Additional Considerations for Usage

The concepts outlined so far have failed to include a measure of the appropriateness of the inclusion of the term with respect to the observer‟s viewing distance, and object sizes. Instead they have looked to determine the most suitable candidate for the relation. However at greater viewing distances, and for smaller items, it may be harder for the observer to judge when two entities share an opposite relation and it may be necessary to restrict the inclusion of the term in a referring expression according to the percentage of the field of view occupied by the items. This approach accommodates entity scale, such that a house may be described as opposite a bakery from only close range, while a park may be described as opposite the hills from greater distances.

Additionally when a referring expression is used in a more general description of a region, and not to identify a particular target, consideration must be given to the ordering of features in the relationship. Jackendorf (1992) makes the observation that not all relationships are symmetrical, and that “the house is next to the bike” makes less sense than “the bike is next to

130 the house”. When considering the “opposite” relation it makes more sense to use the most salient feature as the reference object, such as “turn right after you see a hut opposite a lake”, whereby the viewer‟s attention should be more naturally drawn to the lake, and the decision point confirmed once the hut has been located.

7.5

Conclusions and Future Work

When constructing descriptions of a feature it is useful to include spatial prepositions to guide the user‟s attention. This is particularly relevant when forming referring expressions for use in speech based LBSs when directing the user‟s attention to items in view while exploring a city. The case of spatially „opposite‟ an entity has been considered in this chapter raising a number of observations about how it may be determined and constructed from GIS datasets for use in LBS applications.

The research has shown that to ensure meaningful descriptions it is necessary to determine whether features are visible to the user by calculating their visual exposure and establish a common reference entity to define their relationship. Here one dimensional linear, and two dimensional regional features have been examined for this purpose. Three dimensional entities have not yet been explored, but could be included in future work. They may be of particular use when constructing descriptions for features on the side of a building, such as “the window opposite the balcony“, to limit the vertical scan region.

When a number of possible candidates are found it is necessary to select the most useful by determining its saliency and recognisability, to assist in guiding the user‟s attention and minimise risk of target confusion. Factors including its visibility, distance, and the number of other similar items in the scene are considered to ascertain the best candidate. Future work should examine the weighting of these inputs, to determine the most suitable values for particular tasks.

In a wider context such definitions are useful to provide a geosemantic bridge to a future semantic web and publish the “opposite” relation in a manner that it can be used automatically. Two approaches for achieving this include, firstly, within a description of the concept “opposite” in the ontology a callout can be inserted, which links to an external web service, or secondly the nature of that function could be defined in something akin to an ontology, which is here called an epistemology.

Given the first approach of calling out to an external service, a pointer in the ontology would direct the user, or software agent, to a web service that computed the opposite relation. The service would include a semantic wrapper (e.g. in OWL-S, WSDL, or WSML), defining what the service does and the type of inputs needed and outputs produced by the service, see for

131 example Bruin et al. (2008). The limitation of this approach is that the algorithm for the “opposite” relation is semantically a black box and cannot be inspected or reasoned with.

The second approach for publishing a semantically well defined “opposite” relation, involves describing the algorithm in full, that is, how it works rather than what it does. Gruber (1993) classically defines an ontology from an AI perspective as a specification of a conceptualisation. From a similar perspective, an epistemology might then be defined as a specification of functions that contribute to the concepts in the conceptualisation, that is, a specification of how we come to know what we know. This epistemology might be expressed in some kind of pseudo code or declarative language, such that any software might be able to run it, and is linked to an ontology that describes other aspects of the concepts used. The advantage of this approach over the former is that the functions are no longer black boxes, which might support new kinds of reasoning such as the comparison of epistemologies.

The models presented should be developed further through user trials, and may then be adopted as part of a wider set of defined spatial relations for use in urban LBS, and the geosemantic web.

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