A visualisation language for ontological data does not exist
Even though not standardized, a few general visualisation languages exist (Sect. 4.2.2). However, these cannot directly reference ontology specific constructs such as individuals, classes and relations (P-6). As an example, visualisation authors need to be able to conveniently reference relations between classes that exist indirectly by existential or universal restrictions.
P-7 Generic visualisation design systems do not exploit the specifics of ontological data P-8 Visualisation authors cannot exchange their system’s expert knowledge
Currently, only for tabular, statistical data good visualisation design systems exist (Sect. 4.1.1) that offer basic guidance functionality for simple data types based on internal visualisation knowledge and the analysis of the data. However, they cannot guide the user, when it comes to visualising ontological data with its specifics (P-7). As a basis to reduce visual mapping options to a useful subset, a visualisation approach should consider all the information it can extract from the ontology. Examples are inverse relations or the symmetry and transitivity of relations as well as subproperty relationships. An additional prerequisite for guiding the visualisation author is expert knowledge on visualisation. This visualisation knowledge is usually hard-coded into the system and cannot easily be exchanged, when new empirical results are available (P-8).
1 While we concentrate on the visualisation of ontological data in this thesis, we are aware that many of the
discussed problems, just like many of our proposed solutions, could also be applied to general visualisation systems.