Figure 5 - uploaded by Allan John Brimicombe
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The process of encoding linguistic hedges of uncertainty as ≈ E, propagating them and then 'translation' into fitness-for-use statements.
Source publication
Spatial data quality has been attracting much interest. Much of the problem lies in the degree to which current data structures are unable to model the real world and the way imperfections in the data may propagate during analyses and cast doubt on the validity of the outcomes. Much of the research has concentrated on the quantitative accuracy of s...
Contexts in source publication
Context 1
... of ≈ E are both the building blocks for fuzzy set representations of linguistic hedges and the means for 'translating' fuzzy sets into qualifying statements of fitness-for-use. For example, as illustrated in Figure 5, during a session of interpreting aerial photographs, an interpreter marks or codes the class attribute (label) of a polygon with a linguistic hedge that expresses the interpreter's uncertainty. When recording data in the GIS, the interpreter defines an equivalence using ≈ E intuitive probabilities. ...
Context 2
... must be stressed that the process illustrated in Figure 5 is not proposing a fuzzy logic alternative to standard GIS analyses using Boolean logic. ≈ E is a means of encoding, propagating and decoding linguistically expressed uncertainty associated with spatial objects. ...
Context 3
... problems of dealing with linguistic hedges as discussed in above, also apply to expressions of fitness-for-use. In the example given in Figure 5, linguistic hedges encoded as fuzzy sets through ≈ E have been propagated through a union overlay to give a resultant fuzzy set whose 'meaning' may not be known. To match this propagated uncertainty with the relevant expression of fitness-for-use (in terms of ≈ E), the resultant fuzzy set needs to be equated with one of the stylized fuzzy sets and thus with one of the user's equivalent quality statements. ...
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The aim of our work is to provide a more easier way to access documents in territorial copora and, particularly, spatial information contents. We suggest to go further than classical based-statistic information retrieval systems that are not suitable in the case of spatial information extraction. A light linguistic process can rather be used in order to draw the information's main thing. They can be a good starting point to be used thereafter in a more precise interpretation process, using only the geographic properties extracted, in order to propose a multi-level indexing method, each level corresponding to an abstraction level of spatial information.
Thus, we propose a multi-levels spatial information retrieval system, indexing unstructured textual documents. This method, that interprets spatial information, allows to improve the efficiency of information retrieval systems each time a spatial query is performed. This interpretation can also retrieve the context in which the spatial information is used by the author. Particularly, text units can be classified in itinerary, local description or area comparison contexts.
In the present paper, we plan to introduce a new procedure for learner’s assessment in the learning environment in a way true to reality by using fuzzy logic. In this evaluation, learner’s responses are accompanied by a degree of certainty expressed by him. This method allows detection of problems encountered by the learner and also to fix the concepts mastered and those that are not. It is a diagnostic procedure that improves the process of content adaptation and self-adjustment on the one hand and makes the knowledge model clearly interpretable and more understandable to learner and tutor/teacher/head teacher on the other hand.
