Results and Discussion

The domain expert was asked to judge the correctness of obtained sub-problem to solution mappings. Figure 5.8 shows the degree of mapping between one document’s sub-problems and its solution descriptions. The mappings from the expert are shown in bold. The algorithm was able to correctly identify solution descriptions that match with the sub-problems in all instances where the information it relies on was available. For example, fire alarm and stove shutoff isolator constitute the solution package for the interfere with appliance problem. As Figure 5.8 shows, fire alarm and stove shutoff isolator obtain considerably much higher mapping values in comparison with those obtained by the other devices in the same document. The same pattern is seen for the other mappings except for the situation involving the flood(ing) sub-problem. In the document, there is no section

5.3. Matching Sub-problem Text to SmartHouse Solutions 91 that specifically talks about a solution for the flooding problem as was done for the other sub-problems because no flood detection device was provided in response to the flooding sub-problem. The mapping algorithm relies on information that appears in the problem and solution parts of the document and particularly, on the description of how the device is meant to help with the sub-problem. Consequently, it was not possible for the algorithm to identify the solution description for this particular sub-problem because a dedicated description does not exist.

case1

interfere with appliance : fire alarm - 0.65 interfere with appliance : out of bedroom alert - 0.1 interfere with appliance : out of house alert - 0.0 interfere with appliance : page unit - 0.1

interfere with appliance : stove shutoff isolator - 0.53 leave the home unsupervised : fire alarm - 0.1

leave the home unsupervised : out of bedroom alert - 0.2 leave the home unsupervised : out of house alert - 0.65 leave the home unsupervised : page unit - 0.2

leave the home unsupervised : stove shutoff isolator - 0.1 restlessness at night : fire alarm - 0.3333333333333333 restlessness at night : out of bedroom alert - 0.17 restlessness at night : out of house alert - 0.17 restlessness at night : page unit - 0.67 restlessness at night : stove shutoff isolator - 0.50 flood : fire alarm - 0.13

flood : out of bedroom alert - 0.13 flood : out of house alert - 0.00 flood : page unit - 0.00

flood : stove shutoff isolator - 0.13

Figure 5.8: Mapping Problems to Solutions

The solution for the flooding sub-problem was mentioned in a different and general part of the report that is not part of the solutions’ descriptions. Figure 5.9 shows the para-graph in which the solution for the flooding sub-problem appears. As Figure 5.9 shows, the paragraph also contains SmartHouse device names (underlined) for other sub-problems’

solutions. It mentions door contacts, movement detector and fire alarms, which are so-lutions to the sub-problems regarding doors, movement and fire respectively. Since the device names have overlapping words with their corresponding sub-problem descriptions, the algorithm “sees” this paragraph as related to doors, movement, fire and flooding

sub-5.3. Matching Sub-problem Text to SmartHouse Solutions 92 problems. Indeed, as an experiment to further explore the capabilities of the algorithm, this paragraph was temporarily added to the solution descriptions. The sub-problems doors, movement, fire obtained (very low) similar scores and the flooding sub-problem obtained a slightly higher score which can be attributed to the fact that it had two over-lapping words (flood and water) with the paragraph as opposed to the other sub-problems that had one overlapping word.

All of the equipment, except for the door contacts, movement detector, fire alarms and paging device were located in a locked cupboard in the kitchen. The option for the provision of a flood detection and water shut-off was considered, but this was deemed to be unnecessary. Instead, the care staff opted for the fitting of taps that require the user to push the tops to operate, so they cannot accidentally (or intentionally) be left on.

Figure 5.9: Solution for the Flooding sub-problem

The approach was tested on 10 documents with 31 sub-problems; 9 documents had all their sub-problems successfully mapped onto corresponding solutions. Altogether, 30 out of 31 sub-problem descriptions were successfully matched. The algorithm was found to perform very well when the phrase and word overlaps it relies on for mapping were available. The mapping scores were also found to be much higher for correct solutions than for wrong solutions. Thus there was a clear distinction between correct and wrong solutions. In the instance where the algorithm was unable to map a sub-problem to a solution description i.e., for the flooding problem in Figure 5.8, the mapping scores were not distinctly different for all mappings illustrating that no solution was identified as correct for the sub-problem at hand.

The algorithm was implemented in a tool that enabled the expert to carry out the mapping in a user-friendly manner. The expert found the mapping task easy because not only were the solutions ranked, the correct ones predominantly appeared at the top of the ranking. For example, Handsfree telephone unit, video intercom and community alarm telephone base unit are the solutions to the intercom operation sub-problem shown in the screen snapshot in Figure 5.10. The task was also made easier by the fact that no instances of “missing” solution descriptions were found aside from that regarding the

5.4. Summary 93 flooding sub-problem that was discussed earlier.

Sub-problem ID Sub-problem Text

R anked

Sm artH ouse D evices

Figure 5.10: Sanctioning of Sub-problem to Solution Mappings

5.4 Summary

This chapter has shown how Formal Concept Analysis can be applied to contexts of sub-problems (objects) and their representative terms (attributes) to obtain conceptual models.

Mappings of sub-problems to their respective solution descriptions have also been carried out. The mapping algorithm relies on phrase overlaps between sub-problem and solution texts in order to identify solution descriptions that match with a given sub-problem. The mappings will enable case completion by attaching the solutions to appropriate authored problem-parts of cases. The following two chapters describe two case authoring tools that use FCA to learn conceptual models and use the models to create knowledge-rich cases.

Chapter 6

SmartCAT - A Case Authoring