Enhancing Web Searches from Concept Map-based Knowledge Models

摘要

Although many publicly available search engines can retrieve relevant information reasonably well, the list of the retrieved web pages is still often too large or contains information that has no relevance to the query. Our goal is to improve the results of these search engines for queries generated by users while constructing and/or browsing concept map-based knowledge models. By exploiting the propositional and hierarchical nature of concept maps, we have developed two algorithms, SAgent and WAgent, for filtering and ranking the results obtained by the search engines. The algorithms were implemented via mobile agents and evaluated empirically. In our experiments, six subjects submitted queries based on a concept map to publicly available search engines (Google, Altavista, Yahoo, Excite), and were asked to rank the relevance of the results; the agents also filtered and ranked the engines' output. The results are promising, in that the Agents' ranking was ...