DEL MODELO DE EVALUACIÓN DE PLANES DE DESARROLLO PARA EL MUNICIPIO DE MARSELLA

Figure 5.3 provides an example of the kind of semantic maps being shown to the simulator designers for their feedback. In this example, the annotated ontology entities for the Greetings episode for both emotion and body language signal meanings were highlighted by the microscope.

Potential Benefit 1 for Simulator Designers. The designers were able to

very quickly identify clusters of annotated ontology entities formed in the areas of positive and negative emotions, social interactions and psychological processes in the body language signal meanings. The simulator designers noted additional desired user reactions picked up by ontology labels highlighted in the UGC. Semantics can be used to externalise learners‟ reactions to the simulator designers or tutors. From this observation the first requirement was elicited for computational representation of viewpoint focus:

Focus Requirement 1. An automatic mechanism to identify clusters of

annotated ontology entities in the ontology space.

(a) "mental-state" branch of the WNAffect taxonomy of emotions

(b) "body language signal meaning" branch of the body language ontology

Figure 5.3 Semantic maps for the Greetings episode.

The simulator designers were able to very quickly identify clusters of annotated ontology entities using the semantic maps for (a) emotion and (b) body language signal meanings.

Potential Benefit 2 for Simulator Designers. The simulator designers

noted the differences in the number of annotated ontology entities across the different clusters for both emotions and body language signal meanings. Clusters with higher cardinality are referred to as hot topics by the designer,

e.g. the „positive emotion‟ cluster (as opposed to the „ambiguous emotion‟ cluster) in Figure 5.3. This prompted designers to consider if enough illustrations were provided, or indeed whether some of the less hot topics should be showcased more. This observation depicts the following requirement for viewpoints focus representation.

Focus Requirement 2. Preserve the cardinality of the clusters of the

annotated ontology entities.

Potential Benefit 3 for Simulator Designers. Another filter was executed

to visualise semantic maps for different user groups. From the 27 participants who had used the microblogging tool, 17 (8 female and 9 male) completed the anonymised user profile questionnaire. 13 participants were 22-35 years old and only 4 belonged to age groups >36. Figure 5.4 depicts two examples of semantic maps derived from microblogs of male participants that were shown to the simulator designers. The simulator designers again quickly identified clusters of ontology entities. In addition, they queried the parent node of the clustered annotated ontology entities, as well as the non-annotated ontology entities close to them. The extended set of annotated ontology entities, which include the entities close to those highlighted by the UGC, is hereafter called an aggregate. Labels for aggregates such as positive, negative and ambiguous emotions, as well as social interaction and psychological process were explicitly given to the designers, and thereafter their observations were based on them. The semantic map and the aggregates can be used together with the UGC to augment the simulation. For example, wider range of related words or concepts could now be considered in the dialogue design.

This observation complemented the identification of clusters with the notion of aggregates in the ontology space, from which the third requirement was elicited:

Focus Requirement 3. Extract aggregates of ontology entities given the

(a) "mental-state" branch of the WNAffect taxonomy of emotions

(b) "body language signal meaning" branch of the body language ontology

Figure 5.4 Semantics maps for the male participants.

In addition to clusters, the simulator designers mentioned aggregates of ontology entities formed by the clustered ontology entities in the (a) emotion and (b) body language signal meaning semantic maps.

Potential Benefit 4 for Simulator Designers. The designers noted that the

semantic maps could show how close ontology entities are related in the aggregates. This observation leads to the question of what distance should be used to form clusters and consequently aggregates. Longer distance will result in larger clusters and aggregates, while shorter distance in smaller clusters. With respect to the number of ontology entities in each cluster this affects how abstract of specific clusters can be identified. Following two requirements are drawn from this observation:

Focus Requirement 4. Construct clusters and aggregates based on the

distance of the annotated ontology entities.

Focus Requirement 5. Allow for clustering and aggregation using different

distances between ontology entities.

Potential Benefit 5 for Simulator Designers. Another example of semantic

maps derived from user groups is shown in Figure 5.5, where the visualisations shown to the designers included semantics maps from young participants (age 17-26 years old) in the study.

While the simulator designers identified clusters and aggregates mainly regarding negative emotions (dislike, anger, shame and fear), they also commented on the breadth of emotions and body language signals

meanings covered by the annotated ontology entities in the semantic maps. For example, the fact that all positive, negative and ambiguous emotions were partially covered by participants' comments was seen by the designers as an indication that a variety of emotions was triggered when interacting with the simulator. In addition to the breadth, the semantic map visualisation provided a tool for the designers to examine how abstract or specific are the triggered emotions and be able to adapt the simulator accordingly, e.g. by illustrating situations expressing more specific emotions when abstract ones have been triggered. From this observation, two requirements were elicited:

Focus Requirement 6. Represent the breadth of annotated ontology

entities.

Focus Requirement 7. Exploit the ontology hierarchy to be able to reason

about specificity and generality of ontology entities.

(a) "mental-state" branch of the WNAffect taxonomy of emotions

(b) "body language signal meaning" branch of the body language ontology

Figure 5.5 Semantics maps for the young participants.

With the semantic maps for (a) emotion and (b) body language signal meanings the simulator designers were also able to examine the breadth and depth of annotated ontology entities across and within the clusters respectively. The curved lines indicate areas from which ontology entities were extracted. The ellipses indicate the areas of interest for the simulator designers.

Potential Benefit 6 for Simulator Designers. By examining each cluster of

annotated ontology entities closer, the designers also identified interesting sub-clusters inside the same aggregate. For example (see Figure 5.6 snapshot from Figure 5.5,b) , the social interaction concept in the body language signal meaning branch of the body language ontology is a super-

class of communication (populated aggregate), contest, cooperation and pretending. In turn, communication is a super-class of expressing, linguistic communication (most populated) and remembering. Parsing the ontology hierarchy for a cluster (consequently aggregate) of annotated ontology entities, several sub-clusters can be extracted that compose the cluster in discussion with a decreasing cardinality of ontology entities. This property leads to the following requirement for viewpoints focus representation:

Focus Requirement 8 Represent the composition of the clusters and

consequently of the aggregates in the ontology space.

Figure 5.6 Composition of a cluster of annotated ontology entities with

smaller sub-clusters.