Web browsing task is based on depth-first searching scheme, so that searching relevant information from Web may be very tedious. In this paper, we propose personal browsing assistant system based on user intentions modeling. Before explicitly requested by a user, this system can analyze the prefetched resources from the hyperlinked Webpages and compare them with the estimated user intention, so that it can help him to make a better decision like which Webpage should be requested next. More important problem is the semantic heterogeneity between Web spaces. It makes the understandability of locally annotated resources more difficult. We apply semantic annotation, which is a transcoding procedure with the global ontology. Therefore, each local metadata can be semantically enriched, and efficiently comparable. As testing bed of our experiment, we organized three different online clothes stores whose images are annotated by semantically heterogeneous metadata. We simulated virtual customers navigating these cyberspaces. According to the predefined preferences of customer models, they conducted comparison-shopping. We have shown the reasonability of supporting the Web browsing, and its performance was evaluated as measuring the total size of browsed hyperspace.

We exploit a structural knowledge representation scheme called System Entity Structure (SES) methodology to represent and manage wafer failure patterns which can make a significant influence to FABs in the semiconductor industry. It is important for the engineers to simulate various system verification processes by using predefined system entities (e.g., decomposition, taxonomy, and coupling relationships of a system) contained in the SES. For better computational performance, given a certain failure pattern, a Pruned SES (PES) can be extracted by selecting the only relevant system entities from the SES. Therefore, the SES-based simulation system allows the engineers to efficiently evaluate and monitor semiconductor data by i) analyzing failures to find out the corresponding causes and ii) managing historical data related to such failures.