Chapter 6 Viewpoint Focus Modelling
6.2 Focus Modelling Steps
In order to clarify what the viewpoint focus model should include, this Section organises the elicited requirements into logical steps for support and discusses the implications to support them. Figure 6.1 illustrates these steps as a sequence based on their interdependencies.
Figure 6.1 Sequence of steps to support the FRs based on their
interdependencies.
I: Data Selection. The first requirement to support concerns the selective data partitioning. The representation model should be able to distinguish
focus between different types of UGC partitions. For example, as presented in Chapter 5, Section 5.5, the data was partitioned based on the simulator's episodes (e.g. "Greetings" and "Bill") and user profile characteristics (e.g. age and gender groups). This allows for different ViewS to be constructed and more relevant comparisons can be made. Another type of partitioning includes different dimensions to be examined; focus spaces between emotions and body language signal meanings, for example, can be analysed.
II: Semantic Distance. The second part of the course concerns a block of
requirements to support with respect to the distance between two ontology entities. Figure 6.2 presents a simple example of possible distance-wise grouping of ontology entities. The representation model should allow flexibility in deciding the accorded distance, as more ontology entities in the same group illustrate more abstract clusters (supersets), while, fewer entities more specific (subsets) respectively. For ontological knowledge representation, distance concerns the semantic distance between ontology entities [143]. In this work, the semantic distance is defined by the hierarchy of the ontology (counting edges between ontology entities[144], see also Section 6.6 for implementation). In Section 6.8 (discussion) considering other types of semantic distances between ontology entities -e.g.
VI: Comparison
viewpoint focus comparison
V: Focus Model
viewpoint focus
IV: Aggregation
aggregation
III: Clustering
clustering composition of clusters cardinality of clusters
II: Semantic Distance
distance function max distance value ontology hierarchy I: Data Selection
considering ontology object properties -, is discussed both as a resolvable (based on the modelling approach) limitation and future research extension.
Grouping A with maximum distance 3 edges. Grouping B with maximum distance 2 edges.
Figure 6.2 Deciding the accorded distance between two ontology entities.
Based on the distance cap, two groupings are presented: A and B including b1and b2.
III: Clustering. The third part also concerns a block of requirements: for clustering (a), close in distance ontology entities should be grouped
together, hence all the possible pairs of annotated ontology entities have to be checked. Figure 6.3 illustrates a case in which one ontology entity, based on the decided distance can belong into two different clusters. This observation concerns the composition (b) of the clusters based on the neighbourhood of close ontology entities, as well as the cardinality (c) (number of entities in the cluster) of the clusters.
ontology entity annotated ontology entity b1
b2 A
Grouping with maximum distance 2 edges.
Figure 6.3 One (or more) ontology entities can belong to more than one
clusters based on the accorded distance.
This observation illustrates the requirement for representing the composition of a cluster of ontology entities.
IV: Aggregation. The aggregation is directly dependent on the distance,
clustering and hierarchy preservation requirements.
An aggregate is defined as the set of annotated ontology entities in a cluster together with the set of non-annotated ontology entities which belong in the hierarchy paths between the annotated ontology entities.
Longer distances result in larger clusters, which in turn results in different aggregates; difference can be identified quantitatively - considering the number of aggregates and the cardinality of the set of ontology entities in each aggregate, and qualitatively - considering the labels of the ontology entities. Figure 6.4 presents the resulted aggregates from the clusters presented in Figure 6.2 considering two different distance measures for the same set of annotated ontology entities. An aggregate of ontology entities reflects and inherits all the previously defined requirements including: distance, hierarchy (depth), cardinality, clustering and composition.
ontology entity annotated ontology entity common clustered ontology entity A
Grouping A and aggregate with maximum distance 3 edges.
Grouping B and aggregates (b1 and b2) with maximum distance 2 edges.
Figure 6.4 Two different ontology entity aggregates which emanated by
using different distance measures between ontology entities, thus different clusters (adapted from Figure 6.2).
V: Focus Model. The aggregates of ontology entities constitute the viewpoint focus.
VI: Comparison. Extracting the viewpoint focus consequently enables
support for comparison of different viewpoint focus: different aggregates from the viewpoint focus can be contrasted to explore similarities and differences on the semantic maps.
To conclude, a computational framework which will allow clustering of ontology entities based on the semantic distance is needed. The framework should allow for intelligent processing including: aggregation, and composition of different ontology entity clusters with respect to the ontology hierarchy and desired cardinality, as well as comparison. The problem of solving the course for supporting the requirements presented in Figure 6.1 can then be considered as a methodology for conceptual processing of the knowledge represented with the selected ontologies. To do this Formal Concept Analysis is exploited in this thesis and is discussed next.
ontology entity annotated ontology entity aggregated ontology entity b1
b2 A