WWW/Internet - Portal do Software Público Brasileiro

IADIS International Conference WWW/Internet 20104.2 EvaluationA property belongs not only to a certain product domain, but also to multiple ones. For example, "screensize" is for a digital camera, and also for a laptop PC. Therefore, it is difficult to explicitly classify theproperties to each class unlike the instances, and the adaptation filter has a possibility to greatly reduce therecall. So, the experiment uses the NEE without the adaptation filter. We selected the target product domains(classes) which relatively have many properties: camera, lens, TV, PC, LCD, Blu-ray player, and made thecorrect properties set. A flow of the experiment in case of the camera is as follows. We first register theproperties {"optical zoom", "digital zoom", "pixels"} as the seeds to the camera class in advance, and get aninstance of the camera using the instances recommendation. Then, we execute the NEE with the seeds {acamera instance, "optical zoom", "digital zoom", "pixels"}, and extract the candidates of the properties.The result is shown in Fig.8. We confirmed the recall achieved 70-80 %, and almost all the properties ofthe class were extracted. However, the precision remained 50-60 % on the whole. Although it's ideal toextract all the properties from pages such as the product specification of the manufacturers, this experimenthas not selected the page to be extracted. As a consequence, it extracted a number of the irrelevant termsfrom the pages like explanation of the property meanings, and they lowered the precision.recallprecisioncameralensTVPCLCDBlu-rayFigure 8. Result of property recommendation5. DISCUSSIONIn terms of C. Support of term registration in section 1, we presented that the instance and propertyrecommendation reduces the user's strain. The instance recommendation also introduced the adaptation filterdue to insufficient precision of the Named Entity Extraction. As the result, it raised the precision though itlowers the recall. However, the reason we rather emphasize the precision is that it would be more difficult forthe target users of ONTOMO who are not experts of the ontology to pick the correct terms from a large setwith many irrelevant terms (Of course, it depends on the domain knowledge). On the other hand, in case ofthe low recall the user needs only to repeat the process of the recommendation, which would be a relativelysimple task (if the precision keeps high). Furthermore, the property recommendation could not use theadaptation filter, so that to improve the precision we are now considering introduction of a process to selectthe pages to be extracted by looking at hit count of search, and so forth.However, the ontology which can use this recommendation function is limited to ones, where a class iscorresponding to the already-existing category like a product domain. This is a kind of limitation of the NEEfrom the web, which means that it can not collect the terms of a classification unless someone classified it inadvance. Moreover, the current procedure of the instance recommendation can not classify the classes whichare not determined by just three terms. For example, the instances {"Tokyo", "Shinagawa", "Shinjuku"} arefor Tokyo City class, and also for Train Station class, and then those are not determined automatically. But,as the property recommendation we can add a typical keyword like "City" or "Station" when searching thepages, and specify the classes to some extent. In the future, we will discuss this improvement with our patternextraction method.In terms of other approaches: A. Easy preparation for the introduction and B. Improvement of usability(design of user interface), as mentioned in section 1 we skipped the tool install by using the web browser, and109